%%% -*-BibTeX-*- %%% ==================================================================== %%% BibTeX-file{ %%% author = "Nelson H. F. Beebe", %%% version = "1.69", %%% date = "06 November 2025", %%% time = "07:45:23 MDT", %%% filename = "ijig.bib", %%% address = "University of Utah %%% Department of Mathematics, 110 LCB %%% 155 S 1400 E RM 233 %%% Salt Lake City, UT 84112-0090 %%% USA", %%% telephone = "+1 801 581 5254", %%% URL = "https://www.math.utah.edu/~beebe", %%% checksum = "05228 32947 163544 1611670", %%% email = "beebe at math.utah.edu, beebe at acm.org, %%% beebe at computer.org (Internet)", %%% codetable = "ISO/ASCII", %%% keywords = "BibTeX; bibliography; International Journal %%% of Image and Graphics (IJIG)", %%% license = "public domain", %%% supported = "yes", %%% docstring = "This is a COMPLETE bibliography of the %%% International Journal of Image and Graphics %%% (IJIG) (CODEN ????, ISSN 0219-4678), published by %%% World Scientific. %%% %%% Publication began with volume 1, number 1, in %%% January 2001. %%% %%% The journal has a World-Wide Web site at %%% %%% http://ejournals.wspc.com.sg/ijig/ijig.shtml %%% %%% At version 1.69, the year coverage looked %%% like this: %%% %%% 2001 ( 39) 2010 ( 33) 2019 ( 25) %%% 2002 ( 38) 2011 ( 33) 2020 ( 37) %%% 2003 ( 37) 2012 ( 30) 2021 ( 61) %%% 2004 ( 37) 2013 ( 32) 2022 ( 62) %%% 2005 ( 42) 2014 ( 22) 2023 ( 76) %%% 2006 ( 39) 2015 ( 27) 2024 ( 62) %%% 2007 ( 43) 2016 ( 23) 2025 ( 69) %%% 2008 ( 36) 2017 ( 25) 2026 ( 10) %%% 2009 ( 33) 2018 ( 25) %%% %%% Article: 996 %%% %%% Total entries: 996 %%% %%% Data for the bibliography has been collected %%% from the journal Web site. %%% %%% Numerous errors in the sources noted above %%% have been corrected. Spelling has been %%% verified with the UNIX spell and GNU ispell %%% programs using the exception dictionary %%% stored in the companion file with extension %%% .sok. %%% %%% BibTeX citation tags are uniformly chosen %%% as name:year:abbrev, where name is the %%% family name of the first author or editor, %%% year is a 4-digit number, and abbrev is a %%% 3-letter condensation of important title %%% words. Citation tags were automatically %%% generated by software developed for the %%% BibNet Project. %%% %%% In this bibliography, entries are sorted in %%% publication order, using ``bibsort -byvolume''. %%% %%% The checksum field above contains a CRC-16 %%% checksum as the first value, followed by the %%% equivalent of the standard UNIX wc (word %%% count) utility output of lines, words, and %%% characters. This is produced by Robert %%% Solovay's checksum utility.", %%% } %%% ==================================================================== @Preamble{ "\ifx \undefined \TM \def \TM {${}^{\sc TM}$} \fi" } %%% ==================================================================== %%% Acknowledgement abbreviations: @String{ack-nhfb = "Nelson H. F. Beebe, University of Utah, Department of Mathematics, 110 LCB, 155 S 1400 E RM 233, Salt Lake City, UT 84112-0090, USA, Tel: +1 801 581 5254, e-mail: \path|beebe@math.utah.edu|, \path|beebe@acm.org|, \path|beebe@computer.org| (Internet), URL: \path|https://www.math.utah.edu/~beebe/|"} %%% ==================================================================== %%% Journal abbreviations: @String{j-INT-J-IMAGE-GRAPHICS = "International Journal of Image and Graphics (IJIG)"} %%% ==================================================================== %%% Bibliography entries: @Article{Magnenat-Thalmann:2001:DSC, author = "N. Magnenat-Thalmann and P. Volino and L. Moccozet", title = "Designing and Simulating Clothes", journal = j-INT-J-IMAGE-GRAPHICS, volume = "1", number = "1", pages = "1--??", month = jan, year = "2001", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Feb 26 12:00:38 MST 2002", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Hong:2001:IST, author = "P. Hong and Z. Wen and T. S. Huang", title = "{iFACE}: a {$3$D} Synthetic Talking Face", journal = j-INT-J-IMAGE-GRAPHICS, volume = "1", number = "1", pages = "19--??", month = jan, year = "2001", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Feb 26 12:00:38 MST 2002", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Nakamae:2001:OPR, author = "E. Nakamae", title = "An Overview of Photo-Realism for Outdoor Scenes", journal = j-INT-J-IMAGE-GRAPHICS, volume = "1", number = "1", pages = "27--??", month = jan, year = "2001", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Feb 26 12:00:38 MST 2002", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Li:2001:CIB, author = "J. Li and H.-Y. Shum and Y.-Q. Zhang", title = "On the Compression of Image Based Rendering Scene: a Comparison among Block, Reference and Wavelet Coders", journal = j-INT-J-IMAGE-GRAPHICS, volume = "1", number = "1", pages = "45--??", month = jan, year = "2001", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Feb 26 12:00:38 MST 2002", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Hanjalic:2001:RAV, author = "A. Hanjalic and R. L. Lagendijk and J. Biemond", title = "Recent Advances in Video Content Analysis: From Visual Features to Semantic Video Segments", journal = j-INT-J-IMAGE-GRAPHICS, volume = "1", number = "1", pages = "63--??", month = jan, year = "2001", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Feb 26 12:00:38 MST 2002", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Feng:2001:CBR, author = "D. Feng", title = "Content-Based Retrieval of Multimedia Information", journal = j-INT-J-IMAGE-GRAPHICS, volume = "1", number = "1", pages = "83--??", month = jan, year = "2001", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Feb 26 12:00:38 MST 2002", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Wayman:2001:FBA, author = "J. L. Wayman", title = "Fundamentals of Biometric Authentication Technologies", journal = j-INT-J-IMAGE-GRAPHICS, volume = "1", number = "1", pages = "93--??", month = jan, year = "2001", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Feb 26 12:00:38 MST 2002", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Bouvier:2001:TTO, author = "E. Bouvier and E. Gobbetti", title = "{TOM}: Totally Ordered Mesh a Multiresolution Structure for Time Critical Graphics Applications", journal = j-INT-J-IMAGE-GRAPHICS, volume = "1", number = "1", pages = "115--??", month = jan, year = "2001", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Feb 26 12:00:38 MST 2002", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Shu:2001:APV, author = "W. Shu and G. Rong and Z. Bian and D. Zhang", title = "Automatic Palmprint Verification", journal = j-INT-J-IMAGE-GRAPHICS, volume = "1", number = "1", pages = "135--??", month = jan, year = "2001", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Feb 26 12:00:38 MST 2002", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Luo:2001:RCD, author = "Y. Luo and R. Galli and D. S{\'a}nchez and A. Bennasar and J. Forn{\'e}s and J. C. Serra and J. M. Hu{\'e}scar and J. Gay{\`a}", title = "A Remote Cooperative Design System Using Interactive {$3$D} Graphics", journal = j-INT-J-IMAGE-GRAPHICS, volume = "1", number = "1", pages = "153--??", month = jan, year = "2001", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Feb 26 12:00:38 MST 2002", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Pal:2001:FIP, author = "S. K. Pal", title = "Fuzzy Image Processing and Recognition: Uncertainty Handling and Applications", journal = j-INT-J-IMAGE-GRAPHICS, volume = "1", number = "2", pages = "169--??", month = apr, year = "2001", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Feb 26 12:00:39 MST 2002", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Yan:2001:HFI, author = "H. Yan", title = "Human Face Image Processing Techniques", journal = j-INT-J-IMAGE-GRAPHICS, volume = "1", number = "2", pages = "197--??", month = apr, year = "2001", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Feb 26 12:00:39 MST 2002", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Gavrilova:2001:CLI, author = "M. Gavrilova and J. Rokne", title = "Computing Line Intersections", journal = j-INT-J-IMAGE-GRAPHICS, volume = "1", number = "2", pages = "217--??", month = apr, year = "2001", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Feb 26 12:00:39 MST 2002", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Chiang:2001:SVC, author = "T. Chiang and Y.-Q. Zhang", title = "Stereoscopic Video Coding Using a Fast and Robust Affine Motion Search", journal = j-INT-J-IMAGE-GRAPHICS, volume = "1", number = "2", pages = "231--??", month = apr, year = "2001", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Feb 26 12:00:39 MST 2002", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Oh:2001:ESQ, author = "K.-M. Oh and J.-D. Choi and C.-S. Lee and C.-J. Park and E.-T. Lee", title = "An Efficient and Simple Quad Edge Conversion of Polygonal Mainfold Objects", journal = j-INT-J-IMAGE-GRAPHICS, volume = "1", number = "2", pages = "251--??", month = apr, year = "2001", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Feb 26 12:00:39 MST 2002", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Heng:2001:INV, author = "P.-A. Heng and H. Sun and K.-W. Chen and T.-T. Wong", title = "Interactive Navigation of Virtual Vessel Tracking with {$3$D} Intelligent Scissors", journal = j-INT-J-IMAGE-GRAPHICS, volume = "1", number = "2", pages = "273--??", month = apr, year = "2001", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Feb 26 12:00:39 MST 2002", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Cai:2001:MPP, author = "J. Cai and Z.-Q. Liu", title = "{Markov} Process in Pattern Recognition", journal = j-INT-J-IMAGE-GRAPHICS, volume = "1", number = "2", pages = "287--??", month = apr, year = "2001", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Feb 26 12:00:39 MST 2002", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Tadamura:2001:FRW, author = "K. Tadamura and X. Qin and G. Jiao and E. Nakamae", title = "Fast Rendering Water Surface for Outdoor Scenes", journal = j-INT-J-IMAGE-GRAPHICS, volume = "1", number = "2", pages = "313--??", month = apr, year = "2001", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Feb 26 12:00:39 MST 2002", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Pan:2001:LDM, author = "Z. Pan and M. Zhang and K. Zhou and C. Cheng and J. Shi", title = "Level of Detail and Multi-Resolution Modeling for Virtual Prototyping", journal = j-INT-J-IMAGE-GRAPHICS, volume = "1", number = "2", pages = "329--??", month = apr, year = "2001", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Feb 26 12:00:39 MST 2002", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Amin:2001:PSC, author = "A. Amin and R. Shiu", title = "Page Segmentation and Classification Utilizing Bottom-Up Approach", journal = j-INT-J-IMAGE-GRAPHICS, volume = "1", number = "2", pages = "345--??", month = apr, year = "2001", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Feb 26 12:00:39 MST 2002", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Rodriguez:2001:GAD, author = "W. Rodriguez and M. Last and A. Kandel and H. Bunke", title = "Geometric Approach to Data Mining", journal = j-INT-J-IMAGE-GRAPHICS, volume = "1", number = "2", pages = "363--??", month = apr, year = "2001", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Feb 26 12:00:39 MST 2002", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Worring:2001:IRC, author = "M. Worring and T. Gevers", title = "Interactive Retrieval of Color Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "1", number = "3", pages = "387--??", month = jul, year = "2001", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Feb 26 12:00:39 MST 2002", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Jaimes:2001:LSV, author = "A. Jaimes and S.-F. Chang", title = "Learning Structured Visual Detectors from User Input at Multiple Levels", journal = j-INT-J-IMAGE-GRAPHICS, volume = "1", number = "3", pages = "415--??", month = jul, year = "2001", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Feb 26 12:00:39 MST 2002", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Ngo:2001:RAC, author = "C.-W. Ngo and T.-C. Pong and H.-J. Zhang", title = "Recent Advances in Content-Based Video Analysis", journal = j-INT-J-IMAGE-GRAPHICS, volume = "1", number = "3", pages = "445--??", month = jul, year = "2001", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Feb 26 12:00:39 MST 2002", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Lienhart:2001:RTD, author = "R. Lienhart", title = "Reliable Transition Detection in Videos: a Survey and Practitioner's Guide", journal = j-INT-J-IMAGE-GRAPHICS, volume = "1", number = "3", pages = "469--??", month = jul, year = "2001", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Feb 26 12:00:39 MST 2002", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Dimitrova:2001:VCU, author = "N. Dimitrova and L. Agnihotri and G. Wei", title = "Video Classification Using Object Tracking", journal = j-INT-J-IMAGE-GRAPHICS, volume = "1", number = "3", pages = "487--??", month = jul, year = "2001", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Feb 26 12:00:39 MST 2002", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Lin:2001:VCR, author = "T. Lin and H. J. Zhang and Q.-Y. Shi", title = "Video Content Representation for Shot Retrieval and Scene Extraction", journal = j-INT-J-IMAGE-GRAPHICS, volume = "1", number = "3", pages = "507--??", month = jul, year = "2001", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Feb 26 12:00:39 MST 2002", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Pereira:2001:MSM, author = "F. Pereira and R. Koenen", title = "{MPEG-7}: a Standard for Multimedia Content Description", journal = j-INT-J-IMAGE-GRAPHICS, volume = "1", number = "3", pages = "527--??", month = jul, year = "2001", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Feb 26 12:00:39 MST 2002", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Wu:2001:MTD, author = "P. Wu and Y. Choi and Y. M. Ro and C. S. Won", title = "{MPEG-7} Texture Descriptors", journal = j-INT-J-IMAGE-GRAPHICS, volume = "1", number = "3", pages = "547--??", month = jul, year = "2001", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Feb 26 12:00:39 MST 2002", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Liu:2001:AIG, author = "W. Liu and T. Xin and Y. Xu and H. Shum and H. Zhong", title = "Artistic Image Generation by Deviation Mapping", journal = j-INT-J-IMAGE-GRAPHICS, volume = "1", number = "4", pages = "565--??", month = oct, year = "2001", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jan 27 07:06:41 MST 2004", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Li:2001:DCS, author = "G. Li and X. Li and H. Li", title = "Discrete Clothoid Spline Surfaces on Open Meshes", journal = j-INT-J-IMAGE-GRAPHICS, volume = "1", number = "4", pages = "575--??", month = oct, year = "2001", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jan 27 07:06:41 MST 2004", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Prasad:2001:TTC, author = "M. V. N. K. Prasad and K. K. Shukla", title = "Tree Triangular Coding Image Compression Algorithms", journal = j-INT-J-IMAGE-GRAPHICS, volume = "1", number = "4", pages = "591--??", month = oct, year = "2001", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jan 27 07:06:41 MST 2004", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Parker:2001:RSD, author = "J. R. Parker and J. Pivovarov", title = "Recognizing Symbols by Drawing Them", journal = j-INT-J-IMAGE-GRAPHICS, volume = "1", number = "4", pages = "605--??", month = oct, year = "2001", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jan 27 07:06:41 MST 2004", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kumar:2001:EQD, author = "B. P. Kumar and P. Gupta and C. J. Hwang", title = "An Efficient Quadtree Datastructure for Neighbor Finding Algorithm", journal = j-INT-J-IMAGE-GRAPHICS, volume = "1", number = "4", pages = "619--??", month = oct, year = "2001", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jan 27 07:06:41 MST 2004", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Gavrilova:2001:TAC, author = "M. L. Gavrilova and M. H. Alsuwaiyel", title = "Two Algorithms for Computing the {Euclidean} Distance Transform", journal = j-INT-J-IMAGE-GRAPHICS, volume = "1", number = "4", pages = "635--??", month = oct, year = "2001", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jan 27 07:06:41 MST 2004", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Stetten:2001:AFC, author = "G. D. Stetten and R. Drezek", title = "Active {Fourier} Contour Applied to Real Time {$3$D} Ultrasound of the Heart", journal = j-INT-J-IMAGE-GRAPHICS, volume = "1", number = "4", pages = "647--??", month = oct, year = "2001", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jan 27 07:06:41 MST 2004", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Tamburo:2001:GOP, author = "R. J. Tamburo and G. D. Stetten", title = "Gradient-Oriented Profiles for Boundary Parameterization and Their Application to Core Atoms Towards Shape Analysis", journal = j-INT-J-IMAGE-GRAPHICS, volume = "1", number = "4", pages = "659--??", month = oct, year = "2001", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jan 27 07:06:41 MST 2004", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Suri:2001:MSG, author = "J. Suri and D. Wu and L. Reden and J. Gao and S. Singh and S. Laxminarayan", title = "Modeling Segmentation Via Geometric Deformable Regularizers, {PDE} and Level Sets in Still and Motion Imagery: a Revisit", journal = j-INT-J-IMAGE-GRAPHICS, volume = "1", number = "4", pages = "681--??", month = oct, year = "2001", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jan 27 07:06:41 MST 2004", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Anonymous:2001:AI, author = "Anonymous", title = "Author Index", journal = j-INT-J-IMAGE-GRAPHICS, volume = "1", number = "4", pages = "735--??", month = oct, year = "2001", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jan 27 07:06:41 MST 2004", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Lee:2002:AIV, author = "J. Lee", title = "{ABSolute}: An Information Visualization System for Decision Support in Sourcing", journal = j-INT-J-IMAGE-GRAPHICS, volume = "2", number = "1", pages = "1--??", month = jan, year = "2002", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jan 27 07:06:41 MST 2004", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kitts:2002:LSM, author = "B. Kitts and K. Hetherington-Young and M. Vrieze", title = "Large-Scale Mining, Discovery and Visualization of {WWW} User Clickpaths", journal = j-INT-J-IMAGE-GRAPHICS, volume = "2", number = "1", pages = "21--??", month = jan, year = "2002", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jan 27 07:06:41 MST 2004", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Unwin:2002:DMG, author = "A. R. Unwin and H. Hofmann and A. F. X. Wilhelm", title = "Direct Manipulation Graphics for Data Mining", journal = j-INT-J-IMAGE-GRAPHICS, volume = "2", number = "1", pages = "49--??", month = jan, year = "2002", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jan 27 07:06:41 MST 2004", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Fischer:2002:AID, author = "S. Fischer and H. Bunke", title = "Automatic Identification of Diatoms Using Visual Human-Interpretable Features", journal = j-INT-J-IMAGE-GRAPHICS, volume = "2", number = "1", pages = "67--??", month = jan, year = "2002", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jan 27 07:06:41 MST 2004", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Pratt:2002:SPC, author = "K. B. Pratt and E. Fink", title = "Search for Patterns in Compressed Time Series", journal = j-INT-J-IMAGE-GRAPHICS, volume = "2", number = "1", pages = "89--??", month = jan, year = "2002", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jan 27 07:06:41 MST 2004", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Last:2002:PBA, author = "M. Last and A. Kandel", title = "Perception-Based Analysis of Engineering Experiments in the Semiconductor Industry", journal = j-INT-J-IMAGE-GRAPHICS, volume = "2", number = "1", pages = "107--??", month = jan, year = "2002", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jan 27 07:06:41 MST 2004", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Poulet:2002:FVV, author = "F. Poulet", title = "Full-View: a Visual Data-Mining Environment", journal = j-INT-J-IMAGE-GRAPHICS, volume = "2", number = "1", pages = "127--??", month = jan, year = "2002", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jan 27 07:06:41 MST 2004", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Solka:2002:VFA, author = "J. L. Solka and C. E. Priebe and B. T. Clark", title = "A Visualization Framework for the Analysis of Hyperdimensional Data", journal = j-INT-J-IMAGE-GRAPHICS, volume = "2", number = "1", pages = "145--??", month = jan, year = "2002", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jan 27 07:06:41 MST 2004", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Ren:2002:MDT, author = "Y. Ren and C. S. Chua and Y. K. Ho", title = "Motion Detection from Time-Varied Background", journal = j-INT-J-IMAGE-GRAPHICS, volume = "2", number = "2", pages = "163--??", month = apr, year = "2002", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jan 27 07:06:41 MST 2004", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kanatani:2002:MSS, author = "K. Kanatani", title = "Motion Segmentation by Subspace Separation: Model Selection and Reliability Evaluation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "2", number = "2", pages = "179--??", month = apr, year = "2002", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jan 27 07:06:41 MST 2004", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Wang:2002:SVW, author = "L. Wang and K. L. Chan and X.-J. Xiong", title = "A Sub-Vector Weighting Scheme for Image Retrieval with Relevance Feedback", journal = j-INT-J-IMAGE-GRAPHICS, volume = "2", number = "2", pages = "199--??", month = apr, year = "2002", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jan 27 07:06:41 MST 2004", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Adams:2002:FBA, author = "B. Adams and C. Dorai and S. Venkatesh", title = "Finding the Beat: An Analysis of the Rhythmic Elements of Motion Pictures", journal = j-INT-J-IMAGE-GRAPHICS, volume = "2", number = "2", pages = "215--??", month = apr, year = "2002", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jan 27 07:06:41 MST 2004", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Luo:2002:EG, author = "B. Luo and E. Hancock and R. Wilson", title = "Eigenspaces for Graphs", journal = j-INT-J-IMAGE-GRAPHICS, volume = "2", number = "2", pages = "247--??", month = apr, year = "2002", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jan 27 07:06:41 MST 2004", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Hild:2002:RSS, author = "M. Hild and K. Nishijima", title = "Reconstruction of {$3$D} Space Structure with a Rotational Imaging System", journal = j-INT-J-IMAGE-GRAPHICS, volume = "2", number = "2", pages = "269--??", month = apr, year = "2002", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jan 27 07:06:41 MST 2004", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Liu:2002:PAB, author = "Y. Liu and C.-K. Wu and H.-T. Tsui", title = "A Practical Approach for {$3$D} Building Modeling from Uncalibrated Video Sequences", journal = j-INT-J-IMAGE-GRAPHICS, volume = "2", number = "2", pages = "287--??", month = apr, year = "2002", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jan 27 07:06:41 MST 2004", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Tamaki:2002:CDI, author = "T. Tamaki and T. Yamamura and N. Ohnishi", title = "Correcting Distortion of Image by Image Registration with the Implicit Function Theorem", journal = j-INT-J-IMAGE-GRAPHICS, volume = "2", number = "2", pages = "309--??", month = apr, year = "2002", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jan 27 07:06:41 MST 2004", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Iwata:2002:DOH, author = "A. Iwata and K. Kato and K. Yamamoto", title = "The Detection of Obstacles by the Horizon View Camera", journal = j-INT-J-IMAGE-GRAPHICS, volume = "2", number = "2", pages = "331--??", month = apr, year = "2002", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jan 27 07:06:41 MST 2004", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Tissainayagam:2002:PMA, author = "P. Tissainayagam and D. Suter", title = "Performance Measures for Assessing Contour Trackers", journal = j-INT-J-IMAGE-GRAPHICS, volume = "2", number = "2", pages = "343--??", month = apr, year = "2002", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jan 27 07:06:41 MST 2004", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Wang:2002:ROM, author = "Yaming Wang and George Baciu", title = "Robust object matching using a modified version of the {Hausdorff} measure", journal = j-INT-J-IMAGE-GRAPHICS, volume = "2", number = "3", pages = "361--??", month = jul, year = "2002", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 06 07:36:43 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{ElBadawy:2002:SBI, author = "Ossama {El Badawy} and Mohamed Kamel", title = "Shape-based image retrieval applied to trademark images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "2", number = "3", pages = "375--??", month = jul, year = "2002", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 06 07:36:52 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Wang:2002:OOB, author = "Taehyung Wang and Phillip C. Y. Sheu", title = "An object-oriented {BSP} tree algorithm for hidden surface removal", journal = j-INT-J-IMAGE-GRAPHICS, volume = "2", number = "3", pages = "395--??", month = jul, year = "2002", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 06 07:37:19 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kate:2002:TAA, author = "Rohit Jaivant Kate and Prem Kalra and Subhashis Banerjee", title = "Towards an automatic approach for view-dependent geometry", journal = j-INT-J-IMAGE-GRAPHICS, volume = "2", number = "3", pages = "413--??", month = jul, year = "2002", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 06 07:37:26 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Bao:2002:IRB, author = "Paul Bao and Sung-Wai Hong", title = "Image restoration based on generalized finite automata encoded edge preserving regularization", journal = j-INT-J-IMAGE-GRAPHICS, volume = "2", number = "3", pages = "425--??", month = jul, year = "2002", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 06 07:37:33 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Zhang:2002:IER, author = "Y. J. Zhang", title = "Image engineering and related publications", journal = j-INT-J-IMAGE-GRAPHICS, volume = "2", number = "3", pages = "441--??", month = jul, year = "2002", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jan 27 07:06:41 MST 2004", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Ablameyko:2002:CSI, author = "S. Ablameyko and V. Bereishik and M. Homenko and D. Lagunovsky and N. Paramonova and O. Patsko", title = "A complete system for interpretation of color maps", journal = j-INT-J-IMAGE-GRAPHICS, volume = "2", number = "3", pages = "453--??", month = jul, year = "2002", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 06 07:37:39 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{You:2002:SSB, author = "Jane You and David Zhang", title = "Smart sensor: an on-board image processing system for real-time remote sensing", journal = j-INT-J-IMAGE-GRAPHICS, volume = "2", number = "3", pages = "481--??", month = jul, year = "2002", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 06 07:37:44 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Liu:2002:E, author = "Zhi-Qiang Liu", title = "Editorial", journal = j-INT-J-IMAGE-GRAPHICS, volume = "2", number = "4", pages = "501--??", month = oct, year = "2002", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jan 27 07:06:41 MST 2004", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Pu:2002:NNB, author = "Her-Chang Pu and Chin-Teng Lin", title = "A neural-network-based image resolution enhancement scheme for image resizing", journal = j-INT-J-IMAGE-GRAPHICS, volume = "2", number = "4", pages = "503--??", month = oct, year = "2002", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 06 07:37:57 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Liu:2002:ASS, author = "Zhi-Qiang Liu", title = "Adaptive subspace self-organizing map and its applications in face recognition", journal = j-INT-J-IMAGE-GRAPHICS, volume = "2", number = "4", pages = "519--??", month = oct, year = "2002", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jan 27 07:06:41 MST 2004", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Shang:2002:RFS, author = "Changjing Shang and Qiang Shen", title = "Rough feature selection for neural network based image classification", journal = j-INT-J-IMAGE-GRAPHICS, volume = "2", number = "4", pages = "541--??", month = oct, year = "2002", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 06 07:38:05 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Schenker:2002:FCG, author = "Adam Schenker and Mark Last and Horst Bunke and Abraham Kandel", title = "Fuzzy clustering with genetically adaptive scaling", journal = j-INT-J-IMAGE-GRAPHICS, volume = "2", number = "4", pages = "557--??", month = oct, year = "2002", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 06 07:38:10 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Miyamoto:2002:FMM, author = "Sadaaki Miyamoto and Arnold C. Alanzado", title = "Fuzzy $c$-means and mixture distribution models in the presence of noise clusters", journal = j-INT-J-IMAGE-GRAPHICS, volume = "2", number = "4", pages = "573--??", month = oct, year = "2002", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 06 07:38:11 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Wachs:2002:CFS, author = "Juan Wachs and Helman Stern and Mark Last", title = "Color face segmentation using a fuzzy min-max neural network", journal = j-INT-J-IMAGE-GRAPHICS, volume = "2", number = "4", pages = "587--??", month = oct, year = "2002", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 06 07:38:11 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Soodamani:2002:FHT, author = "R. Soodamani and Z. Q. Liu", title = "A fuzzy {Hough} transform approach to shape description", journal = j-INT-J-IMAGE-GRAPHICS, volume = "2", number = "4", pages = "603--??", month = oct, year = "2002", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 06 07:38:11 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Loia:2002:FRC, author = "Vincenzo Loia and Witold Pedrycz and Salvatore Sessa", title = "Fuzzy relation calculus in the compression and decompression of fuzzy relations", journal = j-INT-J-IMAGE-GRAPHICS, volume = "2", number = "4", pages = "617--??", month = oct, year = "2002", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 06 07:38:12 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Ke:2002:MST, author = "Shih-Hao Ke and Tsu-Tian Lee", title = "A multi-scale two-step fast search algorithm for block motion estimation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "2", number = "4", pages = "633--??", month = oct, year = "2002", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 06 07:38:12 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kameyama:2002:CRM, author = "Keisuke Kameyama and Kazuo Toraichi and Yukio Kosugi", title = "Constructive relaxation matching involving dynamical model switching and its application to shape matching", journal = j-INT-J-IMAGE-GRAPHICS, volume = "2", number = "4", pages = "655--??", month = oct, year = "2002", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 06 07:38:12 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Anonymous:2002:AIV, author = "Anonymous", title = "Author index volume 2 (2002)", journal = j-INT-J-IMAGE-GRAPHICS, volume = "2", number = "4", pages = "669--??", month = oct, year = "2002", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jan 27 07:06:41 MST 2004", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Anonymous:2003:Ea, author = "Anonymous", title = "Editorial", journal = j-INT-J-IMAGE-GRAPHICS, volume = "3", number = "1", pages = "1--??", month = jan, year = "2003", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jan 27 07:06:41 MST 2004", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Lang:2003:FSS, author = "Christian A. Lang and Ambuj K. Singh", title = "Faster Similarity Search for Multimedia Data Via Query Transformations", journal = j-INT-J-IMAGE-GRAPHICS, volume = "3", number = "1", pages = "3--??", month = jan, year = "2003", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 06 07:38:13 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Park:2003:SBS, author = "Sanghyun Park and Wesley W. Chu", title = "Similarity-Based Subsequence Search in Image Sequence Databases", journal = j-INT-J-IMAGE-GRAPHICS, volume = "3", number = "1", pages = "31--??", month = jan, year = "2003", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 06 07:38:13 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Oria:2003:VPV, author = "Vincent Oria and M. Tamer {\"O}zsu", title = "Views or Points of View on Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "3", number = "1", pages = "55--??", month = jan, year = "2003", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 06 07:38:13 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Chen:2003:FAF, author = "Longbin Chen and Baogang Hu and Lei Zhang and Mingjing Li and Hongjiang Zhang", title = "Face Annotation for Family Photo Album Management", journal = j-INT-J-IMAGE-GRAPHICS, volume = "3", number = "1", pages = "81--??", month = jan, year = "2003", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 06 07:38:14 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Prabhakar:2003:MLJ, author = "Sunil Prabhakar and Rahul Chari", title = "Minimizing Latency and Jitter for Large-Scale Multimedia Repositories Through Prefix Caching", journal = j-INT-J-IMAGE-GRAPHICS, volume = "3", number = "1", pages = "95--??", month = jan, year = "2003", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 06 07:38:14 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Wang:2003:CBI, author = "Zhiyong Wang and Zheru Chi and Dagan Feng and Ah Chung Tsoi", title = "Content-Based Image Retrieval with Relevance Feedback Using Adaptive Processing of Tree-Structure Image Representation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "3", number = "1", pages = "119--??", month = jan, year = "2003", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 06 07:38:14 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Cucchiara:2003:SVT, author = "Rita Cucchiara and Costantino Grana and Andrea Prati", title = "Semantic Video Transcoding Using Classes of Relevance", journal = j-INT-J-IMAGE-GRAPHICS, volume = "3", number = "1", pages = "145--??", month = jan, year = "2003", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 06 07:38:16 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Doulamis:2003:ECB, author = "Anastasios Doulamis and Nikolaos Doulamis and Theodora Varvarigou", title = "Efficient Content-Based Image Retrieval Using Fuzzy Organization and Optimal Relevance Feedback", journal = j-INT-J-IMAGE-GRAPHICS, volume = "3", number = "1", pages = "171--??", month = jan, year = "2003", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 06 07:38:16 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Tangelder:2003:PMR, author = "Johan W. H. Tangelder and Remco C. Veltkamp", title = "Polyhedral Model Retrieval Using Weighted Point Sets", journal = j-INT-J-IMAGE-GRAPHICS, volume = "3", number = "1", pages = "209--??", month = jan, year = "2003", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 06 07:38:16 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Anonymous:2003:Eb, author = "Anonymous", title = "Editorial", journal = j-INT-J-IMAGE-GRAPHICS, volume = "3", number = "2", pages = "231--??", month = apr, year = "2003", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jan 27 07:06:41 MST 2004", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Yilmaz:2003:IBI, author = "Ula{\c{s}} Yilmaz and Adem Yasar M{\"u}layim and Volkan Atalay", title = "An Image-Based Inexpensive {$3$D} Scanner", journal = j-INT-J-IMAGE-GRAPHICS, volume = "3", number = "2", pages = "235--??", month = apr, year = "2003", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jan 27 07:06:41 MST 2004", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{El-Sana:2003:VDR, author = "Jihad El-Sana and Neta Sokolovsky", title = "View-Dependent Rendering for Large Polygonal Models over Networks", journal = j-INT-J-IMAGE-GRAPHICS, volume = "3", number = "2", pages = "265--??", month = apr, year = "2003", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jan 27 07:06:41 MST 2004", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Cai:2003:SPA, author = "Kangying Cai and Wencheng Wang and Guangzheng Fei and Enhua Wu", title = "A Single-Pass Approach to Adaptive Simplification of Out-of-Core Models", journal = j-INT-J-IMAGE-GRAPHICS, volume = "3", number = "2", pages = "291--??", month = apr, year = "2003", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 06 07:38:16 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Xu:2003:FSB, author = "Weiwei Xu and Zhigeng Pan and Mingmin Zhang", title = "Footprint Sampling-Based Motion Editing", journal = j-INT-J-IMAGE-GRAPHICS, volume = "3", number = "2", pages = "311--??", month = apr, year = "2003", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 06 07:38:16 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Liu:2003:NRB, author = "Wenyu Liu and Hua Li and Guangxi Zhu", title = "Non-Rigid Body Interpolation Based on Generalized Morphologic Morphing", journal = j-INT-J-IMAGE-GRAPHICS, volume = "3", number = "2", pages = "325--??", month = apr, year = "2003", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 06 07:38:16 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Arya:2003:PFA, author = "Ali Arya and Babak Hamidzadeh", title = "Personalized Face Animation in Showface System", journal = j-INT-J-IMAGE-GRAPHICS, volume = "3", number = "2", pages = "345--??", month = apr, year = "2003", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 06 07:38:16 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Geng:2003:PUI, author = "Weidong Geng and Wolfgang Strauss and Monika Fleischmann and Vladimir Elistratov and Marina Kolesnik", title = "Perceptual User Interface in Virtual Shopping Environment", journal = j-INT-J-IMAGE-GRAPHICS, volume = "3", number = "2", pages = "365--??", month = apr, year = "2003", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 06 07:38:16 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Wan:2003:OTA, author = "Huagen Wan and Shuming Gao and Qunsheng Peng and Yiyu Cai", title = "Optimization Techniques for Assembly Planning of Complex Models in Large-Scale Virtual Environments", journal = j-INT-J-IMAGE-GRAPHICS, volume = "3", number = "2", pages = "379--??", month = apr, year = "2003", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 06 07:38:16 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Anonymous:2003:Ec, author = "Anonymous", title = "Editorial", journal = j-INT-J-IMAGE-GRAPHICS, volume = "3", number = "3", pages = "399--??", month = jul, year = "2003", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jan 27 07:06:41 MST 2004", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Ailisto:2003:RFI, author = "Heikki Ailisto and Mikko Lindholm and Pauli Tikkanen", title = "A Review of Fingerprint Image Enhancement Methods", journal = j-INT-J-IMAGE-GRAPHICS, volume = "3", number = "3", pages = "401--??", month = jul, year = "2003", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 06 07:38:16 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Tico:2003:RAS, author = "Marius Tico and Pauli Kuosmanen", title = "A Remote Authentication System Using Fingerprints", journal = j-INT-J-IMAGE-GRAPHICS, volume = "3", number = "3", pages = "425--??", month = jul, year = "2003", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 06 07:38:16 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Konvalinka:2003:VSF, author = "Ira Konvalinka", title = "Verification Speed in Fingerprint-based Biometric Systems", journal = j-INT-J-IMAGE-GRAPHICS, volume = "3", number = "3", pages = "447--??", month = jul, year = "2003", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jan 27 07:06:41 MST 2004", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Miao:2003:SHF, author = "Jun Miao and Hong Liu and Wen Gao and Hongming Zhang and Gang Deng and Xilin Chen", title = "A System for Human Face and Facial Feature Location", journal = j-INT-J-IMAGE-GRAPHICS, volume = "3", number = "3", pages = "461--??", month = jul, year = "2003", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 06 07:38:16 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Liu:2003:MBA, author = "Zhi-Qiang Liu and Jessica Y. Guo", title = "A Model-based Approach to Hair Region Segmentation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "3", number = "3", pages = "481--??", month = jul, year = "2003", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 06 07:38:16 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Sun:2003:DSC, author = "Jun Sun and Wenyuan Wang and Qing Zhuo and Chengyuan Ma", title = "Discriminatory Sparse Coding and Its Application to Face Recognition", journal = j-INT-J-IMAGE-GRAPHICS, volume = "3", number = "3", pages = "503--??", month = jul, year = "2003", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 06 07:38:16 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Schuckers:2003:UBB, author = "Michael E. Schuckers", title = "Using the Beta-Binomial Distribution to Assess Performance of a Biometric Identification Device", journal = j-INT-J-IMAGE-GRAPHICS, volume = "3", number = "3", pages = "523--??", month = jul, year = "2003", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jan 27 07:06:41 MST 2004", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Anonymous:2003:Ed, author = "Anonymous", title = "Editorial", journal = j-INT-J-IMAGE-GRAPHICS, volume = "3", number = "4", pages = "531--??", month = oct, year = "2003", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jan 27 07:06:41 MST 2004", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Takizawa:2003:RML, author = "Hotaka Takizawa and Kanae Shigemoto and Shinji Yamamoto and Tohru Matsumoto and Yukio Tateno and Takeshi Iinuma and Mitsuomi Matsumoto", title = "A Recognition Method of Lung Nodule Shadows in {X}-Ray {CT} Images Using {$3$D} Object Models", journal = j-INT-J-IMAGE-GRAPHICS, volume = "3", number = "4", pages = "533--??", month = oct, year = "2003", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 06 07:38:16 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Pratikakis:2003:RMD, author = "Ioannis Pratikakis and Christian Barillot and Pierre Hellier and Etienne Memin", title = "Robust Multiscale Deformable Registration of {$3$D} Ultrasound Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "3", number = "4", pages = "547--??", month = oct, year = "2003", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 06 07:38:16 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Haraguchi:2003:TDR, author = "Ryo Haraguchi and Naozo Sugimoto and Shigeru Eiho and Yoshio Ishida", title = "Three Dimensional Reconstruction of Coronary Arteries by Using Registration and Texture-Mapping onto Epicardial Surface on Nuclear {$3$D} Image", journal = j-INT-J-IMAGE-GRAPHICS, volume = "3", number = "4", pages = "567--??", month = oct, year = "2003", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 06 07:38:16 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Wang:2003:EDE, author = "Jiahui Wang and Hideo Saito and Shinji Ozawa and Tomohiro Kuwahara and Toyonobu Yamashita and Motoji Takahashi", title = "Extraction of Dermo-Epidermal Surface from {$3$D} Volumetric Images of Human Skin", journal = j-INT-J-IMAGE-GRAPHICS, volume = "3", number = "4", pages = "589--??", month = oct, year = "2003", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 06 07:38:16 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Owada:2003:ECC, author = "Shigeru Owada and Yoshihisa Shinagawa and Frank Nielsen", title = "Enumeration of Contour Correspondence", journal = j-INT-J-IMAGE-GRAPHICS, volume = "3", number = "4", pages = "609--??", month = oct, year = "2003", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 06 07:38:16 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Oshiro:2003:KGO, author = "Osamu Oshiro and Kumi Kamada and Masataka Imura and Kunihiro Chihara and Eiji Toyota and Yasuo Ogasawara and Fumihiko Kajiya", title = "Kidney Glomerulus Observation in Interactive {VR} Space", journal = j-INT-J-IMAGE-GRAPHICS, volume = "3", number = "4", pages = "629--??", month = oct, year = "2003", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 06 07:38:16 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Gunaratne:2003:EAF, author = "Pujitha Gunaratne and Yukio Sato", title = "Estimation of Asymmetry in Facial Actions for the Analysis of Motion Dysfunction Due to Paralysis", journal = j-INT-J-IMAGE-GRAPHICS, volume = "3", number = "4", pages = "639--??", month = oct, year = "2003", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 06 07:38:16 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Hussain:2003:FSF, author = "Muhammad Hussain and Yoshihiro Okada and Koichi Niijima", title = "Fast, Simple, Feature Preserving and Memory Efficient Simplification of Triangle Meshes", journal = j-INT-J-IMAGE-GRAPHICS, volume = "3", number = "4", pages = "653--??", month = oct, year = "2003", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 06 07:38:16 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Anonymous:2003:AIV, author = "Anonymous", title = "Author Index Volume 3 (2003)", journal = j-INT-J-IMAGE-GRAPHICS, volume = "3", number = "4", pages = "671--??", month = oct, year = "2003", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jan 27 07:06:41 MST 2004", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Bennamoun:2004:E, author = "Mohammed Bennamoun", title = "Editorial", journal = j-INT-J-IMAGE-GRAPHICS, volume = "4", number = "1", pages = "1--??", month = jan, year = "2004", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jan 27 07:06:41 MST 2004", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Hu:2004:SCR, author = "Zhencheng Hu and Keiichi Uchimura", title = "Solution of camera registration problem via {$3$D}--{$2$D} parameterized model matching for on-road navigation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "4", number = "1", pages = "3--??", month = jan, year = "2004", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 06 07:38:16 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kanatani:2004:ATC, author = "Kenichi Kanatani and Yasushi Kanazawa", title = "Automatic thresholding for correspondence detection", journal = j-INT-J-IMAGE-GRAPHICS, volume = "4", number = "1", pages = "21--??", month = jan, year = "2004", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 06 07:38:16 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kanatani:2004:ADC, author = "Kenichi Kanatani and Naoya Ohta", title = "Automatic detection of circular objects by ellipse growing", journal = j-INT-J-IMAGE-GRAPHICS, volume = "4", number = "1", pages = "35--??", month = jan, year = "2004", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 06 07:38:16 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Mamic:2004:ASS, author = "G. Mamic and M. Bennamoun", title = "Automated spline surface modeling and matching for recognition of free-form objects", journal = j-INT-J-IMAGE-GRAPHICS, volume = "4", number = "1", pages = "51--??", month = jan, year = "2004", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 06 07:38:16 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Bhanu:2004:MLA, author = "Bir Bhanu and Grinnell {Jones III}", title = "Multiple look angle {SAR} recognition", journal = j-INT-J-IMAGE-GRAPHICS, volume = "4", number = "1", pages = "85--??", month = jan, year = "2004", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 06 07:38:16 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Toulminet:2004:FAS, author = "Gwena{\"e}lle Toulminet and St{\'e}phane Mousset and Abdelaziz Bensrhair", title = "Fast and accurate stereo vision-based estimation of {$3$D} position and axial motion of road obstacles", journal = j-INT-J-IMAGE-GRAPHICS, volume = "4", number = "1", pages = "99--??", month = jan, year = "2004", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 06 07:38:16 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Roy:2004:MCU, author = "Micha{\"e}l Roy and Sebti Foufou and Fr{\'e}d{\'e}ric Truchetet", title = "Mesh comparison using attribute deviation metric", journal = j-INT-J-IMAGE-GRAPHICS, volume = "4", number = "1", pages = "127--??", month = jan, year = "2004", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 06 07:38:16 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Meegama:2004:FAP, author = "Ravinda G. N. Meegama and Jagath C. Rajapakse", title = "Fully Automated Peeling Technique for {T1}-Weighted, High-Quality {MR} Head Scans", journal = j-INT-J-IMAGE-GRAPHICS, volume = "4", number = "2", pages = "141--??", month = apr, year = "2004", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 06 07:38:17 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Lukac:2004:PBO, author = "Rastislav Lukac", title = "Performance Boundaries of Optimal Weighted Median Filters", journal = j-INT-J-IMAGE-GRAPHICS, volume = "4", number = "2", pages = "157--??", month = apr, year = "2004", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 6 06:44:13 MST 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Gatos:2004:FIM, author = "Basilios Gatos and Stavros J. Perantonis and Nikos Papamarkos and Ioannis Andreadis", title = "Fast Implementation of Morphological Operations Using Binary Image Block Decomposition", journal = j-INT-J-IMAGE-GRAPHICS, volume = "4", number = "2", pages = "183--??", month = apr, year = "2004", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 6 06:44:13 MST 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Palacios:2004:HBC, author = "Rafael Palacios and Amar Gupta and Patrick S. Wang", title = "Handwritten Bank Check Recognition of Courtesy Amounts", journal = j-INT-J-IMAGE-GRAPHICS, volume = "4", number = "2", pages = "203--??", month = apr, year = "2004", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 6 06:44:13 MST 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Sarkar:2004:GAB, author = "Biswajit Sarkar and Lokendra Kumar Singh and Debranjan Sarkar", title = "A Genetic Algorithm-Based Approach for Detection of Significant Vertices for Polygonal Approximation of Digital Curves", journal = j-INT-J-IMAGE-GRAPHICS, volume = "4", number = "2", pages = "223--??", month = apr, year = "2004", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 6 06:44:13 MST 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Mari:2004:CFF, author = "Jean-Luc Mari and Jean Sequeira", title = "Closed Free-Form Surface Geometrical Modeling a New Approach with Global and Local Characterization", journal = j-INT-J-IMAGE-GRAPHICS, volume = "4", number = "2", pages = "241--??", month = apr, year = "2004", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 6 06:44:13 MST 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Babu:2004:SGM, author = "R. Venkatesh Babu and K. R. Ramakrishnan", title = "Sprite Generation from {MPEG} Video Using Motion Information", journal = j-INT-J-IMAGE-GRAPHICS, volume = "4", number = "2", pages = "263--??", month = apr, year = "2004", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 6 06:44:13 MST 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Alhichri:2004:AIR, author = "Haikel S. Alhichri and Mohamed Kamel", title = "Automatic Image Registration Using Virtual Circles", journal = j-INT-J-IMAGE-GRAPHICS, volume = "4", number = "2", pages = "281--??", month = apr, year = "2004", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 6 06:44:13 MST 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Sarfraz:2004:SAC, author = "Muhammad Sarfraz", title = "Some Algorithms for Curve Design and Automatic Outline Capturing of Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "4", number = "2", pages = "301--??", month = apr, year = "2004", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 6 06:44:13 MST 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Koyama:2004:VPR, author = "Kazuhiro Koyama and Yoshiaki Tomizawa and Minoru Okada", title = "Vectorization and Precise Refractions In Beam Tracing", journal = j-INT-J-IMAGE-GRAPHICS, volume = "4", number = "2", pages = "325--??", month = apr, year = "2004", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 6 06:44:13 MST 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Anonymous:2004:Ea, author = "Anonymous", title = "Editorial", journal = j-INT-J-IMAGE-GRAPHICS, volume = "4", number = "3", pages = "341--??", month = jul, year = "2004", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 6 06:44:13 MST 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Abd-Almageed:2004:ADM, author = "Wael Abd-Almageed and Christopher E. Smith", title = "Active Deformable Models Using Density Estimation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "4", number = "3", pages = "343--??", month = jul, year = "2004", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 6 06:44:13 MST 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Giraldi:2004:ISI, author = "Gilson A. Giraldi and Antonio A. F. Oliveira", title = "Invariant Snakes and Initialization of Deformable Models", journal = j-INT-J-IMAGE-GRAPHICS, volume = "4", number = "3", pages = "363--??", month = jul, year = "2004", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 6 06:44:13 MST 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Shao:2004:APS, author = "Fan Shao and Keck Voon Ling and Wan Sing Ng", title = "Automatic {$3$D} Prostate Surface Detection from {TRUS} with Level Sets", journal = j-INT-J-IMAGE-GRAPHICS, volume = "4", number = "3", pages = "385--??", month = jul, year = "2004", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 6 06:44:13 MST 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Tohka:2004:DMA, author = "Jussi Tohka and Jouni M. Mykk{\"a}nen", title = "Deformable Mesh for Automated Surface Extraction from Noisy Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "4", number = "3", pages = "405--??", month = jul, year = "2004", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 6 06:44:13 MST 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Pujol:2004:TSS, author = "Oriol Pujol and Petia Radeva", title = "Texture Segmentation By Statistical Deformable Models", journal = j-INT-J-IMAGE-GRAPHICS, volume = "4", number = "3", pages = "433--??", month = jul, year = "2004", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 6 06:44:13 MST 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Yazdi:2004:IFP, author = "Mehran Yazdi and Andre Zaccarin", title = "Inter-Frame Prediction of Medical and Videophone Sequences: a Deformable Triangle-Based Approach", journal = j-INT-J-IMAGE-GRAPHICS, volume = "4", number = "3", pages = "453--??", month = jul, year = "2004", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 6 06:44:13 MST 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Tsechpenakis:2004:PBB, author = "Gabriel Tsechpenakis and Nicolas Tsapatsoulis and Stefanos Kollias", title = "Probabilistic Boundary-Based Contour Tracking with Snakes In Natural Cluttered Video Sequences", journal = j-INT-J-IMAGE-GRAPHICS, volume = "4", number = "3", pages = "469--??", month = jul, year = "2004", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 6 06:44:13 MST 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Dornaika:2004:FFF, author = "F. Dornaika and J. Ahlberg", title = "Face and Facial Feature Tracking Using Deformable Models", journal = j-INT-J-IMAGE-GRAPHICS, volume = "4", number = "3", pages = "499--??", month = jul, year = "2004", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 6 06:44:13 MST 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Anonymous:2004:Eb, author = "Anonymous", title = "Editorial", journal = j-INT-J-IMAGE-GRAPHICS, volume = "4", number = "4", pages = "533--??", month = oct, year = "2004", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 6 06:44:13 MST 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Wang:2004:DCI, author = "Yongmei Michelle Wang and Jingdan Zhang and Zhunping Zhang and Baining Guo", title = "Directional Coherence Interpolation for Three-Dimensional Gray-Level Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "4", number = "4", pages = "535--??", month = oct, year = "2004", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 6 06:44:13 MST 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Theobalt:2004:CFT, author = "Christian Theobalt and Marcus A. Magnor and Pascal Sch{\"u}ler and Hans-Peter Seidel", title = "Combining {$2$D} Feature Tracking and Volume Reconstruction for Online Video-Based Human Motion Capture", journal = j-INT-J-IMAGE-GRAPHICS, volume = "4", number = "4", pages = "563--??", month = oct, year = "2004", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 6 06:44:13 MST 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Matsushita:2004:LSI, author = "Yasuyuki Matsushita and Stephen Lin and Heung-Yeung Shum and Xin Tong and Sing Bing Kang", title = "Lighting and Shadow Interpolation Using Intrinsic Lumigraphs", journal = j-INT-J-IMAGE-GRAPHICS, volume = "4", number = "4", pages = "585--??", month = oct, year = "2004", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 6 06:44:13 MST 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Yu:2004:SCS, author = "Jingyi Yu and Leonard McMillan and Steven Gortler", title = "Surface Camera ({SCAM}) Light Field Rendering", journal = j-INT-J-IMAGE-GRAPHICS, volume = "4", number = "4", pages = "605--??", month = oct, year = "2004", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 6 06:44:13 MST 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Yang:2004:UAR, author = "Ruigang Yang and Marc Pollefeys and Hua Yang and Greg Welch", title = "A Unified Approach To Real-Time, Multi-Resolution, Multi-Baseline {$2$D} View Synthesis and {$3$D} Depth Estimation Using Commodity Graphics Hardware", journal = j-INT-J-IMAGE-GRAPHICS, volume = "4", number = "4", pages = "627--??", month = oct, year = "2004", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 6 06:44:13 MST 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Pajarola:2004:DFD, author = "Renato Pajarola and Miguel Sainz and Yu Meng", title = "{DMesh}: Fast Depth-Image Meshing and Warping", journal = j-INT-J-IMAGE-GRAPHICS, volume = "4", number = "4", pages = "653--??", month = oct, year = "2004", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 6 06:44:13 MST 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Brown:2004:IGR, author = "Michael S. Brown and W. Brent Seales", title = "Incorporating Geometric Registration with {PC}-Cluster Rendering for Flexible Tiled Displays", journal = j-INT-J-IMAGE-GRAPHICS, volume = "4", number = "4", pages = "683--??", month = oct, year = "2004", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 6 06:44:13 MST 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Sun:2004:IBT, author = "Jing Sun and George Baciu and Xiaobo Yu and Mark Green", title = "Image-Based Template Generation of Road Networks for Virtual Maps", journal = j-INT-J-IMAGE-GRAPHICS, volume = "4", number = "4", pages = "701--??", month = oct, year = "2004", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 6 06:44:13 MST 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Anonymous:2004:AIV, author = "Anonymous", title = "Author Index (Volume 4)", journal = j-INT-J-IMAGE-GRAPHICS, volume = "4", number = "4", pages = "721--??", month = oct, year = "2004", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 6 06:44:13 MST 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Anonymous:2005:I, author = "Anonymous", title = "Introduction", journal = j-INT-J-IMAGE-GRAPHICS, volume = "5", number = "1", pages = "1--??", month = jan, year = "2005", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Jan 12 05:16:34 MST 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Voloshynovskiy:2005:ITD, author = "Sviatoslav Voloshynovskiy and Frederic Deguillaume and Oleksiy Koval and Thierry Pun", title = "Information-Theoretic Data-Hiding: Recent Achievements and Open Problems", journal = j-INT-J-IMAGE-GRAPHICS, volume = "5", number = "1", pages = "5--??", month = jan, year = "2005", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Jan 12 05:16:34 MST 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Lee:2005:IWR, author = "Choong-Hoon Lee and Heung-Kyu Lee and Youngho Suh", title = "Image Watermarking Resistant to Combined Geometric and Removal Attacks", journal = j-INT-J-IMAGE-GRAPHICS, volume = "5", number = "1", pages = "37--??", month = jan, year = "2005", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Jan 12 05:16:34 MST 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Lu:2005:BIW, author = "Haiping Lu and Yun Q. Shi and Alex C. Kot and Lihui Chen", title = "Binary Image Watermarking Through Blurring and Biased Binarization", journal = j-INT-J-IMAGE-GRAPHICS, volume = "5", number = "1", pages = "67--??", month = jan, year = "2005", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Jan 12 05:16:34 MST 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Echizen:2005:PAV, author = "Isao Echizen and Yasuhiro Fujii and Takaaki Yamada and Satoru Tezuka and Hiroshi Yoshiura", title = "Perceptually Adaptive Video Watermarking Using Motion Estimation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "5", number = "1", pages = "89--??", month = jan, year = "2005", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Jan 12 05:16:34 MST 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Liu:2005:DBV, author = "Hongmei Liu and Jiwu Huang and Yun Q. Shi", title = "{DWT}-Based Video Data Hiding Robust to {MPEG} Compression and Frame Loss", journal = j-INT-J-IMAGE-GRAPHICS, volume = "5", number = "1", pages = "111--??", month = jan, year = "2005", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Jan 12 05:16:34 MST 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Sun:2005:CSS, author = "Qibin Sun and Shuiming Ye and Ching-Yung Lin and Shih-Fu Chang", title = "A Crypto Signature Scheme for Image Authentication over Wireless Channel", journal = j-INT-J-IMAGE-GRAPHICS, volume = "5", number = "1", pages = "135--??", month = jan, year = "2005", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Jan 12 05:16:34 MST 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Piva:2005:SRA, author = "Alessandro Piva and Franco Bartolini and Roberto Caldelli", title = "Self Recovery Authentication of Images in the {DWT} Domain", journal = j-INT-J-IMAGE-GRAPHICS, volume = "5", number = "1", pages = "149--??", month = jan, year = "2005", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Jan 12 05:16:34 MST 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Sallee:2005:MBM, author = "Phil Sallee", title = "Model-Based Methods for Steganography and Steganalysis", journal = j-INT-J-IMAGE-GRAPHICS, volume = "5", number = "1", pages = "167--??", month = jan, year = "2005", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Jan 12 05:16:34 MST 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Gang:2005:CNI, author = "Litao Gang and Ali N. Akansu", title = "Cover Noise Interference Suppression in Multimedia Data Hiding", journal = j-INT-J-IMAGE-GRAPHICS, volume = "5", number = "1", pages = "191--??", month = jan, year = "2005", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Jan 12 05:16:34 MST 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Cheriet:2005:SFB, author = "Mohamed Cheriet and Jean-Christophe Demers and Sylvain Deblois", title = "Shock Filter-Based Diffusion Fields --- Application to Grayscale Character Image Processing", journal = j-INT-J-IMAGE-GRAPHICS, volume = "5", number = "2", pages = "209--??", month = apr, year = "2005", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon Feb 7 16:17:59 MST 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Amin:2005:RST, author = "Adnan Amin and Sue Wu", title = "A Robust System for Thresholding and Skew Detection in Mixed Text\slash Graphics Documents", journal = j-INT-J-IMAGE-GRAPHICS, volume = "5", number = "2", pages = "247--??", month = apr, year = "2005", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon Feb 7 16:17:59 MST 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Dawoud:2005:NSN, author = "Amer Dawoud and Mohamed Kamel", title = "Natural Skeletonization: New Approach for the Skeletonization of Handwritten Characters", journal = j-INT-J-IMAGE-GRAPHICS, volume = "5", number = "2", pages = "267--??", month = apr, year = "2005", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon Feb 7 16:17:59 MST 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Chi:2005:DIB, author = "Zheru Chi and Qing Wang", title = "Document Image Binarization with Feedback for Improving Character Segmentation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "5", number = "2", pages = "281--??", month = apr, year = "2005", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon Feb 7 16:17:59 MST 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Brown:2005:DRC, author = "Michael S. Brown and Yau-Chat Tsoi", title = "Distortion Removal for Camera-Imaged Print Materials Using Boundary Interpolation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "5", number = "2", pages = "311--??", month = apr, year = "2005", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon Feb 7 16:17:59 MST 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Lelandais:2005:STL, author = "S. Lelandais and L. Boutte and J. Plantier", title = "Shape from Texture: Local Scales and Vanishing Line Computation to Improve Results for Macrotextures", journal = j-INT-J-IMAGE-GRAPHICS, volume = "5", number = "2", pages = "329--??", month = apr, year = "2005", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon Feb 7 16:17:59 MST 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Wang:2005:AEM, author = "Xiuying Wang and David Dagan Feng", title = "Automatic Elastic Medical Image Registration Based On Image Intensity", journal = j-INT-J-IMAGE-GRAPHICS, volume = "5", number = "2", pages = "351--??", month = apr, year = "2005", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon Feb 7 16:17:59 MST 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Zhao:2005:HPR, author = "Jianhui Zhao and Ling Li and Kwoh Chee Keong", title = "Human Posture Reconstruction and Animation from Monocular Images Based on Genetic Algorithms", journal = j-INT-J-IMAGE-GRAPHICS, volume = "5", number = "2", pages = "371--??", month = apr, year = "2005", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon Feb 7 16:17:59 MST 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Bing:2005:EPR, author = "Cheng Bing and Wang Ying and Zheng Nanning and Bian Zhengzhong", title = "An Efficient {$3$D} Plenoptic Representation for Approximating a Path of Motion to a Curved Line", journal = j-INT-J-IMAGE-GRAPHICS, volume = "5", number = "2", pages = "397--??", month = apr, year = "2005", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon Feb 7 16:17:59 MST 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Wang:2005:PVR, author = "Wencheng Wang and Hanqiu Sun and Enhua Wu", title = "Projective Volume Rendering by Excluding Occluded Voxels", journal = j-INT-J-IMAGE-GRAPHICS, volume = "5", number = "2", pages = "413--??", month = apr, year = "2005", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon Feb 7 16:17:59 MST 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kang:2005:NVE, author = "Hyung W. Kang", title = "Nonphotorealistic Virtual Environment Navigation From Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "5", number = "2", pages = "433--??", month = apr, year = "2005", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon Feb 7 16:17:59 MST 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Ratschek:2005:SHM, author = "Helmut Ratschek and Jon Rokne", title = "{SCCI}-Hybrid Methods for {$2$D} Curve Tracing", journal = j-INT-J-IMAGE-GRAPHICS, volume = "5", number = "3", pages = "447--479", month = jul, year = "2005", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467805001859", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jul 5 06:13:03 MDT 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "http://ejournals.wspc.com.sg/ijig/05/preserved-docs/0503/S0219467805001859.pdf", abstract = "A hybrid method for plotting 2-dimensional curves, defined implicitly by equations of the form f(x,y) = 0 is presented. The method is extremely robust and reliable and consists of Space Covering techniques, Continuation principles and Interval analysis (i.e. SCCI). The space covering, based on iterated subdivision, guarantees that no curve branches or isolated curve parts or even points are lost (which can happen if grid methods are used). The continuation method is initiated in a subarea as soon as it is proven that the subarea contains only one smooth curve. Such a subarea does not need to be subdivided further so that the computation is accelerated as far as possible with respect to the subdivision process. The novelty of the SCCI-hybrid method is the intense use of the implicit function theorem for controlling the steps of the method. Although the implicit function theorem has a rather local nature, it is empowered with global properties by evaluating it in an interval environment. This means that the theorem can provide global information about the curve in a subarea such as existence, non-existence, uniqueness of the curve or even the presence of singular points. The information gained allows the above-mentioned control of the subarea and the decision of its further processing, i.e. deleting it, subdividing it, switching to the continuation method or preparing the plotting of the curve in this subarea. The curves can be processed mathematically in such a manner, that the derivation of the plotted curve from the exact curve is as small as desired (modulo the screen resolution).", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", keywords = "SCCI (Space Covering techniques, Continuation principles and Interval analysis)", } @Article{Hicks:2005:AMC, author = "B. J. Hicks and G. Mullineux and A. J. Medland", title = "Automatic Model Creation for Kinematic Analysis and Optimization of Engineering Systems", journal = j-INT-J-IMAGE-GRAPHICS, volume = "5", number = "3", pages = "481--499", month = jul, year = "2005", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467805001860", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jul 5 06:13:03 MDT 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "http://ejournals.wspc.com.sg/ijig/05/preserved-docs/0503/S0219467805001860.pdf", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Hong:2005:RPE, author = "Jin-Hyuk Hong and Eun-Kyung Yun and Sung-Bae Cho", title = "A Review of Performance Evaluation for Biometrics Systems", journal = j-INT-J-IMAGE-GRAPHICS, volume = "5", number = "3", pages = "501--536", month = jul, year = "2005", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467805001872", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jul 5 06:13:03 MDT 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "http://ejournals.wspc.com.sg/ijig/05/preserved-docs/0503/S0219467805001872.pdf", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Bhowmick:2005:DMS, author = "Partha Bhowmick and Arijit Bishnu and Bhargab Bikram Bhattacharya and Malay Kumar Kundu and C. A. Murthy and Tinku Acharya", title = "Determination of Minutiae Scores for Fingerprint Image Applications", journal = j-INT-J-IMAGE-GRAPHICS, volume = "5", number = "3", pages = "537--571", month = jul, year = "2005", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467805001896", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jul 5 06:13:03 MDT 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "http://ejournals.wspc.com.sg/ijig/05/preserved-docs/0503/S0219467805001896.pdf", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Pan:2005:FRR, author = "Gang Pan and Zhaohui Wu", title = "{$3$D} Face Recognition from Range Data", journal = j-INT-J-IMAGE-GRAPHICS, volume = "5", number = "3", pages = "573--593", month = jul, year = "2005", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467805001884", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jul 5 06:13:03 MDT 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "http://ejournals.wspc.com.sg/ijig/05/preserved-docs/0503/S0219467805001884.pdf", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kharma:2005:PCC, author = "Nawwaf Kharma and Ching Y. Suen and Pei F. Guo", title = "{Palmprints}: a Cooperative Co-Evolutionary Algorithm for Clustering Hand Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "5", number = "3", pages = "595--616", month = jul, year = "2005", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467805001902", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jul 5 06:13:03 MDT 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "http://ejournals.wspc.com.sg/ijig/05/preserved-docs/0503/S0219467805001902.pdf", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Drago:2005:LAC, author = "Fr{\'e}d{\'e}ric Drago and Norishige Chiba", title = "Locally Adaptive Chromatic Restoration of Digitally Acquired Paintings", journal = j-INT-J-IMAGE-GRAPHICS, volume = "5", number = "3", pages = "617--637", month = jul, year = "2005", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467805001914", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jul 5 06:13:03 MDT 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "http://ejournals.wspc.com.sg/ijig/05/preserved-docs/0503/S0219467805001914.pdf", abstract = "This article presents a semi-automatic procedure to restore the visual appearance of aged paintings converted to a digital form. The innovative implementation of an image-processing algorithm based on the Retinex theory of human vision alleviates layers of yellowed varnish and dust, restores chromatic balance and contrast, and recovers some of the original painted details. This virtual cleaning of artwork is totally non-intrusive and can be applied automatically to color images of paintings or ancient illustrations. Cleaned virtual reproductions help art historians and restorers in their research and classification work, and also show the artwork in good condition to a wide audience while avoiding an always costly and dangerous manual restoration.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Noyer:2005:SME, author = "Jean-Charles Noyer and Christophe Boucher and Mohammed Benjelloun", title = "{$3$D} Structure and Motion Estimation from Range and Intensity Images Using Particle Filtering", journal = j-INT-J-IMAGE-GRAPHICS, volume = "5", number = "3", pages = "639--661", month = jul, year = "2005", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467805001926", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jul 5 06:13:03 MDT 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "http://ejournals.wspc.com.sg/ijig/05/preserved-docs/0503/S0219467805001926.pdf", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Taylor-Hell:2005:SAR, author = "Julia F. Taylor-Hell and Gladimir V. G. Baranoski and Jon G. Rokne", title = "State of the Art in the Realistic Modeling of Plant Venation Systems", journal = j-INT-J-IMAGE-GRAPHICS, volume = "5", number = "3", pages = "663--678", month = jul, year = "2005", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467805001938", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jul 5 06:13:03 MDT 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "http://ejournals.wspc.com.sg/ijig/05/preserved-docs/0503/S0219467805001938.pdf", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Marchesotti:2005:VPU, author = "Luca Marchesotti and Carlo Regazzoni and Carlo Bonamico and Fabio Lavagetto", title = "Video Processing and Understanding Tools for Augmented Multisensor Perception and Mobile User Interaction in Smart Spaces", journal = j-INT-J-IMAGE-GRAPHICS, volume = "5", number = "3", pages = "679--698", month = jul, year = "2005", CODEN = "????", DOI = "https://doi.org/10.1142/S021946780500194X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jul 5 06:13:03 MDT 2005", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "http://ejournals.wspc.com.sg/ijig/05/preserved-docs/0503/S021946780500194X.pdf", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Zhou:2005:COV, author = "Jianlong Zhou and Andreas D{\"o}ring and Klaus D. T{\"o}nnies", title = "Control of Object Visibility in Volume Rendering --- a Distance-Based Approach", journal = j-INT-J-IMAGE-GRAPHICS, volume = "5", number = "4", pages = "699--??", month = oct, year = "2005", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 23 08:55:54 MDT 2006", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Wang:2005:NMA, author = "Qiang Wang and Hongbo Chen and Xiaorong Xu and Haiyan Liu", title = "A Newly Modified Algorithm of {Hough Transform} for Line Detection", journal = j-INT-J-IMAGE-GRAPHICS, volume = "5", number = "4", pages = "715--??", month = oct, year = "2005", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 23 08:55:54 MDT 2006", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Zhu:2005:FMR, author = "En Zhu and Jian-Ping Yin and Guo-Min Zhang and Chun-Feng Hu", title = "Fingerprint Minutiae Relationship Representation and Matching Based on Curve Coordinate System", journal = j-INT-J-IMAGE-GRAPHICS, volume = "5", number = "4", pages = "729--??", month = oct, year = "2005", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 23 08:55:54 MDT 2006", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Hu:2005:LBR, author = "Yu-Chen Hu", title = "Low Bit-Rate Image Compression Schemes Based on Vector Quantization", journal = j-INT-J-IMAGE-GRAPHICS, volume = "5", number = "4", pages = "745--??", month = oct, year = "2005", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 23 08:55:54 MDT 2006", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Liu:2005:MSM, author = "Yong-Jin Liu and Kai Tang and Ajay Joneja and Matthew Ming-Fai Yuen", title = "Multiresolution Shape Modeling and Editing in Reverse Engineering", journal = j-INT-J-IMAGE-GRAPHICS, volume = "5", number = "4", pages = "765--??", month = oct, year = "2005", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 23 08:55:54 MDT 2006", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Singh:2005:FHS, author = "Chandan Singh and Ekta Walia", title = "Fast Hybrid Shading: an Application of Finite Element Methods in {$3$D} Rendering", journal = j-INT-J-IMAGE-GRAPHICS, volume = "5", number = "4", pages = "789--??", month = oct, year = "2005", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 23 08:55:54 MDT 2006", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Rahman:2005:ODE, author = "M. Masudur Rahman and Seiji Ishikawa", title = "Overcoming Dress Effect in Eigenspace", journal = j-INT-J-IMAGE-GRAPHICS, volume = "5", number = "4", pages = "811--??", month = oct, year = "2005", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 23 08:55:54 MDT 2006", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Rahman:2005:HPR, author = "M. Masudur Rahman and Seiji Ishikawa", title = "Human Posture Recognition: Eigenspace Tuning by a Mean Eigenspace", journal = j-INT-J-IMAGE-GRAPHICS, volume = "5", number = "4", pages = "825--??", month = oct, year = "2005", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 23 08:55:54 MDT 2006", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Chen:2005:TSC, author = "Zhe Chen and David Dagan Feng and Weidong Cai", title = "Temporal and Spatial Compression of Dynamic Positron Emission Tomography in Sinogram Domain", journal = j-INT-J-IMAGE-GRAPHICS, volume = "5", number = "4", pages = "839--??", month = oct, year = "2005", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 23 08:55:54 MDT 2006", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Deng:2005:PBS, author = "Yuhui Deng and Frank Wang and Jiangling Zhang and Dan Feng and Fang Wang and Hong Jiang", title = "Push the Bottleneck of Streaming Media System from Streaming Media Server to Network", journal = j-INT-J-IMAGE-GRAPHICS, volume = "5", number = "4", pages = "859--??", month = oct, year = "2005", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 23 08:55:54 MDT 2006", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Anonymous:2005:AIV, author = "Anonymous", title = "Author Index (Volume 5)", journal = j-INT-J-IMAGE-GRAPHICS, volume = "5", number = "4", pages = "871--??", month = oct, year = "2005", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 23 08:55:54 MDT 2006", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kim:2006:VPC, author = "Jaeho Kim and Hyungseok Kim and Kwangyun Wohn", title = "Visibility Preprocessing for Complex {$3$D} Scenes Using Hardware-Visibility Queries", journal = j-INT-J-IMAGE-GRAPHICS, volume = "6", number = "1", pages = "1--??", month = jan, year = "2006", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 23 08:55:54 MDT 2006", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Xu:2006:NCW, author = "Qing Xu and Wei Wang and Shiqiang Bao", title = "A New Computational Way to {Monte Carlo} Global Illumination", journal = j-INT-J-IMAGE-GRAPHICS, volume = "6", number = "1", pages = "23--??", month = jan, year = "2006", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 23 08:55:54 MDT 2006", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Li:2006:PMW, author = "Li Li and Zhigeng Pan and David Zhang", title = "A Public Mesh Watermarking Algorithm Based on Addition Property of {Fourier Transform}", journal = j-INT-J-IMAGE-GRAPHICS, volume = "6", number = "1", pages = "35--??", month = jan, year = "2006", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 23 08:55:54 MDT 2006", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Cao:2006:DRD, author = "Weiqun Cao and Hendrik Gaertner and Hannes Guddat and Andreas M. Straube and Stefan Conrad and Ernst Kruijff and Dirk Langenberg", title = "Design Review in a Distributed Collaborative Virtual Environment", journal = j-INT-J-IMAGE-GRAPHICS, volume = "6", number = "1", pages = "45--??", month = jan, year = "2006", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 23 08:55:54 MDT 2006", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Tan:2006:IVE, author = "Jiacheng Tan and Gordon J. Clapworthy and Igor R. Belousov", title = "The Integration of a Virtual Environment and {$3$D} Modeling Tools in a Networked Robot System", journal = j-INT-J-IMAGE-GRAPHICS, volume = "6", number = "1", pages = "65--??", month = jan, year = "2006", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 23 08:55:54 MDT 2006", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Favier:2006:DAI, author = "Pierre-Alexandre Favier and Pierre {De Loor}", title = "From Decision to Action: Intentionality, a Guide for the Specification of Intelligent Agent's Behavior", journal = j-INT-J-IMAGE-GRAPHICS, volume = "6", number = "1", pages = "87--??", month = jan, year = "2006", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 23 08:55:54 MDT 2006", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kumar:2006:IST, author = "Ajay Kumar and David Zhang", title = "Integrating Shape and Texture for Hand Verification", journal = j-INT-J-IMAGE-GRAPHICS, volume = "6", number = "1", pages = "101--??", month = jan, year = "2006", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 23 08:55:54 MDT 2006", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Zheng:2006:SBA, author = "Qing-Fang Zheng and Wei Zeng and Wei-Qiang Wang and Wen Gao", title = "Shape-Based Adult Image Detection", journal = j-INT-J-IMAGE-GRAPHICS, volume = "6", number = "1", pages = "115--??", month = jan, year = "2006", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 23 08:55:54 MDT 2006", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Zhan:2006:FER, author = "Yongzhao Zhan and Jingfu Ye and Dejiao Niu and Peng Cao", title = "Facial Expression Recognition Based on {Gabor} Wavelet Transformation and Elastic Templates Matching", journal = j-INT-J-IMAGE-GRAPHICS, volume = "6", number = "1", pages = "125--??", month = jan, year = "2006", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 23 08:55:54 MDT 2006", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Li:2006:AMS, author = "Xuelong Li and Yuan Yuan and Dacheng Tao", title = "Artistic Mosaic Series Generation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "6", number = "1", pages = "139--??", month = jan, year = "2006", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 23 08:55:54 MDT 2006", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Wang:2006:DLL, author = "Zhenlan Wang and Chee-Kong Chui and Yiyu Cai and Chuan-Heng Ang and Swee-Hin Teoh", title = "Dynamic Linear Level Octree-Based Volume Rendering Methods for Interactive Microsurgical Simulation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "6", number = "2", pages = "155--??", month = apr, year = "2006", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 23 08:55:54 MDT 2006", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Fang:2006:MSI, author = "Shiaofen Fang and Marwan Adada", title = "Multi-Scale Iso-Surface Extraction for Volume Visualization", journal = j-INT-J-IMAGE-GRAPHICS, volume = "6", number = "2", pages = "173--??", month = apr, year = "2006", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 23 08:55:54 MDT 2006", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Lakshmipathy:2006:TBI, author = "Jagannathan Lakshmipathy and Wieslaw L. Nowinski and Eric A. Wernert", title = "Template-Based Isocontouring", journal = j-INT-J-IMAGE-GRAPHICS, volume = "6", number = "2", pages = "187--??", month = apr, year = "2006", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 23 08:55:54 MDT 2006", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Heng:2006:HNM, author = "Pheng-Ann Heng and Tien-Tsin Wong and Ka-Man Leung and Yim-Pan Chui and Hanqiu Sun", title = "A Haptic Needle Manipulation Simulator for {Chinese} Acupuncture Learning and Training", journal = j-INT-J-IMAGE-GRAPHICS, volume = "6", number = "2", pages = "205--??", month = apr, year = "2006", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 23 08:55:54 MDT 2006", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Guan:2006:VEC, author = "Y. Q. Guan and Y. Y. Cai and M. Opas and Z. W. Xiong and Y. T. Lee", title = "A {VR} Enhanced Collaborative System for {$3$D} Confocal Microscopic Image Processing and Visualization", journal = j-INT-J-IMAGE-GRAPHICS, volume = "6", number = "2", pages = "231--??", month = apr, year = "2006", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 23 08:55:54 MDT 2006", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Lu:2006:BNS, author = "Baifang Lu and Zhaowei Fan and Jianmin Zheng and Lin Li", title = "Bio-Native Shape Modeling and Virtual Reality for Bio Education", journal = j-INT-J-IMAGE-GRAPHICS, volume = "6", number = "2", pages = "251--??", month = apr, year = "2006", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 23 08:55:54 MDT 2006", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Djemal:2006:AAC, author = "Khalifa Djemal and William Puech and Bruno Rossetto", title = "Automatic Active Contours Propagation in a Sequence of Medical Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "6", number = "2", pages = "267--??", month = apr, year = "2006", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 23 08:55:54 MDT 2006", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Marcialis:2006:DLF, author = "Gian Luca Marcialis and Fabio Roli", title = "Decision-Level Fusion of {PCA} and {LDA}-Based Face Recognition Algorithms", journal = j-INT-J-IMAGE-GRAPHICS, volume = "6", number = "2", pages = "293--??", month = apr, year = "2006", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 23 08:55:54 MDT 2006", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Gong:2006:RTI, author = "Minglun Gong and Yee-Hong Yang", title = "{Rayset}: a Taxonomy for Image-Based Rendering", journal = j-INT-J-IMAGE-GRAPHICS, volume = "6", number = "3", pages = "313--??", month = jul, year = "2006", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 23 08:55:54 MDT 2006", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Knopf:2006:FSR, author = "George K. Knopf and Archana P. Sangole", title = "Freeform Surface Reconstruction from Scattered Points Using a Deformable Spherical Map", journal = j-INT-J-IMAGE-GRAPHICS, volume = "6", number = "3", pages = "341--??", month = jul, year = "2006", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 23 08:55:54 MDT 2006", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{AlAghbari:2006:RBS, author = "Zaher {Al Aghbari}", title = "Region-Based Semantic Image Classification", journal = j-INT-J-IMAGE-GRAPHICS, volume = "6", number = "3", pages = "357--??", month = jul, year = "2006", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 23 08:55:54 MDT 2006", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Tizhoosh:2006:RCA, author = "Hamid R. Tizhoosh and Graham W. Taylor", title = "Reinforced Contrast Adaptation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "6", number = "3", pages = "377--??", month = jul, year = "2006", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 23 08:55:54 MDT 2006", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Zhou:2006:SAR, author = "Hong Zhou and Ray Seyfarth", title = "Semi Automatic Registration of Partially Overlapped Aerial Images Via Pattern Search Method", journal = j-INT-J-IMAGE-GRAPHICS, volume = "6", number = "3", pages = "393--??", month = jul, year = "2006", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 23 08:55:54 MDT 2006", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Li:2006:OSV, author = "Bin Li and David Zhang and Kuanquan Wang", title = "Online Signature Verification by Combining Shape Contexts and Local Features", journal = j-INT-J-IMAGE-GRAPHICS, volume = "6", number = "3", pages = "407--??", month = jul, year = "2006", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 23 08:55:54 MDT 2006", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{ElRube:2006:MRS, author = "Ibrahim {El Rub{\'e}} and Naif Alajlan and Mohamed S. Kamel and Maher Ahmed and George H. Freeman", title = "{Mtar}: a Robust {$2$D} Shape Representation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "6", number = "3", pages = "421--??", month = jul, year = "2006", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 23 08:55:54 MDT 2006", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Zhong:2006:HDS, author = "Yongmin Zhong and Bijan Shirinzadeh and Gursel Alici and Julian Smith", title = "Haptic Deformation Simulation with {Poisson} Equation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "6", number = "3", pages = "445--??", month = jul, year = "2006", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 23 08:55:54 MDT 2006", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Bao:2006:RTS, author = "Paul Bao and Xiaohu Ma and Wan-Chi Siu", title = "Real-Time Seamless Texture Synthesis Based on Patch Quantization Clustering", journal = j-INT-J-IMAGE-GRAPHICS, volume = "6", number = "3", pages = "475--??", month = jul, year = "2006", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 23 08:55:54 MDT 2006", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Fang:2006:NMF, author = "Xianyong Fang and Zhigeng Pan and Gaoqi He and Li Li", title = "A New Method of Feature Based Image Mosaic", journal = j-INT-J-IMAGE-GRAPHICS, volume = "6", number = "3", pages = "497--??", month = jul, year = "2006", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 23 08:55:54 MDT 2006", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Ng:2006:IFN, author = "Geok See Ng and Sevki Erdogan and Daming Shi and Abdul Wahab", title = "Insight of Fuzzy Neural Systems in the Application of Handwritten Digits Classification", journal = j-INT-J-IMAGE-GRAPHICS, volume = "6", number = "4", pages = "511--532", month = oct, year = "2006", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467806002410", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Cai:2006:DIW, author = "Weiting Cai and Malek Adjouadi", title = "Design and Implementation of Wavelet-Domain Video Compression Using Multiresolution Motion Estimation and Compensation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "6", number = "4", pages = "533--549", month = oct, year = "2006", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467806002471", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Faudot:2006:SVV, author = "Dominique Faudot and Gilles Gesquiere", title = "Study of Volume Variation of Implicit Objects", journal = j-INT-J-IMAGE-GRAPHICS, volume = "6", number = "4", pages = "551--568", month = oct, year = "2006", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467806002483", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Regentova:2006:ISU, author = "Emma Regentova and Dongsheng Yao and Shahram Latifi and Jun Zheng", title = "Image Segmentation Using Ncut in the Wavelet Domain", journal = j-INT-J-IMAGE-GRAPHICS, volume = "6", number = "4", pages = "569--582", month = oct, year = "2006", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467806002458", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Yu:2006:CIR, author = "Shengsheng Yu and Chaobing Huang and Jingli Zhou", title = "Color Image Retrieval Based on Color-Texture-Edge Feature Histograms", journal = j-INT-J-IMAGE-GRAPHICS, volume = "6", number = "4", pages = "583--598", month = oct, year = "2006", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467806002392", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Chow:2006:FRR, author = "S. K. Chow and K. L. Chan", title = "Fast and Realistic Rendering of Deformable Virtual Characters Using Impostor and Stencil Buffer", journal = j-INT-J-IMAGE-GRAPHICS, volume = "6", number = "4", pages = "599--624", month = oct, year = "2006", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467806002409", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Skala:2006:LAV, author = "Vaclav Skala", title = "Length, Area and Volume Computation in Homogeneous Coordinates", journal = j-INT-J-IMAGE-GRAPHICS, volume = "6", number = "4", pages = "625--639", month = oct, year = "2006", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467806002422", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Li:2006:RTM, author = "Xiaoying Li and Enhua Wu", title = "Relief Texture Mapping on Field Programmable Gate Array", journal = j-INT-J-IMAGE-GRAPHICS, volume = "6", number = "4", pages = "641--655", month = oct, year = "2006", CODEN = "????", DOI = "https://doi.org/10.1142/S021946780600246X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Hardy:2006:HII, author = "Alexandre Hardy and Willi-Hans Steeb", title = "Harmonic Interpolation for Image Compression", journal = j-INT-J-IMAGE-GRAPHICS, volume = "6", number = "4", pages = "657--675", month = oct, year = "2006", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467806002434", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Bi:2006:NNR, author = "Dong-Liang Bi and Wei Guo and Ai-Dong Xu", title = "A New Noise Removing Algorithm", journal = j-INT-J-IMAGE-GRAPHICS, volume = "6", number = "4", pages = "677--687", month = oct, year = "2006", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467806002446", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Anonymous:2006:AIV, author = "Anonymous", title = "Author Index (Volume 6)", journal = j-INT-J-IMAGE-GRAPHICS, volume = "6", number = "4", pages = "689--691", month = oct, year = "2006", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467806002446", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Liang:2007:GEM, author = "Jerome Zhengrong Liang and Hongbing Lu and Dimitris N. Metaxas and Joseph M. Reinhardt", title = "Guest Editorial: Medical Imaging Informatics --- An Information Processing from Image Formation to Visualization", journal = j-INT-J-IMAGE-GRAPHICS, volume = "7", number = "1", pages = "1--15", month = jan, year = "2007", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467807002568", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Hawkins:2007:ESU, author = "William G. Hawkins", title = "On the Equivalence of Stable and Unstable Forms of the Inverse Circular Harmonic Transform Solution for the {Radon} Transform", journal = j-INT-J-IMAGE-GRAPHICS, volume = "7", number = "1", pages = "17--33", month = jan, year = "2007", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467807002519", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kao:2007:EAA, author = "Chien-Min Kao and Yu Zou and Seungryong Cho and Xiaochuan Pan", title = "An Exact Analytic Approach to {$3$D} {PET} Image Reconstruction", journal = j-INT-J-IMAGE-GRAPHICS, volume = "7", number = "1", pages = "35--54", month = jan, year = "2007", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467807002520", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Gao:2007:FVO, author = "Xin Gao and Yuanmei Wang and Cishen Zhang", title = "Fuzzy Vector Objective Optimization Algorithm for Image Reconstruction from Incomplete Projections", journal = j-INT-J-IMAGE-GRAPHICS, volume = "7", number = "1", pages = "55--69", month = jan, year = "2007", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467807002532", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Huang:2007:AIE, author = "Qiu Huang and Gengsheng L. Zeng and Grant T. Gullberg", title = "An Analytical Inversion of the $180^\circ$ Exponential {Radon} Transform With a Numerically Generated Kernel", journal = j-INT-J-IMAGE-GRAPHICS, volume = "7", number = "1", pages = "71--85", month = jan, year = "2007", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467807002544", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Fan:2007:FAR, author = "Yi Fan and Hongbing Lu and Chongyang Hao and Zhengrong Liang and Zhiming Zhou", title = "Fast Analytical Reconstruction of Gated Cardiac {SPECT} with Non-Uniform Attenuation Compensation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "7", number = "1", pages = "87--104", month = jan, year = "2007", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467807002556", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Wang:2007:ISE, author = "Zhenguo Wang and Christopher S. D. Lee and Wayne C. Waltzer and Zhijia Yuan and Yingtian Pan", title = "Interpixel-Shifted Endoscopic Optical Coherence Tomography for in Vivo Bladder Cancer Diagnosis", journal = j-INT-J-IMAGE-GRAPHICS, volume = "7", number = "1", pages = "105--117", month = jan, year = "2007", CODEN = "????", DOI = "https://doi.org/10.1142/S021946780700257X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Lei:2007:MRM, author = "Tianhu Lei and Felix W. Wehrli", title = "Magnetic Resonance ({MR}) Image Analysis --- a Statistical Approach", journal = j-INT-J-IMAGE-GRAPHICS, volume = "7", number = "1", pages = "119--141", month = jan, year = "2007", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467807002581", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Cui:2007:DMF, author = "Yunfeng Cui and Jing Bai and Yingmao Chen and Jiahe Tian", title = "A Digital Model Framework of Metabolic System Based on Visible Human Data Set", journal = j-INT-J-IMAGE-GRAPHICS, volume = "7", number = "1", pages = "143--157", month = jan, year = "2007", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467807002593", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Cai:2007:CVC, author = "Wenli Cai and Gordon J. Harris and Hiroyuki Yoshida", title = "Computation of Vesselness in {CTA} Images for Fast and Interactive Vessel Segmentation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "7", number = "1", pages = "159--176", month = jan, year = "2007", CODEN = "????", DOI = "https://doi.org/10.1142/S021946780700260X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Papaleo:2007:ASR, author = "Laura Papaleo", title = "An Approach to Surface Reconstruction Using Uncertain Data", journal = j-INT-J-IMAGE-GRAPHICS, volume = "7", number = "1", pages = "177--194", month = jan, year = "2007", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467807002611", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Tian:2007:DEV, author = "Yun Tian and Chongyang Hao and Yi Wang and Guiqing He and Jun Wei and Haitiao Zhao and Benhua Zhao", title = "Dynamic Extraction for {VOI} from {CT} Images Based on Volume Rendering", journal = j-INT-J-IMAGE-GRAPHICS, volume = "7", number = "1", pages = "195--209", month = jan, year = "2007", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467807002623", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Li:2007:SMI, author = "Xuelong Li and Jing Li and Dacheng Tao and Yuan Yuan", title = "A Similarity Metric in Image Searching", journal = j-INT-J-IMAGE-GRAPHICS, volume = "7", number = "2", pages = "211--225", month = apr, year = "2007", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467807002635", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Tao:2007:RCO, author = "Ji Tao and Yap-Peng Tan and Wenmiao Lu", title = "Robust Color Object Tracking with Application to People Monitoring", journal = j-INT-J-IMAGE-GRAPHICS, volume = "7", number = "2", pages = "227--254", month = apr, year = "2007", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467807002647", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Werghi:2007:LTD, author = "Naoufel Werghi and Yijun Xiao and Paul Siebert", title = "Labelling of Three Dimensional Human Body Scans: a Topological Approach", journal = j-INT-J-IMAGE-GRAPHICS, volume = "7", number = "2", pages = "255--272", month = apr, year = "2007", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467807002659", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Livny:2007:DAP, author = "Yotam Livny and Neta Sokolovsky and Jihad El-Sana", title = "Dual Adaptive Paths for Multiresolution Hierarchies", journal = j-INT-J-IMAGE-GRAPHICS, volume = "7", number = "2", pages = "273--290", month = apr, year = "2007", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467807002726", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Abdelwahab:2007:FCD, author = "Ahmed A. Abdelwahab and Nora S. Muharram", title = "A Fast Codebook Design Algorithm Based on a Fuzzy Clustering Methodology", journal = j-INT-J-IMAGE-GRAPHICS, volume = "7", number = "2", pages = "291--302", month = apr, year = "2007", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467807002714", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Ayed:2007:TOB, author = "Mohamed Ali Ben Ayed and Amine Samet and Nouri Masmoudi", title = "Toward an Optimal Block Motion Estimation Algorithm for {H.264\slash AVC}", journal = j-INT-J-IMAGE-GRAPHICS, volume = "7", number = "2", pages = "303--320", month = apr, year = "2007", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467807002660", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Djebali:2007:CBM, author = "M. Djebali and M. Melkemi and K. Melkemi and N. Sapidis", title = "Coiflet Based Methods for Range Image Processing", journal = j-INT-J-IMAGE-GRAPHICS, volume = "7", number = "2", pages = "321--351", month = apr, year = "2007", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467807002672", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Zhang:2007:ADM, author = "Liang Zhang and Qingping Lin and Robert Gay and Guangbin Huang and Norman Neo", title = "An Autonomous Decentralized Multi-Server Framework for Large Scale Collaborative Virtual Environments", journal = j-INT-J-IMAGE-GRAPHICS, volume = "7", number = "2", pages = "353--375", month = apr, year = "2007", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467807002684", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Nagendraswamy:2007:NMR, author = "H. S. Nagendraswamy and D. S. Guru", title = "A New Method of Representing and Matching Two Dimensional Shapes", journal = j-INT-J-IMAGE-GRAPHICS, volume = "7", number = "2", pages = "377--405", month = apr, year = "2007", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467807002696", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Verma:2007:CSI, author = "Nishchal K. Verma and M. Hanmandlu", title = "Color Segmentation Via Improved Mountain Clustering Technique", journal = j-INT-J-IMAGE-GRAPHICS, volume = "7", number = "2", pages = "407--426", month = apr, year = "2007", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467807002702", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Xu:2007:MCM, author = "Xinyu Xu and Baoxin Li", title = "Multiple Class Multiple-Instance Learning and Its Application to Image Categorization", journal = j-INT-J-IMAGE-GRAPHICS, volume = "7", number = "3", pages = "427--444", month = jul, year = "2007", CODEN = "????", DOI = "https://doi.org/10.1142/S021946780700274X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Cheng:2007:OBP, author = "Jun Cheng and Ronald Chung and Edmund Y. Lam and Kenneth S. M. Fung and Yangsheng Xu", title = "Optimization of Bit-Pairing Codification with Learning for {$3$D} Reconstruction", journal = j-INT-J-IMAGE-GRAPHICS, volume = "7", number = "3", pages = "445--462", month = jul, year = "2007", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467807002763", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Song:2007:LLD, author = "Mingli Song and Huiqiong Wang and Chun Chen", title = "Local {Laplacian} Detail Learning for Face Aging Manipulation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "7", number = "3", pages = "463--480", month = jul, year = "2007", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467807002775", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Li:2007:NBA, author = "Weihai Li and Yuan Yuan", title = "A New Blind Attack Procedure for {DCT}-Based Image Encryption with Spectrum Learning", journal = j-INT-J-IMAGE-GRAPHICS, volume = "7", number = "3", pages = "481--496", month = jul, year = "2007", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467807002787", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Zhou:2007:IRD, author = "Huiyu Zhou and Tangwei Liu and Faquan Lin and Yusheng Pang and Ji Wu", title = "Image Restoration and Detail Preservation by {Bayesian} Estimation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "7", number = "3", pages = "497--514", month = jul, year = "2007", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467807002738", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Liu:2007:TDI, author = "Chunsheng Liu and Tianxu Zhang and Biyin Zhang", title = "Turbulence Degraded Images Restoration Based on Improved Multiframe Iterative Loops and Data Mining", journal = j-INT-J-IMAGE-GRAPHICS, volume = "7", number = "3", pages = "515--527", month = jul, year = "2007", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467807002799", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Wang:2007:BCR, author = "Kongqiao Wang and Yanming Zou and Hao Wang", title = "{$1$D} Bar Code Reading on Camera Phones", journal = j-INT-J-IMAGE-GRAPHICS, volume = "7", number = "3", pages = "529--550", month = jul, year = "2007", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467807002805", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Shen:2007:ESQ, author = "Jialie Shen and John Shepherd and Anne H. H. Ngu", title = "An Empirical Study of Query Effectiveness Improvement Via Multiple Visual Feature Integration", journal = j-INT-J-IMAGE-GRAPHICS, volume = "7", number = "3", pages = "551--581", month = jul, year = "2007", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467807002751", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Xu:2007:FRG, author = "Dong Xu and Dacheng Tao and Xuelong Li and Shuicheng Yan", title = "Face Recognition --- a Generalized Marginal {Fisher} Analysis Approach", journal = j-INT-J-IMAGE-GRAPHICS, volume = "7", number = "3", pages = "583--591", month = jul, year = "2007", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467807002817", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Anonymous:2007:MLI, author = "Anonymous", title = "Machine Learning in Image and Graphics", journal = j-INT-J-IMAGE-GRAPHICS, volume = "7", number = "3", pages = "v--v", month = jul, year = "2007", CODEN = "????", DOI = "", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Brunn:2007:CRU, author = "Meru Brunn and Mario Costa Sousa and Faramarz F. Samavati", title = "Capturing and Re-Using Artistic Styles with Reverse Subdivision-Based Multiresolution Methods", journal = j-INT-J-IMAGE-GRAPHICS, volume = "7", number = "4", pages = "593--615", month = oct, year = "2007", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467807002829", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Curran:2007:APD, author = "Kevin Curran and Neil McCaughley and Xuelong Li", title = "Addressing the Problems of Detecting Faces with Neural Networks", journal = j-INT-J-IMAGE-GRAPHICS, volume = "7", number = "4", pages = "617--640", month = oct, year = "2007", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467807002830", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Park:2007:FMO, author = "Chan Jong Park and Kwang Yun Wohn", title = "Fusion of the Magnetic and Optical Information for Motion Capturing", journal = j-INT-J-IMAGE-GRAPHICS, volume = "7", number = "4", pages = "641--662", month = oct, year = "2007", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467807002842", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Khare:2007:DCW, author = "Ashish Khare and Uma Shanker Tiwary", title = "{Daubechies} Complex Wavelet Transform Based Technique for Denoising of Medical Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "7", number = "4", pages = "663--687", month = oct, year = "2007", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467807002854", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Bougleux:2007:SDS, author = "S{\'e}bastien Bougleux and Mahmoud Melkemi and Abderrahim Elmoataz", title = "Structure Detection from a {$3$D} Set of Points with Anisotropic Alpha-Shapes", journal = j-INT-J-IMAGE-GRAPHICS, volume = "7", number = "4", pages = "689--708", month = oct, year = "2007", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467807002866", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Ablameyko:2007:RED, author = "Sergey V. Ablameyko and Seiichi Uchida", title = "Recognition of Engineering Drawing Entities: Review of Approaches", journal = j-INT-J-IMAGE-GRAPHICS, volume = "7", number = "4", pages = "709--733", month = oct, year = "2007", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467807002878", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Hosny:2007:ECL, author = "Khalid M. Hosny", title = "Efficient Computation of {Legendre} Moments for Gray Level Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "7", number = "4", pages = "735--747", month = oct, year = "2007", CODEN = "????", DOI = "https://doi.org/10.1142/S021946780700288X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Sanchez:2007:CFT, author = "Danmary Sanchez and Malek Adjouadi and Nolan R. Altman and Daniel Sanchez and Byron Bernal", title = "Comprehensive {$3$D} Fiber Tracking As a New Visualization System in Brain Studies", journal = j-INT-J-IMAGE-GRAPHICS, volume = "7", number = "4", pages = "749--765", month = oct, year = "2007", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467807002891", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Li:2007:KGR, author = "Jing Li and Yuan Yuan", title = "Kernel {GBDA} for Relevance Feedback in Image Retrieval", journal = j-INT-J-IMAGE-GRAPHICS, volume = "7", number = "4", pages = "767--776", month = oct, year = "2007", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467807002908", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Zhang:2007:LGB, author = "Wenchao Zhang and Shiguang Shan and Xilin Chen and Wen Gao", title = "Local {Gabor} Binary Patterns Based on Mutual Information for Face Recognition", journal = j-INT-J-IMAGE-GRAPHICS, volume = "7", number = "4", pages = "777--793", month = oct, year = "2007", CODEN = "????", DOI = "https://doi.org/10.1142/S021946780700291X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Anonymous:2007:AIV, author = "Anonymous", title = "Author Index (Volume 7)", journal = j-INT-J-IMAGE-GRAPHICS, volume = "7", number = "4", pages = "795--797", month = oct, year = "2007", CODEN = "????", DOI = "https://doi.org/10.1142/S021946780700291X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Feng:2008:TFM, author = "Guiyu Feng and David Zhang and Jian Yang and Dewen Hu", title = "A Theoretical Framework for Matrix-Based Feature Extraction Algorithms with Its Application to Image Recognition", journal = j-INT-J-IMAGE-GRAPHICS, volume = "8", number = "1", pages = "1--23", month = jan, year = "2008", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467808002940", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Lu:2008:NRM, author = "Jianming Lu and Ling Wang and Yeqiu Li and Takashi Yahagi", title = "Noise Removal for Medical {X}-Ray Images in Multiwavelet Domain", journal = j-INT-J-IMAGE-GRAPHICS, volume = "8", number = "1", pages = "25--46", month = jan, year = "2008", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467808002952", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Manjunath:2008:DSD, author = "A. V. N. Manjunath and K. G. Hemantha and S. Noushath", title = "Document Skew Detection --- a Novel Approach", journal = j-INT-J-IMAGE-GRAPHICS, volume = "8", number = "1", pages = "47--59", month = jan, year = "2008", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467808002964", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Yang:2008:VBT, author = "Shiueng-Bien Yang", title = "Variable-Branch Tree-Structured Residual Vector Quantization", journal = j-INT-J-IMAGE-GRAPHICS, volume = "8", number = "1", pages = "61--80", month = jan, year = "2008", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467808002976", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Courty:2008:ANR, author = "Nicolas Courty and Pierre Hellier", title = "Accelerating {$3$D} Non-Rigid Registration Using Graphics Hardware", journal = j-INT-J-IMAGE-GRAPHICS, volume = "8", number = "1", pages = "81--98", month = jan, year = "2008", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467808002988", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Zou:2008:RCM, author = "Jie Zou", title = "Rose Curve Model and an Analytical Solution for Estimating Its Parameters", journal = j-INT-J-IMAGE-GRAPHICS, volume = "8", number = "1", pages = "99--108", month = jan, year = "2008", CODEN = "????", DOI = "https://doi.org/10.1142/S021946780800299X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Ho:2008:RSI, author = "Charlotte Yuk-Fan Ho and Tai-Chiu Hsung and Daniel Pak-Kong Lun and Bingo Wing-Kuen Ling and Peter Kwong-Shun Tam and Wan-Chi Siu", title = "Regularity Scalable Image Coding Based on Wavelet Singularity Detection", journal = j-INT-J-IMAGE-GRAPHICS, volume = "8", number = "1", pages = "109--134", month = jan, year = "2008", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467808003003", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Fabrizio:2008:ASP, author = "Jonathan Fabrizio and Jean Devars", title = "An Analytical Solution to the Perspective-{$n$}-Point Problem for Common Planar Camera and for Catadioptric Sensor", journal = j-INT-J-IMAGE-GRAPHICS, volume = "8", number = "1", pages = "135--155", month = jan, year = "2008", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467808003015", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Chang:2008:LFE, author = "Kuan-Tsung Chang and Tian-Yuan Shih", title = "Linear Features Extraction with an Orientation Constrained Probabilistic {Hough} Transform", journal = j-INT-J-IMAGE-GRAPHICS, volume = "8", number = "1", pages = "157--168", month = jan, year = "2008", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467808003027", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Didier:2008:HCP, author = "Jean-Yves Didier and Fakhr-Eddine Ababsa and Malik Mallem", title = "Hybrid Camera Pose Estimation Combining Square Fiducials Localization Technique and Orthogonal Iteration Algorithm", journal = j-INT-J-IMAGE-GRAPHICS, volume = "8", number = "1", pages = "169--188", month = jan, year = "2008", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467808003039", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:01 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Ge:2008:PBA, author = "Jinghua Ge and Daniel J. Sandin and Tom Peterka and Robert Kooima and Javier I. Girado and Andrew Johnson", title = "A Point-Based Asynchronous Remote Visualization Framework for Real-Time Virtual Reality", journal = j-INT-J-IMAGE-GRAPHICS, volume = "8", number = "2", pages = "189--207", month = apr, year = "2008", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467808003040", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Peng:2008:RNP, author = "Haoyu Peng and Hua Xiong and Zhen Liu and Jiaoying Shi", title = "Research of Nested Parallel Pipelines on Parallel Graphics Rendering System", journal = j-INT-J-IMAGE-GRAPHICS, volume = "8", number = "2", pages = "209--222", month = apr, year = "2008", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467808003052", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Zhang:2008:HAP, author = "Fan Zhang and Hanqiu Sun and Leilei Xu and Kitlun Lee", title = "Hardware-Accelerated Parallel-Split Shadow Maps", journal = j-INT-J-IMAGE-GRAPHICS, volume = "8", number = "2", pages = "223--241", month = apr, year = "2008", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467808003064", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Ahlborn:2008:DIF, author = "Benjamin A. Ahlborn and Oliver Kreylos and Sohail Shafii and Bernd Hamann and Oliver G. Staadt", title = "Design and Implementation of a Foveal Projection Display", journal = j-INT-J-IMAGE-GRAPHICS, volume = "8", number = "2", pages = "243--263", month = apr, year = "2008", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467808003076", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Zhong:2008:RDB, author = "Yongmin Zhong and Bijan Shirinzadeh and Julian Smith", title = "Reaction-Diffusion Based Deformable Object Simulation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "8", number = "2", pages = "265--280", month = apr, year = "2008", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467808003088", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Baciu:2008:GSW, author = "George Baciu and Liang Ma and Jinlian Hu", title = "Generating Seams and Wrinkles for Virtual Clothing", journal = j-INT-J-IMAGE-GRAPHICS, volume = "8", number = "2", pages = "281--297", month = apr, year = "2008", CODEN = "????", DOI = "https://doi.org/10.1142/S021946780800309X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Scherff:2008:IPI, author = "Phillip-Christoph Scherff and George Baciu and Jinlian Hu", title = "Intuitive Parameterized Input Interface for Proportional Reshaping of Human Bodies", journal = j-INT-J-IMAGE-GRAPHICS, volume = "8", number = "2", pages = "299--325", month = apr, year = "2008", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467808003106", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Anonymous:2008:P, author = "Anonymous", title = "Preface", journal = j-INT-J-IMAGE-GRAPHICS, volume = "8", number = "2", pages = "vii--vii", month = apr, year = "2008", CODEN = "????", DOI = "", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Leung:2008:UID, author = "Man-Kang Leung and Chi-Wing Fu", title = "A User Interface Design for Acquiring Statistics from Video", journal = j-INT-J-IMAGE-GRAPHICS, volume = "8", number = "3", pages = "327--349", month = jul, year = "2008", CODEN = "????", DOI = "https://doi.org/10.1142/S021946780800312X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Agarwal:2008:DWS, author = "Rashmi Agarwal and M. S. Santhanam", title = "Digital Watermarking in the Singular Vector Domain", journal = j-INT-J-IMAGE-GRAPHICS, volume = "8", number = "3", pages = "351--368", month = jul, year = "2008", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467808003131", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Yang:2008:VTA, author = "Tao Yang and Jing Li and Quan Pan and Yong-Mei Cheng", title = "Visual Tracking with Automatic Confident Region Extraction", journal = j-INT-J-IMAGE-GRAPHICS, volume = "8", number = "3", pages = "369--381", month = jul, year = "2008", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467808003143", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Chan:2008:VBG, author = "K. L. Chan", title = "Video-Based Gait Analysis by Silhouette {Chamfer} Distance and {Kalman} Filter", journal = j-INT-J-IMAGE-GRAPHICS, volume = "8", number = "3", pages = "383--418", month = jul, year = "2008", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467808003155", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Yau:2008:VSR, author = "Wai Chee Yau and Dinesh Kant Kumar and Sridhar Poosapadi Arjunan", title = "Visual Speech Recognition Using Dynamic Features and Support Vector Machines", journal = j-INT-J-IMAGE-GRAPHICS, volume = "8", number = "3", pages = "419--437", month = jul, year = "2008", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467808003167", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Sun:2008:BWN, author = "Shusen Sun and Zhigeng Pan and Tae-Wan Kim", title = "Blind Watermarking of Non-Uniform {B}-Spline Surfaces", journal = j-INT-J-IMAGE-GRAPHICS, volume = "8", number = "3", pages = "439--454", month = jul, year = "2008", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467808003179", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Snidaro:2008:THM, author = "Lauro Snidaro and Gian Luca Foresti and Luca Chittaro", title = "Tracking Human Motion from Monocular Sequences", journal = j-INT-J-IMAGE-GRAPHICS, volume = "8", number = "3", pages = "455--471", month = jul, year = "2008", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467808003180", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Lezoray:2008:GBO, author = "O. Lezoray and C. Meurie and A. Elmoataz", title = "Graph-Based Ordering Scheme for Color Image Filtering", journal = j-INT-J-IMAGE-GRAPHICS, volume = "8", number = "3", pages = "473--493", month = jul, year = "2008", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467808003192", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Coli:2008:FSR, author = "Pietro Coli and Gian Luca Marcialis and Fabio Roli", title = "Fingerprint Silicon Replicas: Static and Dynamic Features for Vitality Detection Using an Optical Capture Device", journal = j-INT-J-IMAGE-GRAPHICS, volume = "8", number = "4", pages = "495--512", month = oct, year = "2008", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467808003209", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Hussain:2008:WBE, author = "Muhammad Hussain and Turghunjan Abdukirim and Yoshihiro Okada", title = "Wavelet-Based Edge Detection in Digital Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "8", number = "4", pages = "513--533", month = oct, year = "2008", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467808003210", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Zhu:2008:MSS, author = "Dengming Zhu and Zhaoqi Wang and Yingping Zhang", title = "Motion Synthesis from the Semantic Signals", journal = j-INT-J-IMAGE-GRAPHICS, volume = "8", number = "4", pages = "535--550", month = oct, year = "2008", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467808003222", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kampke:2008:AGR, author = "Thomas K{\"a}mpke", title = "Automatic Generation of {$3$D} Radar Display Views", journal = j-INT-J-IMAGE-GRAPHICS, volume = "8", number = "4", pages = "551--572", month = oct, year = "2008", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467808003234", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Thakoor:2008:AVO, author = "Ninad Thakoor and Jean X. Gao", title = "Automatic Video Object Extraction with Camera in Motion", journal = j-INT-J-IMAGE-GRAPHICS, volume = "8", number = "4", pages = "573--600", month = oct, year = "2008", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467808003246", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Xin:2008:IST, author = "Binjie Xin and Jinlian Hu and George Baciu", title = "An Imaging System for Textile Surface Profile Based on Silhouette Image Analysis", journal = j-INT-J-IMAGE-GRAPHICS, volume = "8", number = "4", pages = "601--613", month = oct, year = "2008", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467808003258", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Skala:2008:ICP, author = "Vaclav Skala", title = "Intersection Computation in Projective Space Using Homogeneous Coordinates", journal = j-INT-J-IMAGE-GRAPHICS, volume = "8", number = "4", pages = "615--628", month = oct, year = "2008", CODEN = "????", DOI = "https://doi.org/10.1142/S021946780800326X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Ou:2008:LIT, author = "Chien-Min Ou and Hui-Ya Li and Wen-Jyi Hwang and Mei-Hwa Liu", title = "Layered Image Transmission with Quality Pre-Specifiable {JPEG2000}", journal = j-INT-J-IMAGE-GRAPHICS, volume = "8", number = "4", pages = "629--641", month = oct, year = "2008", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467808003271", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Li:2008:NAS, author = "Jing Li and Tao Yang and Quan Pan and Yong-Mei Cheng and Jun Hou", title = "A Novel Algorithm for Speeding Up Keypoint Detection and Matching", journal = j-INT-J-IMAGE-GRAPHICS, volume = "8", number = "4", pages = "643--661", month = oct, year = "2008", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467808003283", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Anonymous:2008:AIV, author = "Anonymous", title = "Author Index (Volume 8)", journal = j-INT-J-IMAGE-GRAPHICS, volume = "8", number = "4", pages = "663--665", month = oct, year = "2008", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467808003283", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Lao:2009:ORA, author = "Yuanwei Lao and Yuan F. Zheng", title = "Optimal Rate Allocation for Logo Watermarking", journal = j-INT-J-IMAGE-GRAPHICS, volume = "9", number = "1", pages = "1--25", month = jan, year = "2009", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467809003319", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Porta:2009:NVM, author = "Marco Porta", title = "New Visualization Modes for Effective Image Presentation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "9", number = "1", pages = "27--49", month = jan, year = "2009", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467809003320", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Chen:2009:IRB, author = "Huawei Chen and Ichiro Hagiwara and A. Kiet Tieu", title = "Image Reconstruction Based on Combination of Wavelet Decomposition, Inpainting and Texture Synthesis", journal = j-INT-J-IMAGE-GRAPHICS, volume = "9", number = "1", pages = "51--65", month = jan, year = "2009", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467809003332", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Vasuki:2009:EAC, author = "S. Vasuki and L. Ganesan", title = "An Efficient Approach to Color Image Segmentation Using Intermediate Features of Maximum Overlap Wavelet Transform in Peak Finding Algorithm", journal = j-INT-J-IMAGE-GRAPHICS, volume = "9", number = "1", pages = "67--76", month = jan, year = "2009", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467809003344", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Arivazhagan:2009:TCU, author = "S. Arivazhagan and L. Ganesan", title = "Texture Characterization Using {WSFS} and {WCFS}", journal = j-INT-J-IMAGE-GRAPHICS, volume = "9", number = "1", pages = "77--100", month = jan, year = "2009", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467809003356", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Miyamoto:2009:FAM, author = "Kentaro Miyamoto and Tetsuo Kamina and Tetsuo Sugiyama and Keisuke Kameyama and Kazuo Toraichi and Yasuhiro Ohmiya", title = "A Function Approximation Method for Images with Grading Regions", journal = j-INT-J-IMAGE-GRAPHICS, volume = "9", number = "1", pages = "101--119", month = jan, year = "2009", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467809003307", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Zongqing:2009:NFB, author = "Lu Zongqing and Liao Qingmin and Pei Jihong", title = "A Nonlinear Filtering Based Optical Flow Computation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "9", number = "1", pages = "121--132", month = jan, year = "2009", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467809003368", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Ng:2009:RFM, author = "Ada N. Y. Ng and Edmund Y. Lam and Ronald Chung and Kenneth S. M. Fung and W. H. Leung", title = "Reference-Free Machine Vision Inspection of Semiconductor Die Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "9", number = "1", pages = "133--152", month = jan, year = "2009", CODEN = "????", DOI = "https://doi.org/10.1142/S021946780900337X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Wang:2009:JSN, author = "Xingyuan Wang and Wenjing Song and Lixian Zou", title = "{Julia} Set of the {Newton} Method for Solving Some Complex Exponential Equation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "9", number = "2", pages = "153--169", month = apr, year = "2009", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467809003381", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Nikam:2009:COP, author = "Shankar Bhausaheb Nikam and Suneeta Agarwal", title = "Co-Occurrence Probabilities and Wavelet-Based Spoof Fingerprint Detection", journal = j-INT-J-IMAGE-GRAPHICS, volume = "9", number = "2", pages = "171--199", month = apr, year = "2009", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467809003393", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Zhang:2009:GOM, author = "Shixue Zhang and Enhua Wu", title = "Generation of Optimal Multiresolution Models for Deforming Mesh Sequence", journal = j-INT-J-IMAGE-GRAPHICS, volume = "9", number = "2", pages = "201--215", month = apr, year = "2009", CODEN = "????", DOI = "https://doi.org/10.1142/S021946780900340X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Stylianou:2009:IBF, author = "Georgios Stylianou and Andreas Lanitis", title = "Image Based {$3$D} Face Reconstruction: a Survey", journal = j-INT-J-IMAGE-GRAPHICS, volume = "9", number = "2", pages = "217--250", month = apr, year = "2009", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467809003411", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kumar:2009:UAU, author = "Ajay Kumar and David Zhang", title = "User Authentication Using Fusion of Face and Palmprint", journal = j-INT-J-IMAGE-GRAPHICS, volume = "9", number = "2", pages = "251--270", month = apr, year = "2009", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467809003423", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Goh:2009:TSI, author = "Hock-Ann Goh and Chee-Way Chong and Rosli Besar and Fazly Salleh Abas and Kok-Swee Sim", title = "Translation and Scale Invariants of {Hahn} Moments", journal = j-INT-J-IMAGE-GRAPHICS, volume = "9", number = "2", pages = "271--285", month = apr, year = "2009", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467809003435", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Aiger:2009:GBA, author = "Dror Aiger and Klara Kedem", title = "A {GPU}-Based Algorithm for Approximately Finding the Largest Common Point Set in the Plane Under Similarity Transformation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "9", number = "2", pages = "287--298", month = apr, year = "2009", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467809003459", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Singh:2009:NWM, author = "Vipula Singh and Navin Rajpal and K. Srikanta Murthy", title = "A Neuro-Wavelet Model Using Fuzzy Vector Quantization for Efficient Image Compression", journal = j-INT-J-IMAGE-GRAPHICS, volume = "9", number = "2", pages = "299--320", month = apr, year = "2009", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467809003447", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Jindal:2009:STC, author = "Manish Kumar Jindal and Gurpreet Singh Lehal and Rajendra Kumar Sharma", title = "On Segmentation of Touching Characters and Overlapping Lines in Degraded Printed {Gurmukhi} Script", journal = j-INT-J-IMAGE-GRAPHICS, volume = "9", number = "3", pages = "321--353", month = jul, year = "2009", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467809003460", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Gao:2009:FRS, author = "Xinbo Gao and Jinxiu Li and Bing Xiao", title = "A Face Recognition Scheme Based on Embedded Hidden {Markov} Model and Selective Ensemble Strategy", journal = j-INT-J-IMAGE-GRAPHICS, volume = "9", number = "3", pages = "355--367", month = jul, year = "2009", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467809003472", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Maurer:2009:PVL, author = "Mauricio Rafael Maurer and Helio Pedrini and Marco Antonio Ferreira Randi", title = "Processing and Visualization of Light Microscope Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "9", number = "3", pages = "369--388", month = jul, year = "2009", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467809003484", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kamath:2009:ICS, author = "Chandrika Kamath and Abel Gezahegne and Paul Miller", title = "Identification of Coherent Structures in Three-Dimensional Simulations of a Fluid-Mix Problem", journal = j-INT-J-IMAGE-GRAPHICS, volume = "9", number = "3", pages = "389--410", month = jul, year = "2009", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467809003502", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Seddik:2009:IWB, author = "Hassen Seddik and Mounir Sayadi and Farhat Fnaiech and Mohamed Cheriet", title = "Image Watermarking Based on the {Hessenberg} Transform", journal = j-INT-J-IMAGE-GRAPHICS, volume = "9", number = "3", pages = "411--433", month = jul, year = "2009", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467809003514", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Yang:2009:DEO, author = "Gaobo Yang and Weiwei Chen and Xiao Jing Wang and Zhaoyang Zhang", title = "Dense Estimation of Optical Flow Field Within the {MPEG-2} Compressed Domain", journal = j-INT-J-IMAGE-GRAPHICS, volume = "9", number = "3", pages = "435--448", month = jul, year = "2009", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467809003526", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Bhatnagar:2009:RRW, author = "Gaurav Bhatnagar and Balasubramanian Raman", title = "Robust Reference-Watermarking Scheme Using Wavelet Packet Transform and Bidiagonal-Singular Value Decomposition", journal = j-INT-J-IMAGE-GRAPHICS, volume = "9", number = "3", pages = "449--477", month = jul, year = "2009", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467809003538", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Huang:2009:FPH, author = "Bo Huang and Naimin Li", title = "Fungiform Papillae Hyperplasia ({FPH}) Identification by Tongue Texture Analysis", journal = j-INT-J-IMAGE-GRAPHICS, volume = "9", number = "3", pages = "479--494", month = jul, year = "2009", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467809003496", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Shi:2009:SBI, author = "Weiren Shi and Zuojin Li and Xin Shi and Zhi Zhong", title = "A Survey of Biologically Inspired Image Processing for Objects Recognition", journal = j-INT-J-IMAGE-GRAPHICS, volume = "9", number = "4", pages = "495--510", month = oct, year = "2009", CODEN = "????", DOI = "https://doi.org/10.1142/S021946780900354X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Wong:2009:PPP, author = "Alexander Wong", title = "{PECSI}: a Practical Perceptually-Enhanced Compression Framework for Still Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "9", number = "4", pages = "511--529", month = oct, year = "2009", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467809003551", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Zheng:2009:MTM, author = "Liying Zheng and Kuifeng Liu and Lei Yu", title = "Multilevel Thresholding Method Based on Normalized Cut", journal = j-INT-J-IMAGE-GRAPHICS, volume = "9", number = "4", pages = "531--540", month = oct, year = "2009", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467809003563", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Dinesh:2009:NPA, author = "R. Dinesh and D. S. Guru", title = "Non-Parametric Adaptive Approach for the Detection of Dominant Points on Boundary Curves Based on Non-Symmetric Region of Support", journal = j-INT-J-IMAGE-GRAPHICS, volume = "9", number = "4", pages = "541--557", month = oct, year = "2009", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467809003575", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Vyas:2009:GTI, author = "Vibha S. Vyas and Priti P. Rege", title = "Geometric Transform Invariant Texture Analysis with Modified {Chebyshev} Moments Based Algorithm", journal = j-INT-J-IMAGE-GRAPHICS, volume = "9", number = "4", pages = "559--574", month = oct, year = "2009", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467809003587", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Singh:2009:IIC, author = "Satish Kumar Singh and Shishir Kumar", title = "Improved Image Compression Based on Feed-Forward Adaptive Downsampling Algorithm", journal = j-INT-J-IMAGE-GRAPHICS, volume = "9", number = "4", pages = "575--589", month = oct, year = "2009", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467809003605", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Chang:2009:CSD, author = "Jian Chang and Xiaosong Yang and Jian J. Zhang", title = "Continuous Skeleton-Driven Skinning --- a General Approach For Modeling Skin Deformation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "9", number = "4", pages = "591--608", month = oct, year = "2009", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467809003599", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Wang:2009:MCM, author = "J. Wang and N. V. Patel and W. I. Grosky and F. Fotouhi", title = "Moving Camera Moving Object Segmentation in Compressed Video Sequences", journal = j-INT-J-IMAGE-GRAPHICS, volume = "9", number = "4", pages = "609--627", month = oct, year = "2009", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467809003617", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Anonymous:2009:AIV, author = "Anonymous", title = "Author Index (Volume 9)", journal = j-INT-J-IMAGE-GRAPHICS, volume = "9", number = "4", pages = "629--631", month = oct, year = "2009", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467809003617", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Hossain:2010:VIC, author = "Md. Shafaeat Hossain and Khandaker Abir Rahman and Md. Hasanuzzaman and M. A. Bhuyian and H. Ueno", title = "Video Image Clustering Based on Human Face and Shirt Color", journal = j-INT-J-IMAGE-GRAPHICS, volume = "10", number = "1", pages = "1--19", month = jan, year = "2010", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467810003639", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Heidary:2010:SSD, author = "Kaveh Heidary and H. John Caulfield", title = "Spectral Sensitivity Design for Optical Sensors", journal = j-INT-J-IMAGE-GRAPHICS, volume = "10", number = "1", pages = "21--39", month = jan, year = "2010", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467810003640", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Wang:2010:CSF, author = "Xiaoping Wang and Shenglan Liu and Liyan Zhang", title = "Constructing Surface Features Through Deformation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "10", number = "1", pages = "41--56", month = jan, year = "2010", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467810003652", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Jlassi:2010:DBV, author = "Hejer Jlassi and Kamel Hamrouni", title = "Detection of Blood Vessels in Retinal Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "10", number = "1", pages = "57--72", month = jan, year = "2010", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467810003664", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Liu:2010:EPP, author = "Yong-Jin Liu", title = "On the Evaluation of Progressive Point-Sampled Geometry", journal = j-INT-J-IMAGE-GRAPHICS, volume = "10", number = "1", pages = "73--91", month = jan, year = "2010", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467810003676", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Nigam:2010:FPB, author = "Chhabi Nigam and R. Venkatesh Babu and S. Kumar Raja and K. R. Ramakrishnan", title = "Fragmented Particles-Based Robust Object Tracking with Feature Fusion", journal = j-INT-J-IMAGE-GRAPHICS, volume = "10", number = "1", pages = "93--112", month = jan, year = "2010", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467810003688", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{El-Sattar:2010:NPC, author = "Hussein Karam Hussein Abd El-Sattar", title = "A New Plot\slash Character-Based Interactive System for Story-Based Virtual Reality Applications", journal = j-INT-J-IMAGE-GRAPHICS, volume = "10", number = "1", pages = "113--133", month = jan, year = "2010", CODEN = "????", DOI = "https://doi.org/10.1142/S021946781000369X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Bourouis:2010:SMB, author = "Sami Bourouis and Kamel Hamrouni", title = "{$3$D} Segmentation of {MRI} Brain Using Level Set and Unsupervised Classification", journal = j-INT-J-IMAGE-GRAPHICS, volume = "10", number = "1", pages = "135--154", month = jan, year = "2010", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467810003706", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Ali:2010:VFS, author = "Wajid Ali and Tangui Morvan and Petter Risholm and Ole Jakob Elle and Eigil Samset", title = "A Visualization and Fusion System for Image Guided {RFA} Procedures", journal = j-INT-J-IMAGE-GRAPHICS, volume = "10", number = "2", pages = "155--174", month = apr, year = "2010", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467810003718", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Nesme:2010:AIA, author = "Matthieu Nesme and Fran{\c{c}}ois Faure and Yohan Payan", title = "Accurate Interactive Animation of Deformable Models At Arbitrary Resolution", journal = j-INT-J-IMAGE-GRAPHICS, volume = "10", number = "2", pages = "175--202", month = apr, year = "2010", CODEN = "????", DOI = "https://doi.org/10.1142/S021946781000372X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{DeVisser:2010:DNG, author = "Hans {De Visser} and Josh Passenger and David Conlan and Christoph Russ and David Hellier and Mario Cheng and Oscar Acosta and S{\'e}bastien Ourselin and Olivier Salvado", title = "Developing a Next Generation Colonoscopy Simulator", journal = j-INT-J-IMAGE-GRAPHICS, volume = "10", number = "2", pages = "203--217", month = apr, year = "2010", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467810003731", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Sherstyuk:2010:SAS, author = "Andrei Sherstyuk and Anton Treskunov and Benjamin Berg", title = "Semi-Automatic Surface Scanner for Medical Tangible User Interfaces", journal = j-INT-J-IMAGE-GRAPHICS, volume = "10", number = "2", pages = "219--233", month = apr, year = "2010", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467810003743", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Nguyen:2010:TBR, author = "Van-Hanh Nguyen and Frederic Merienne and Jean-Luc Martinez", title = "Training Based on Real-Time Motion Evaluation for Functional Rehabilitation in Virtual Environment", journal = j-INT-J-IMAGE-GRAPHICS, volume = "10", number = "2", pages = "235--250", month = apr, year = "2010", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467810003755", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kaur:2010:HED, author = "Amandeep Kaur and Chandan Singh", title = "A Hybrid Edge Detector Using Fuzzy Logic and Mathematical Morphology", journal = j-INT-J-IMAGE-GRAPHICS, volume = "10", number = "2", pages = "251--272", month = apr, year = "2010", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467810003767", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Yang:2010:DTC, author = "Zhaohui Yang and Naimin Li", title = "Detection of Tongue Crack Based on Distant Gradient and Prior Knowledge", journal = j-INT-J-IMAGE-GRAPHICS, volume = "10", number = "2", pages = "273--288", month = apr, year = "2010", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467810003779", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Wu:2010:SBM, author = "Jie Wu and Jiabi Chen and Xuelong Zhang and Jinghai Chen", title = "The Segmentation of Brain {MR} Images Using Reformative Expectation--Maximization Algorithm", journal = j-INT-J-IMAGE-GRAPHICS, volume = "10", number = "2", pages = "289--297", month = apr, year = "2010", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467810003780", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Aouat:2010:MDN, author = "Saliha Aouat and Slimane Larabi", title = "Matching Descriptors of Noisy Outline Shapes", journal = j-INT-J-IMAGE-GRAPHICS, volume = "10", number = "3", pages = "299--325", month = jul, year = "2010", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467810003792", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Karthigaikumar:2010:PPV, author = "P. Karthigaikumar and K. Baskaran", title = "Partially Pipelined {VLSI} Implementation of {Blowfish} Encryption\slash Decryption Algorithm", journal = j-INT-J-IMAGE-GRAPHICS, volume = "10", number = "3", pages = "327--341", month = jul, year = "2010", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467810003809", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Soderstrom:2010:RBH, author = "Ulrik S{\"o}derstr{\"o}m and Haibo Li", title = "Representation Bound for Human Facial Mimic with the Aid of Principal Component Analysis", journal = j-INT-J-IMAGE-GRAPHICS, volume = "10", number = "3", pages = "343--363", month = jul, year = "2010", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467810003810", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Gowda:2010:ANI, author = "Rahul Gowda and Shalin M. Mehta and Yue Yang and Baoxin Li", title = "Adaptive Nonlinear Image Enhancement of {Gaussian} Degraded Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "10", number = "3", pages = "365--393", month = jul, year = "2010", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467810003822", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Matungka:2010:EII, author = "Rittavee Matungka and Yuan F. Zheng and Robert L. Ewing", title = "Efficient Invariant Image Registration Utilizing Pre-Shifted Logarithmic Spiral", journal = j-INT-J-IMAGE-GRAPHICS, volume = "10", number = "3", pages = "395--421", month = jul, year = "2010", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467810003834", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Dinesh:2010:CTS, author = "R. Dinesh and D. S. Guru", title = "Concept of Triangular Spatial Relationship and {B}-Tree for Partially Occluded Object Recognition: an Efficient and Robust Approach", journal = j-INT-J-IMAGE-GRAPHICS, volume = "10", number = "3", pages = "423--448", month = jul, year = "2010", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467810003846", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Bai:2010:HPT, author = "Xiaoliang Bai and Shusheng Zhang", title = "Hierarchical Parameterization of Triangular Mesh with a Boundary Polygon Triangulation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "10", number = "3", pages = "449--466", month = jul, year = "2010", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467810003858", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Miyazaki:2010:CDM, author = "Ryuji Miyazaki and Koichi Harada", title = "Creating the Displacement Mapped Low-Level Mesh and Its Application for {CG} Software", journal = j-INT-J-IMAGE-GRAPHICS, volume = "10", number = "3", pages = "467--480", month = jul, year = "2010", CODEN = "????", DOI = "https://doi.org/10.1142/S021946781000386X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 31 08:38:02 MDT 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Treuillet:2010:OIV, author = "Sylvie Treuillet and Eric Royer", title = "Outdoor\slash Indoor Vision-Based Localization for Blind Pedestrian Navigation Assistance", journal = j-INT-J-IMAGE-GRAPHICS, volume = "10", number = "4", pages = "481--496", month = oct, year = "2010", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467810003937", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Dec 9 21:06:32 MST 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Jovanova:2010:OAS, author = "Blagica Jovanova and Ivica Arsov and Marius Preda and Fran{\c{c}}oise Preteux", title = "Online Animation System for Practicing Cued Speech", journal = j-INT-J-IMAGE-GRAPHICS, volume = "10", number = "4", pages = "497--512", month = oct, year = "2010", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467810003925", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Dec 9 21:06:32 MST 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Durette:2010:SRT, author = "Barth{\'e}l{\'e}my Durette and Jeanny H{\'e}rault and David Alleysson", title = "Simulation of the Retina: a Tool for Visual Prostheses", journal = j-INT-J-IMAGE-GRAPHICS, volume = "10", number = "4", pages = "513--529", month = oct, year = "2010", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467810003949", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Dec 9 21:06:32 MST 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Dramas:2010:AVB, author = "Florian Dramas and Simon J. Thorpe and Christophe Jouffrais", title = "Artificial Vision for the Blind: a Bio-Inspired Algorithm for Objects and Obstacles Detection", journal = j-INT-J-IMAGE-GRAPHICS, volume = "10", number = "4", pages = "531--544", month = oct, year = "2010", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467810003871", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Dec 9 21:06:32 MST 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Pissaloux:2010:IMO, author = "Edwige Pissaloux and Yong Chen and Ramiro Velazquez", title = "Image Matching Optimization Via Vision and Inertial Data Fusion: Application to Navigation of the Visually Impaired", journal = j-INT-J-IMAGE-GRAPHICS, volume = "10", number = "4", pages = "545--558", month = oct, year = "2010", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467810003913", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Dec 9 21:06:32 MST 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kulkarni:2010:ITU, author = "Shivali D. Kulkarni and Ameya K. Naik and Nitin S. Nagori", title = "{$2$D} Image Transmission Using Bandwidth Efficient Mapping Technique", journal = j-INT-J-IMAGE-GRAPHICS, volume = "10", number = "4", pages = "559--573", month = oct, year = "2010", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467810003883", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Dec 9 21:06:32 MST 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Nassar:2010:NFE, author = "Hamed Nassar and Ghada El-Taweel and Eman Mahmoud", title = "A Novel Feature Extraction Scheme for Human Gait Recognition", journal = j-INT-J-IMAGE-GRAPHICS, volume = "10", number = "4", pages = "575--587", month = oct, year = "2010", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467810003895", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Dec 9 21:06:32 MST 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Dharwadkar:2010:SSG, author = "Nagaraj V. Dharwadkar and B. B. Amberker", title = "Steganographic Scheme for Gray-Level Image Using Pixel Neighborhood and {LSB} Substitution", journal = j-INT-J-IMAGE-GRAPHICS, volume = "10", number = "4", pages = "589--607", month = oct, year = "2010", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467810003901", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Dec 9 21:06:32 MST 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Anonymous:2010:AIV, author = "Anonymous", title = "Author Index (Volume 10)", journal = j-INT-J-IMAGE-GRAPHICS, volume = "10", number = "4", pages = "609--611", month = oct, year = "2010", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467810003901", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Dec 9 21:06:32 MST 2010", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Nguyen:2011:OTV, author = "Thanh Binh Nguyen and Ashish Khare", title = "Object Tracking of Video Sequences in Curvelet Domain", journal = j-INT-J-IMAGE-GRAPHICS, volume = "11", number = "1", pages = "1--20", month = jan, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467811003968", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Mar 8 10:11:09 MST 2011", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Suraj:2011:RPC, author = "M. G. Suraj and D. S. Guru and S. Manjunath", title = "Recognition of Postal Codes from Fingerspelling Video Sequence", journal = j-INT-J-IMAGE-GRAPHICS, volume = "11", number = "1", pages = "21--41", month = jan, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1142/S021946781100397X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Mar 8 10:11:09 MST 2011", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Abbadeni:2011:TPI, author = "Noureddine Abbadeni and Haikel S. Alhichri and Alaa B. Elmasry", title = "Tackling the Problem of Invariant Texture Retrieval Using Multiple Strategies", journal = j-INT-J-IMAGE-GRAPHICS, volume = "11", number = "1", pages = "43--64", month = jan, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467811003981", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Mar 8 10:11:09 MST 2011", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Yang:2011:MHC, author = "Wenjia Yang and Lihua Dou and Juan Zhan", title = "A Multi-Histogram Clustering Approach Toward {Markov} Random Field for Foreground Segmentation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "11", number = "1", pages = "65--81", month = jan, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467811003993", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Mar 8 10:11:09 MST 2011", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Periasamy:2011:ATB, author = "P. S. Periasamy and S. Athinarayanan and K. Duraiswamy", title = "An Adaptive Thresholding-Based Color Reduction Algorithm and Its Applications", journal = j-INT-J-IMAGE-GRAPHICS, volume = "11", number = "1", pages = "83--101", month = jan, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467811004007", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Mar 8 10:11:09 MST 2011", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kallel:2011:SMM, author = "Mohamed Kallel and Mohamed-Salim Bouhlel and Jean-Christophe Lapayre", title = "Security of the Medical Media Using a Hybrid and Multiple Watermark Technique", journal = j-INT-J-IMAGE-GRAPHICS, volume = "11", number = "1", pages = "103--115", month = jan, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467811004019", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Mar 8 10:11:09 MST 2011", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Boulila:2011:MAS, author = "Wadii Boulila and Imed Riadh Farah", title = "Multi-Approach Satellite Images Fusion Based on Blind Sources Separation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "11", number = "1", pages = "117--136", month = jan, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467811004020", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Mar 8 10:11:09 MST 2011", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Su:2011:ISR, author = "Ya Su and Xinbo Gao", title = "Iterative Shape Refinement in {AAM}", journal = j-INT-J-IMAGE-GRAPHICS, volume = "11", number = "1", pages = "137--151", month = jan, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467811004032", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Mar 8 10:11:09 MST 2011", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Liu:2011:IQF, author = "Haoting Liu and Jie Li and Zheng Wang and Jian Cheng and Hanqing Lu and Yan Zhao", title = "Image Quality Feedback-Based Adaptive Video Definition Improvement for the Space Manipulation Task", journal = j-INT-J-IMAGE-GRAPHICS, volume = "11", number = "2", pages = "153--175", month = apr, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467811004044", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Jul 8 14:32:32 MDT 2011", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Florinabel:2011:MBM, author = "D. Jemi Florinabel and S. Ebenezer Juliet and V. Sadasivam", title = "Multiorientation-Based Multistructure Morphological Inpainting", journal = j-INT-J-IMAGE-GRAPHICS, volume = "11", number = "2", pages = "177--193", month = apr, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467811004056", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Jul 8 14:32:32 MDT 2011", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Wang:2011:QBC, author = "Yuqing Wang and Ming Zhu and Haochen Pang and Yong Wang", title = "Quaternion Based Color Image Quality Assessment Index", journal = j-INT-J-IMAGE-GRAPHICS, volume = "11", number = "2", pages = "195--206", month = apr, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467811004111", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Jul 8 14:32:32 MDT 2011", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Nasrollahi:2011:SSV, author = "Kamal Nasrollahi and Thomas B. Moeslund and Mohammad Rahmati", title = "Summarization of Surveillance Video Sequences Using Face Quality Assessment", journal = j-INT-J-IMAGE-GRAPHICS, volume = "11", number = "2", pages = "207--233", month = apr, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467811004068", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Jul 8 14:32:32 MDT 2011", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Yang:2011:STV, author = "Fuzheng Yang and Shuai Wan", title = "Spatial-Temporal Video Quality Assessment Based on Two-Level Temporal Pooling", journal = j-INT-J-IMAGE-GRAPHICS, volume = "11", number = "2", pages = "235--249", month = apr, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1142/S021946781100407X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Jul 8 14:32:32 MDT 2011", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Yang:2011:IMV, author = "J. X. Yang and D. M. Tan and H. R. Wu", title = "An Impairment Metric for Video Temporal Fluctuation Measure", journal = j-INT-J-IMAGE-GRAPHICS, volume = "11", number = "2", pages = "251--264", month = apr, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467811004081", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Jul 8 14:32:32 MDT 2011", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Deng:2011:IQM, author = "Cheng Deng and Jie Li and Yifan Zhang and Dongyu Huang and Lingling An", title = "An Image Quality Metric Based on Biologically Inspired Feature Model", journal = j-INT-J-IMAGE-GRAPHICS, volume = "11", number = "2", pages = "265--279", month = apr, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467811004093", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Jul 8 14:32:32 MDT 2011", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Lu:2011:NCI, author = "Wen Lu and Lihuo He and Wenjian Tang and Fei Gao and Weilong Hou", title = "A Novel Compressed Images Quality Metric", journal = j-INT-J-IMAGE-GRAPHICS, volume = "11", number = "2", pages = "281--292", month = apr, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1142/S021946781100410X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Jul 8 14:32:32 MDT 2011", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Urolagin:2011:KAR, author = "Siddhaling Urolagin and K. V. Prema and N. V. Subba Reddy", title = "{Kannada} Alphabets Recognition with Application to {Braille} Translation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "11", number = "3", pages = "293--314", month = jul, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467811004159", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 24 06:48:16 MDT 2011", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{He:2011:SBN, author = "Liqiang He and Guangyong Zhang and Yanyan Zhang", title = "Speeding Up Best Neighborhood Matching Algorithm for High-Definition Image on {GPU} {Platform}", journal = j-INT-J-IMAGE-GRAPHICS, volume = "11", number = "3", pages = "315--337", month = jul, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467811004196", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 24 06:48:16 MDT 2011", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Wang:2011:NTL, author = "Xing-Yuan Wang and Zhi-Feng Chen and Jiao-Jiao Yun", title = "A Novel Two-Level Color Image Retrieval Method", journal = j-INT-J-IMAGE-GRAPHICS, volume = "11", number = "3", pages = "339--353", month = jul, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467811004184", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 24 06:48:16 MDT 2011", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Bonyadi:2011:NHQ, author = "Mohammad Reza Bonyadi and Mohsen Ebrahimi Moghaddam", title = "A Nonuniform High-Quality Image Compression Method to Preserve User-Specified Compression Ratio", journal = j-INT-J-IMAGE-GRAPHICS, volume = "11", number = "3", pages = "355--375", month = jul, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467811004123", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 24 06:48:16 MDT 2011", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kamath:2011:RES, author = "Chandrika Kamath and Omar A. Hurricane", title = "Robust Extraction of Statistics from Images of Material Fragmentation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "11", number = "3", pages = "377--401", month = jul, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467811004172", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 24 06:48:16 MDT 2011", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Su:2011:BIR, author = "Liyun Su and Ruihua Liu", title = "Blind Image Restoration with Modified {CMA}", journal = j-INT-J-IMAGE-GRAPHICS, volume = "11", number = "3", pages = "403--413", month = jul, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467811004147", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 24 06:48:16 MDT 2011", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Chia:2011:BUI, author = "Wai Chong Chia and Li Wern Chew and Li-Minn Ang and Kah Phooi Seng", title = "Binary-Uncoded Image and Video Compression Using {SPIHT--ZTR} Coding", journal = j-INT-J-IMAGE-GRAPHICS, volume = "11", number = "3", pages = "415--437", month = jul, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467811004135", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 24 06:48:16 MDT 2011", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Alvarez:2011:VLD, author = "Miguel Alvarez and Mar{\'\i}a-Elena Algorri", title = "Vectorization and Line Detection for Automatic Image Recognition", journal = j-INT-J-IMAGE-GRAPHICS, volume = "11", number = "3", pages = "439--470", month = jul, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467811004160", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 24 06:48:16 MDT 2011", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Rawat:2011:CBR, author = "Sanjay Rawat and Balasubramanian Raman", title = "A Chaos-Based Robust Watermarking Algorithm for Rightful Ownership Protection", journal = j-INT-J-IMAGE-GRAPHICS, volume = "11", number = "4", pages = "471--493", month = oct, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467811004263", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Feb 8 18:48:57 MST 2012", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Lu:2011:NTB, author = "Huchuan Lu and Dong Wang and Yen-Wei Chen and Hao Chen", title = "A Novel Texture-Based Multi-Linear Analysis Algorithm for Face Recognition", journal = j-INT-J-IMAGE-GRAPHICS, volume = "11", number = "4", pages = "495--508", month = oct, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1142/S021946781100424X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Feb 8 18:48:57 MST 2012", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Massoptier:2011:CGC, author = "Laurent Massoptier and Avishkar Misra and Arcot Sowmya and Sergio Casciaro", title = "Combining Graph-Cut Technique and Anatomical Knowledge for Automatic Segmentation of Lungs Affected by Diffuse Parenchymal Disease in {HRCT} Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "11", number = "4", pages = "509--529", month = oct, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467811004202", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Feb 8 18:48:57 MST 2012", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kudelski:2011:FLE, author = "Dimitri Kudelski and Sophie Viseur and Giovanni Scrofani and Jean-Luc Mari", title = "Feature Line Extraction on Meshes Through Vertex Marking and {$2$D} Topological Operators", journal = j-INT-J-IMAGE-GRAPHICS, volume = "11", number = "4", pages = "531--548", month = oct, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467811004226", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Feb 8 18:48:57 MST 2012", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Wang:2011:ISC, author = "Wei Wang and Chi-Kit Ronald Chung", title = "Image Segmentation with Complementary Use of Edge and Region Information", journal = j-INT-J-IMAGE-GRAPHICS, volume = "11", number = "4", pages = "549--570", month = oct, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467811004275", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Feb 8 18:48:57 MST 2012", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Schwartz:2011:IFI, author = "William Robson Schwartz and Helio Pedrini", title = "Improved Fractal Image Compression Based on Robust Feature Descriptors", journal = j-INT-J-IMAGE-GRAPHICS, volume = "11", number = "4", pages = "571--587", month = oct, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467811004251", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Feb 8 18:48:57 MST 2012", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Drew:2011:ICR, author = "Mark S. Drew and Graham D. Finlayson", title = "Improvement of Colorization Realism Via the Structure Tensor", journal = j-INT-J-IMAGE-GRAPHICS, volume = "11", number = "4", pages = "589--609", month = oct, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467811004214", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Feb 8 18:48:57 MST 2012", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Balster:2011:PCR, author = "Eric J. Balster and Benjamin T. Fortener and William F. Turri", title = "Post-Compression Rate-Distortion Development for Embedded Block Coding with Optimal Truncation in {JPEG2000} Imagery", journal = j-INT-J-IMAGE-GRAPHICS, volume = "11", number = "4", pages = "611--627", month = oct, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467811004238", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Feb 8 18:48:57 MST 2012", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Anonymous:2011:AIV, author = "Anonymous", title = "Author Index (Volume 11)", journal = j-INT-J-IMAGE-GRAPHICS, volume = "11", number = "4", pages = "629--631", month = oct, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467811004238", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Feb 8 18:48:57 MST 2012", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Helmy:2012:CMC, author = "Tarek Helmy", title = "A Computational Model for Context-Based Image Categorization and Description", journal = j-INT-J-IMAGE-GRAPHICS, volume = "12", number = "1", pages = "1250001", month = jan, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467812500015", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Feb 29 07:59:06 MST 2012", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", pagecount = "19", } @Article{Fu:2012:DRA, author = "Bin Fu and Wenxin Li and Minghui Wu and Rongfeng Li and Zhuoqun Xu", title = "A Document Rectification Approach Dealing with Both Perspective Distortion and Warping Based on Text Flow Curve Fitting", journal = j-INT-J-IMAGE-GRAPHICS, volume = "12", number = "1", pages = "1250002", month = jan, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467812500027", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Feb 29 07:59:06 MST 2012", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", pagecount = "25", } @Article{Prasath:2012:ADS, author = "V. B. Surya Prasath and Arindama Singh", title = "An Adaptive Diffusion Scheme for Image Restoration and Selective Smoothing", journal = j-INT-J-IMAGE-GRAPHICS, volume = "12", number = "1", pages = "1250003", month = jan, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467812500039", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Feb 29 07:59:06 MST 2012", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", pagecount = "18", } @Article{Mukherjee:2012:CIU, author = "Dipti Prasad Mukherjee and Nilanjan Ray", title = "Contour Interpolation Using Level-Set Analysis", journal = j-INT-J-IMAGE-GRAPHICS, volume = "12", number = "1", pages = "1250004", month = jan, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467812500040", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Feb 29 07:59:06 MST 2012", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", pagecount = "16", } @Article{Rani:2012:FRU, author = "J. Sheeba Rani", title = "Face Recognition Using Hybrid Approach", journal = j-INT-J-IMAGE-GRAPHICS, volume = "12", number = "1", pages = "1250005", month = jan, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467812500052", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Feb 29 07:59:06 MST 2012", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", pagecount = "27", } @Article{Lai:2012:FMI, author = "Shuhua Lai and Fuhua (Frank) Cheng", title = "Fast Mesh Interpolation and Mesh Decomposition with Applications", journal = j-INT-J-IMAGE-GRAPHICS, volume = "12", number = "1", pages = "1250006", month = jan, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467812500064", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Feb 29 07:59:06 MST 2012", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", pagecount = "18", } @Article{Li:2012:HIR, author = "Ping Li and Hanqiu Sun and Jianbing Shen and Chen Huang", title = "{HDR} Image Rerendering Using {GPU}-Based Processing", journal = j-INT-J-IMAGE-GRAPHICS, volume = "12", number = "1", pages = "1250007", month = jan, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467812500076", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Feb 29 07:59:06 MST 2012", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", pagecount = "19", } @Article{Suresh:2012:STP, author = "R. M. Suresh and N. Jayalakshmi", title = "Segmentation and Tracking of Progenitor Cells in Time Lapse Microscopy", journal = j-INT-J-IMAGE-GRAPHICS, volume = "12", number = "1", pages = "1250008", month = jan, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467812500088", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Feb 29 07:59:06 MST 2012", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", pagecount = "15", } @Article{Nejla:2012:BIR, author = "Gribaa Nejla and Noblet Vincent and Khlifa Nawres and Faisan Sylvain and Hamrouni Kamel", title = "Binary Image Registration Based on Geometric Moments: Application to the Registration of {$3$D} Segmented {CT} Head Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "12", number = "2", pages = "1250009", month = apr, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1142/S021946781250009X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu May 3 08:15:54 MDT 2012", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", pagecount = "16", } @Article{Kodavalla:2012:DVC, author = "Vijay Kumar Kodavalla and P. G. Krishna Mohan", title = "Distributed Video Coding: Feedback-Free Architecture and Implementation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "12", number = "2", pages = "1250010", month = apr, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467812500106", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu May 3 08:15:54 MDT 2012", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", pagecount = "16", } @Article{Xu:2012:FRB, author = "Gang Xu and Huchuan Lu and Zunyi Wang", title = "Face Recognition Based on {GPPBTF} and {LBP} with Classifier Fusion", journal = j-INT-J-IMAGE-GRAPHICS, volume = "12", number = "2", pages = "1250011", month = apr, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467812500118", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu May 3 08:15:54 MDT 2012", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", pagecount = "16", } @Article{Fan:2012:GIC, author = "N. Fan and Cheng Jin", title = "Geometric Invariants Construction for Semantic Scene Understanding from Multiple Views Inspired by the Human Visual System", journal = j-INT-J-IMAGE-GRAPHICS, volume = "12", number = "2", pages = "1250012", month = apr, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1142/S021946781250012X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu May 3 08:15:54 MDT 2012", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", pagecount = "14", } @Article{Yang:2012:IRB, author = "Shiueng-Bien Yang and Ting-Wen Liang", title = "Image Restoration Based on Smooth Gray-Level Detection and Line Prediction Method for Large Missing Regions", journal = j-INT-J-IMAGE-GRAPHICS, volume = "12", number = "2", pages = "1250013", month = apr, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467812500131", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu May 3 08:15:54 MDT 2012", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", pagecount = "20", } @Article{Feddaoui:2012:IRM, author = "Nadia Feddaoui and Hela Mahersia and Kamel Hamrouni", title = "Iris Recognition Method Based on {Gabor} Filters and Uniform Local Binary Patterns", journal = j-INT-J-IMAGE-GRAPHICS, volume = "12", number = "2", pages = "1250014", month = apr, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467812500143", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu May 3 08:15:54 MDT 2012", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", pagecount = "20", } @Article{Wang:2012:MIS, author = "Haijun Wang and Ming Liu", title = "Medical Images Segmentation Using Active Contours Driven by Global and Local Image Fitting Energy", journal = j-INT-J-IMAGE-GRAPHICS, volume = "12", number = "2", pages = "1250015", month = apr, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467812500155", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu May 3 08:15:54 MDT 2012", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", pagecount = "15", } @Article{Santosh:2012:RPS, author = "K. C. Santosh and Cholwich Nattee and Bart Lamiroy", title = "Relative Positioning of Stroke-Based Clustering: a New Approach to Online Handwritten {Devanagari} Character Recognition", journal = j-INT-J-IMAGE-GRAPHICS, volume = "12", number = "2", pages = "1250016", month = apr, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467812500167", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu May 3 08:15:54 MDT 2012", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", pagecount = "25", } @Article{Zhi:2012:IIA, author = "Zhanjiang Zhi and Yi Sun", title = "An Image Inpainting Algorithm Based on Energy Minimization", journal = j-INT-J-IMAGE-GRAPHICS, volume = "12", number = "3", pages = "1250017", month = jul, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467812500179", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Nov 3 13:35:52 MDT 2012", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Li:2012:HBS, author = "Shifeng Li and Meng Yao and Huchuan Lu", title = "Human Body Segmentation in a Static Image with On-Line {AdaBoost} at Multiscale Superpixels", journal = j-INT-J-IMAGE-GRAPHICS, volume = "12", number = "3", pages = "1250018", month = jul, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467812500180", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Nov 3 13:35:52 MDT 2012", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Zhang:2012:ICB, author = "Lihe Zhang and Zhenzhen Liu", title = "Image Cosegmentation Based on Local and Global Level Set Methods", journal = j-INT-J-IMAGE-GRAPHICS, volume = "12", number = "3", pages = "1250019", month = jul, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467812500192", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Nov 3 13:35:52 MDT 2012", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Jiang:2012:RTO, author = "Ming-Xin Jiang and Zhi-Jing Shao and Hong-Yu Wang", title = "Real-Time Object Tracking Algorithm with Cameras Mounted on Moving Platforms", journal = j-INT-J-IMAGE-GRAPHICS, volume = "12", number = "3", pages = "1250020", month = jul, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467812500209", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Nov 3 13:35:52 MDT 2012", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Wang:2012:TTC, author = "Dong Wang and Gang Yang and Huchuan Lu", title = "Tri-Tracking: Combining Three Independent Views for Robust Visual Tracking", journal = j-INT-J-IMAGE-GRAPHICS, volume = "12", number = "3", pages = "1250021", month = jul, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467812500210", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Nov 3 13:35:52 MDT 2012", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Mahjoub:2012:MOR, author = "Mohamed Ali Mahjoub and Malek Abbassi", title = "{$3$D} Mesh Object Retrieval by Discrete and Continuous Hidden {Markov} Models", journal = j-INT-J-IMAGE-GRAPHICS, volume = "12", number = "4", pages = "1250022", month = oct, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467812500222", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon May 6 16:27:00 MDT 2013", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Wang:2012:IED, author = "Xingyuan Wang and Zhifeng Chen and Xuemei Bao", title = "An Improved Edge-Directed Image Interpolation Algorithm", journal = j-INT-J-IMAGE-GRAPHICS, volume = "12", number = "4", pages = "1250023", month = oct, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467812500234", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon May 6 16:27:00 MDT 2013", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Pal:2012:CAS, author = "Shyamosree Pal and Rahul Dutta and Partha Bhowmick", title = "Circular Arc Segmentation by Curvature Estimation and Geometric Validation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "12", number = "4", pages = "1250024", month = oct, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467812500246", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon May 6 16:27:00 MDT 2013", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Lee:2012:EGC, author = "Jong Kwan Lee and Timothy S. Newman", title = "Exploring {GPU}- and Cluster-Based Improvements for Over-Sampled Volume Ray Casting Opacity Correction", journal = j-INT-J-IMAGE-GRAPHICS, volume = "12", number = "4", pages = "1250025", month = oct, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467812500258", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon May 6 16:27:00 MDT 2013", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Takimoto:2012:ICP, author = "Hironori Takimoto and Seiki Yoshimori and Yasue Mitsukura", title = "Invisible Calibration Pattern for Print-And-Scan Data Hiding Based on Human Visual Perception", journal = j-INT-J-IMAGE-GRAPHICS, volume = "12", number = "4", pages = "1250026", month = oct, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1142/S021946781250026X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon May 6 16:27:00 MDT 2013", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Yang:2012:LTI, author = "Shiueng-Bien Yang and Chi-Feng Wu", title = "Locating Text in Images Based on the Smooth Gray-Level Detection", journal = j-INT-J-IMAGE-GRAPHICS, volume = "12", number = "4", pages = "1250027", month = oct, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467812500271", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon May 6 16:27:00 MDT 2013", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Dube:2012:PQT, author = "Mridula Dube and Reenu Sharma", title = "Piecewise Quartic Trigonometric Polynomial {B}-Spline Curves with Two Shape Parameters", journal = j-INT-J-IMAGE-GRAPHICS, volume = "12", number = "4", pages = "1250028", month = oct, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467812500283", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon May 6 16:27:00 MDT 2013", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Slamu:2012:SFE, author = "Wushour Slamu and Juming Cao and Xinhui Yao", title = "Sharp Features Extraction from Point Clouds", journal = j-INT-J-IMAGE-GRAPHICS, volume = "12", number = "4", pages = "1250029", month = oct, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467812500295", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon May 6 16:27:00 MDT 2013", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Anonymous:2012:AIV, author = "Anonymous", title = "{Author Index} (Volume 12)", journal = j-INT-J-IMAGE-GRAPHICS, volume = "12", number = "4", pages = "1299001", month = oct, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1142/S021946781299001X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon May 6 16:27:00 MDT 2013", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Mohanta:2013:NTS, author = "Partha Pratim Mohanta and Sanjoy Kumar Saha and Bhabatosh Chanda", title = "A Novel Technique for Size Constrained Video Storyboard Generation Using Statistical Run Test and Spanning Tree", journal = j-INT-J-IMAGE-GRAPHICS, volume = "13", number = "1", pages = "1350001:1--1350001:24", month = jan, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467813500010", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon May 6 16:27:18 MDT 2013", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Melkemi:2013:PAD, author = "Mahmoud Melkemi and Frederic Cordier and Nickolas S. Sapidis", title = "A Provable Algorithm to Detect Weak Symmetry in a Polygon", journal = j-INT-J-IMAGE-GRAPHICS, volume = "13", number = "1", pages = "1350002:1--1350002:28", month = jan, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467813500022", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon May 6 16:27:18 MDT 2013", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Anagnostopoulos:2013:EFS, author = "Vasileios I. Anagnostopoulos and Emmanuel S. Sardis and Theodora A. Varvarigou", title = "Estimation of Frame Sequence Noise with Removal of {JPEG} Artifacts", journal = j-INT-J-IMAGE-GRAPHICS, volume = "13", number = "1", pages = "1350003:1--1350003:31", month = jan, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467813500034", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon May 6 16:27:18 MDT 2013", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Jha:2013:LEU, author = "Rajib Kumar Jha and Prabir Kumar Biswas and B. N. Chatterji", title = "Logo Extraction Using Combined Discrete Wavelet Transform and Dynamic Stochastic Resonance", journal = j-INT-J-IMAGE-GRAPHICS, volume = "13", number = "1", pages = "1350004:1--1350004:21", month = jan, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467813500046", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon May 6 16:27:18 MDT 2013", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Senapati:2013:LBR, author = "Ranjan K. Senapati and Umesh C. Pati and Kamala K. Mahapatra", title = "Low Bit Rate Image Compression Using Hierarchical Listless Block-Tree {DTT} Algorithm", journal = j-INT-J-IMAGE-GRAPHICS, volume = "13", number = "1", pages = "1350005:1--1350005:23", month = jan, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467813500058", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon May 6 16:27:18 MDT 2013", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Gupta:2013:QMC, author = "Rajani Gupta and Prashant Bansod and R. S. Gamad", title = "Quality Measure of the Compressed Echo, {X}-Ray and {CT} Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "13", number = "1", pages = "1350006:1--1350006:29", month = jan, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1142/S021946781350006X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon May 6 16:27:18 MDT 2013", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Mohideen:2013:RBC, author = "Abubacker Kaja Mohideen and Kuttiannan Thangavel", title = "Region-Based Contrast Enhancement of Digital Mammograms Using an Improved Watershed Segmentation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "13", number = "1", pages = "1350007:1--1350007:25", month = jan, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467813500071", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon May 6 16:27:18 MDT 2013", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Agrawal:2013:P, author = "Anupam Agrawal and R. C. Tripathi and Ellen Yi-Luen Do and M. D. Tiwar", title = "Preface", journal = j-INT-J-IMAGE-GRAPHICS, volume = "13", number = "2", pages = "??--??", month = apr, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467813020014", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 6 10:37:51 MDT 2013", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Rautaray:2013:HGR, author = "Siddharth Swarup Rautaray and Anupam Agrawal", title = "Hand Gesture Recognition Towards Vocabulary and Application Independency", journal = j-INT-J-IMAGE-GRAPHICS, volume = "13", number = "2", pages = "??--??", month = apr, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467813400019", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 6 10:37:51 MDT 2013", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Singh:2013:QBF, author = "Durgesh Singh and Shivendra Shivani and Suneeta Agarwal", title = "Quantization-Based Fragile Watermarking Using Block-Wise Authentication and Pixel-Wise Recovery Scheme for Tampered Image", journal = j-INT-J-IMAGE-GRAPHICS, volume = "13", number = "2", pages = "??--??", month = apr, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467813400020", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 6 10:37:51 MDT 2013", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/cryptography2010.bib; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kumar:2013:GAI, author = "Piyush Kumar and Anupam Agrawal", title = "{GPU}-Accelerated Interactive Visualization of {$ 3 D $} Volumetric Data Using {CUDA}", journal = j-INT-J-IMAGE-GRAPHICS, volume = "13", number = "2", pages = "??--??", month = apr, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467813400032", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 6 10:37:51 MDT 2013", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Bhale:2013:ESF, author = "Aparna Narendra Bhale and Manish Ratnakar Joshi", title = "Enhancement of Screen Film Mammogram Up to a Level of Digital Mammogram: Experimental Analysis", journal = j-INT-J-IMAGE-GRAPHICS, volume = "13", number = "2", pages = "??--??", month = apr, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467813400044", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 6 10:37:51 MDT 2013", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{De:2013:SDO, author = "Kanjar De and V. Masilamani", title = "A Spatial Domain Object Separability Based No-Reference Image Quality Measure Using Mean and Variance", journal = j-INT-J-IMAGE-GRAPHICS, volume = "13", number = "2", pages = "??--??", month = apr, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467813400056", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 6 10:37:51 MDT 2013", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Verma:2013:ISP, author = "Nishchal K. Verma and Shikha Singh", title = "Image Sequence Prediction Using {ANN} and {RBFNN}", journal = j-INT-J-IMAGE-GRAPHICS, volume = "13", number = "2", pages = "??--??", month = apr, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467813400068", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 6 10:37:51 MDT 2013", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", keywords = "artificial neural network (ANN); Canny edge detection-based image comparison metric (CIM); mean structure similarity index measure (MSSIM)", } @Article{Singh:2013:HAI, author = "Pankaj Pratap Singh and R. D. Garg", title = "A Hybrid Approach for Information Extraction from High Resolution Satellite Imagery", journal = j-INT-J-IMAGE-GRAPHICS, volume = "13", number = "2", pages = "??--??", month = apr, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1142/S021946781340007X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 6 10:37:51 MDT 2013", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Nigam:2013:FCI, author = "Akriti Nigam and Ajay Indoria and R. C. Tripathi", title = "Fuzzy Clustering of Image Trademark Database and Preprocessing Using Adaptive Filter and {Karhunen--Lo{\`e}ve} Transform", journal = j-INT-J-IMAGE-GRAPHICS, volume = "13", number = "2", pages = "??--??", month = apr, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467813400081", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 6 10:37:51 MDT 2013", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kuijper:2013:CBM, author = "Arjan Kuijper and Ilkka Havukkala", title = "Comparing Bitmapped {MicroRNA} Structure Images Using Mutual Symmetry", journal = j-INT-J-IMAGE-GRAPHICS, volume = "13", number = "3", pages = "??--??", month = jul, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467813500083", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 28 14:00:38 MDT 2013", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Carvalho:2013:IVF, author = "Paulo Roberto {De Carvalho, Jr.} and Maikon Cismoski {Dos Santos} and William Robson Schwartz and Helio Pedrini", title = "An Improved View Frustum Culling Method Using Octrees for {$3$D} Real-Time Rendering", journal = j-INT-J-IMAGE-GRAPHICS, volume = "13", number = "3", pages = "??--??", month = jul, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467813500095", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 28 14:00:38 MDT 2013", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Jiji:2013:FMP, author = "C. V. Jiji and Ravi Krishnan Unni", title = "Fusion of Multispectral and Panchromatic Images Based on the Nonsubsampled Contourlet Transform", journal = j-INT-J-IMAGE-GRAPHICS, volume = "13", number = "3", pages = "??--??", month = jul, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467813500101", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 28 14:00:38 MDT 2013", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Huang:2013:ADC, author = "Wei Huang and Hongtao Lu", title = "Automatic Defect Classification of {TFT-LCD} Panels with Shape, Histogram and Color Features", journal = j-INT-J-IMAGE-GRAPHICS, volume = "13", number = "3", pages = "??--??", month = jul, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467813500113", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 28 14:00:38 MDT 2013", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{He:2013:RAM, author = "Liwen He and Yong Xu and Yan Chen and Jiajun Wen", title = "Recent Advance on Mean Shift Tracking: a Survey", journal = j-INT-J-IMAGE-GRAPHICS, volume = "13", number = "3", pages = "??--??", month = jul, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467813500125", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 28 14:00:38 MDT 2013", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Maitra:2013:MDE, author = "Indra Kanta Maitra and Sanjay Nag and Samir K. Bandyopadhyay", title = "Mammographic Density Estimation and Classification Using Segmentation and Progressive Elimination Method", journal = j-INT-J-IMAGE-GRAPHICS, volume = "13", number = "3", pages = "??--??", month = jul, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467813500137", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 28 14:00:38 MDT 2013", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Wei:2013:SMO, author = "Jie Wei", title = "Small Moving Object Detection from Infra-Red Sequences", journal = j-INT-J-IMAGE-GRAPHICS, volume = "13", number = "3", pages = "??--??", month = jul, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467813500149", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 28 14:00:38 MDT 2013", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Dorini:2013:SDF, author = "Leyza Baldo Dorini and Neucimar Jer{\^o}nimo Leite", title = "A Self-Dual Filtering Toggle Operator for Speckle Noise Filtering", journal = j-INT-J-IMAGE-GRAPHICS, volume = "13", number = "3", pages = "??--??", month = jul, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467813500150", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Aug 28 14:00:38 MDT 2013", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Mollah:2013:HDB, author = "Ayatullah Faruk Mollah and Subhadip Basu and Mita Nasipuri", title = "Handheld Device-Based Character Recognition System for Camera Captured Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "13", number = "4", pages = "1350016", month = oct, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467813500162", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Jun 6 06:13:32 MDT 2014", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Ray:2013:PAD, author = "Kumar S. Ray and Bimal Kumar Ray", title = "Polygonal Approximation of Digital Curve Based on Reverse Engineering Concept", journal = j-INT-J-IMAGE-GRAPHICS, volume = "13", number = "4", pages = "1350017", month = oct, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467813500174", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Jun 6 06:13:32 MDT 2014", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Liu:2013:ROC, author = "Hao Liu and Guanhua Zhu and Jianning Zhao and Hongbo Qian and Ning Dai", title = "Recognition of Occlusions in {CT} Images Using a Curve-Based Parameterization Method", journal = j-INT-J-IMAGE-GRAPHICS, volume = "13", number = "4", pages = "1350018", month = oct, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467813500186", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Jun 6 06:13:32 MDT 2014", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Wang:2013:VBA, author = "Shuenn-Jyi Wang and Chung-Kai Hsieh and Tsorng-Lin Chia", title = "Video-Based Approach for Detecting Prohibited Activities on Sporting Courts", journal = j-INT-J-IMAGE-GRAPHICS, volume = "13", number = "4", pages = "1350019", month = oct, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467813500198", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Jun 6 06:13:32 MDT 2014", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Haddad:2013:SAC, author = "Bashar Haddad and Amin Jarrah", title = "Semi-Automatic Cracks Correction Based on Seam Processing, Stochastic Analysis and Learning Process", journal = j-INT-J-IMAGE-GRAPHICS, volume = "13", number = "4", pages = "1350020", month = oct, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467813500204", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Jun 6 06:13:32 MDT 2014", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Yang:2013:SHB, author = "Jia Yang and Chee Kooi Chan and Ameersing Luximon", title = "A Survey on {$3$D} Human Body Modeling for Interactive Fashion Design", journal = j-INT-J-IMAGE-GRAPHICS, volume = "13", number = "4", pages = "1350021", month = oct, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467813500216", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Jun 6 06:13:32 MDT 2014", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Dorini:2013:SST, author = "Leyza Baldo Dorini and Neucimar Jer{\^o}nimo Leite", title = "A Scale-Space Toggle Operator for Image Transformations", journal = j-INT-J-IMAGE-GRAPHICS, volume = "13", number = "4", pages = "1350022", month = oct, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467813500228", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Jun 6 06:13:32 MDT 2014", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Anonymous:2013:AIV, author = "Anonymous", title = "Author Index (Volume 13)", journal = j-INT-J-IMAGE-GRAPHICS, volume = "13", number = "4", pages = "1399001", month = oct, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467813990015", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Jun 6 06:13:32 MDT 2014", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Yuan:2014:MGD, author = "Yongfeng Yuan and Kuanquan Wang", title = "A Mixed {Gauss} and Directional Distance Filter for Fiber Direction Tracking", journal = j-INT-J-IMAGE-GRAPHICS, volume = "14", number = "1--2", pages = "1450001", year = "2014", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467814500016", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Jun 6 06:13:40 MDT 2014", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Prakash:2014:MID, author = "Om Prakash and Ashish Khare", title = "Medical Image Denoising Based on Soft Thresholding Using Biorthogonal Multiscale Wavelet Transform", journal = j-INT-J-IMAGE-GRAPHICS, volume = "14", number = "1--2", pages = "1450002", year = "2014", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467814500028", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Jun 6 06:13:40 MDT 2014", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Angadi:2014:RST, author = "S. A. Angadi and M. M. Kodabagi", title = "A Robust Segmentation Technique for Line, Word and Character Extraction from {Kannada} Text in Low Resolution Display Board Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "14", number = "1--2", pages = "1450003", year = "2014", CODEN = "????", DOI = "https://doi.org/10.1142/S021946781450003X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Jun 6 06:13:40 MDT 2014", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Batagelo:2014:GBS, author = "Harlen Costa Batagelo and Jo{\~a}o Paulo Gois", title = "{GPU}-Based Sphere Tracing for Radial Basis Function Implicits", journal = j-INT-J-IMAGE-GRAPHICS, volume = "14", number = "1--2", pages = "1450004", year = "2014", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467814500041", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Jun 6 06:13:40 MDT 2014", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Carvalho:2014:HCS, author = "L. E. Carvalho and S. L. Mantelli Neto and A. von Wangenheim and A. C. Sobieranski and L. Coser and E. Comunello", title = "Hybrid Color Segmentation Method Using a Customized Nonlinear Similarity Function", journal = j-INT-J-IMAGE-GRAPHICS, volume = "14", number = "1--2", pages = "1450005", year = "2014", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467814500053", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Jun 6 06:13:40 MDT 2014", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Jasim:2014:RTC, author = "Mahmood Jasim and Tao Zhang and Md. Hasanuzzaman", title = "A Real-Time Computer Vision-Based Static and Dynamic Hand Gesture Recognition System", journal = j-INT-J-IMAGE-GRAPHICS, volume = "14", number = "1--2", pages = "1450006", year = "2014", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467814500065", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Jun 6 06:13:40 MDT 2014", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Takimoto:2014:FOE, author = "Hironori Takimoto and Hitoshi Yamauchi and Mitsuyoshi Kishihara and Kensuke Okubo", title = "Foreground Object Extraction Based on Interactive Color Saliency Map", journal = j-INT-J-IMAGE-GRAPHICS, volume = "14", number = "1--2", pages = "1450007", year = "2014", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467814500077", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Jun 6 06:13:40 MDT 2014", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Tan:2014:SVI, author = "Cheen-Hau Tan and Lap-Pui Chau", title = "Single Viewpoint Image-Driven Simplification", journal = j-INT-J-IMAGE-GRAPHICS, volume = "14", number = "3", pages = "1450008", month = jul, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467814500089?", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 26 06:23:26 MDT 2014", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Ray:2014:PRB, author = "Kumar S. Ray", title = "Pattern Recognition Based on Fuzzy Set and Genetic Algorithm", journal = j-INT-J-IMAGE-GRAPHICS, volume = "14", number = "3", pages = "1450009", month = jul, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467814500090?", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 26 06:23:26 MDT 2014", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kulkarni:2014:GBM, author = "S. B. Kulkarni and Raghavendrarao B. Kulkarni and U. P. Kulkarni and Ravindra S. Hegadi", title = "{GLCM}-Based Multiclass Iris Recognition Using {FKNN} and {KNN}", journal = j-INT-J-IMAGE-GRAPHICS, volume = "14", number = "3", pages = "1450010", month = jul, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467814500107?", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 26 06:23:26 MDT 2014", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Ali:2014:LDF, author = "Haider Ali and Umair Ullah Tariq and Muhammad Abid", title = "Learning Discriminating Features for Gender Recognition of Real World Faces", journal = j-INT-J-IMAGE-GRAPHICS, volume = "14", number = "3", pages = "1450011", month = jul, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467814500119?", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 26 06:23:26 MDT 2014", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Liu:2014:AIA, author = "Yongmei Liu and Tanakrit Wongwitit and Linsen Yu", title = "Automatic Image Annotation Based on Scene Analysis", journal = j-INT-J-IMAGE-GRAPHICS, volume = "14", number = "3", pages = "1450012", month = jul, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467814500120?", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 26 06:23:26 MDT 2014", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Roy:2014:MSU, author = "Kaushik Roy and Brian O'Connor and Foysal Ahmad and Mohamed S. Kamel", title = "Multibiometric System Using Level Set, Modified {LBP} and Random Forest", journal = j-INT-J-IMAGE-GRAPHICS, volume = "14", number = "3", pages = "1450013", month = jul, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467814500132?", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 26 06:23:26 MDT 2014", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Lin:2014:LBF, author = "Jian Lin and Bo Peng and Tianrui Li", title = "A Learning-Based Framework for Supervised and Unsupervised Image Segmentation Evaluation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "14", number = "3", pages = "1450014", month = jul, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467814500144?", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Aug 26 06:23:26 MDT 2014", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Bareja:2014:IIB, author = "Milan N. Bareja and Chintan K. Modi", title = "An Improved Iterative Back Projection Based Single Image Super Resolution Approach", journal = j-INT-J-IMAGE-GRAPHICS, volume = "14", number = "4", pages = "1450015", month = oct, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467814500156", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Dec 3 09:27:35 MST 2014", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Wei:2014:MEM, author = "Jie Wei", title = "On {Markov Earth Mover}'s Distance", journal = j-INT-J-IMAGE-GRAPHICS, volume = "14", number = "4", pages = "1450016", month = oct, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467814500168", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Dec 3 09:27:35 MST 2014", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Ma:2014:EDV, author = "Ji Ma and David Murphy and Gregory Provan and Cian O'Mathuna and Michael Hayes", title = "The Evaluation of Direct Volume Rendering-Based Uncertainty Visualization Techniques for {$3$D} Scalar Data", journal = j-INT-J-IMAGE-GRAPHICS, volume = "14", number = "4", pages = "1450017", month = oct, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1142/S021946781450017X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Dec 3 09:27:35 MST 2014", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{AlNachar:2014:REB, author = "Rabih {Al Nachar} and Elie Inaty and Patrick J. Bonnin and Yasser Alayli", title = "A Robust Edge-Based Corner Detector {(EBCD)}", journal = j-INT-J-IMAGE-GRAPHICS, volume = "14", number = "4", pages = "1450018", month = oct, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467814500181", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Dec 3 09:27:35 MST 2014", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Jacobsen:2014:IED, author = "C. Robert Jacobsen and Morten Nielsen", title = "Investigation of the Effects of Data Collection on Visual Stylometry", journal = j-INT-J-IMAGE-GRAPHICS, volume = "14", number = "4", pages = "1450019", month = oct, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467814500193", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Dec 3 09:27:35 MST 2014", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Senapati:2014:ILE, author = "Ranjan Kumar Senapati and Prasanth Mankar", title = "Improved Listless Embedded Block Partitioning Algorithms for Image Compression", journal = j-INT-J-IMAGE-GRAPHICS, volume = "14", number = "4", pages = "1450020", month = oct, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1142/S021946781450020X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Dec 3 09:27:35 MST 2014", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/datacompression.bib; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Guo:2014:FLD, author = "Yanyan Guo and Xiangdong Fei and Qijun Zhao", title = "Fingerprint Liveness Detection Using Multiple Static Features and Random Forests", journal = j-INT-J-IMAGE-GRAPHICS, volume = "14", number = "4", pages = "1450021", month = oct, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467814500211", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Dec 3 09:27:35 MST 2014", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Anonymous:2014:AIV, author = "Anonymous", title = "Author Index (Volume 14)", journal = j-INT-J-IMAGE-GRAPHICS, volume = "14", number = "4", pages = "1499001", month = oct, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467814990010", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Dec 3 09:27:35 MST 2014", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Suruliandi:2015:EEG, author = "A. Suruliandi and G. Murugeswari and P. Arockia Jansi Rani", title = "Empirical Evaluation of Generic Weighted Cubicle Pattern and {LBP} Derivatives for Abnormality Detection in Mammogram Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "15", number = "1", pages = "1550001", month = jan, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467815500011", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Apr 15 14:00:35 MDT 2015", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Singh:2015:MOT, author = "Brij Mohan Singh and Rahul Sharma and Debashis Ghosh and Ankush Mittal", title = "Multi-Oriented Text Extraction in Stylistic Documents", journal = j-INT-J-IMAGE-GRAPHICS, volume = "15", number = "1", pages = "1550002", month = jan, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467815500023", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Apr 15 14:00:35 MDT 2015", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{daSilva:2015:EDB, author = "Ricardo Dutra da Silva and Rosane Minghim and Helio Pedrini", title = "{$3$D} Edge Detection Based on {Boolean} Functions and Local Operators", journal = j-INT-J-IMAGE-GRAPHICS, volume = "15", number = "1", pages = "1550003", month = jan, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467815500035", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Apr 15 14:00:35 MDT 2015", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Heickal:2015:CVB, author = "Hasnain Heickal and Tao Zhang and Md. Hasanuzzaman", title = "Computer Vision-Based Real-Time {$3$D} Gesture Recognition Using Depth Image", journal = j-INT-J-IMAGE-GRAPHICS, volume = "15", number = "1", pages = "1550004", month = jan, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467815500047", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Apr 15 14:00:35 MDT 2015", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Keefer:2015:SDI, author = "Robert Keefer and Nikolaos Bourbakis", title = "A Survey on Document Image Processing Methods Useful for Assistive Technology for the Blind", journal = j-INT-J-IMAGE-GRAPHICS, volume = "15", number = "1", pages = "1550005", month = jan, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467815500059", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Apr 15 14:00:35 MDT 2015", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Filisbino:2015:CRM, author = "Tiene A. Filisbino and Gilson A. Giraldi and Carlos E. Thomaz", title = "Comparing Ranking Methods for Tensor Components in Multilinear and Concurrent Subspace Analysis with Applications in Face Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "15", number = "1", pages = "1550006", month = jan, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467815500060", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Apr 15 14:00:35 MDT 2015", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Li:2015:IFB, author = "Shuiwang Li and Qijun Zhao and Xiangdong Fei", title = "An Improved {AM--FM}-Based Approach for Reconstructing Fingerprints from Minutiae", journal = j-INT-J-IMAGE-GRAPHICS, volume = "15", number = "1", pages = "1550007", month = jan, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467815500072", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Apr 15 14:00:35 MDT 2015", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Stando:2015:P, author = "Jacek Stando and Ali Dehghan Tanha and Waralak V. Siricharoen and Yoshiro Imai", title = "Preface", journal = j-INT-J-IMAGE-GRAPHICS, volume = "15", number = "2", pages = "1502001", month = apr, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467815020015", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Apr 15 14:00:43 MDT 2015", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kim:2015:DDA, author = "Yejin Kim and Myunggyu Kim", title = "Data-Driven Approach for Human Locomotion Generation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "15", number = "2", pages = "1540001", month = apr, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1142/S021946781540001X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Apr 15 14:00:43 MDT 2015", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Javadi:2015:ARI, author = "Mohammad Saleh Javadi and Zulaikha Kadim and Hon Hock Woon and Khairunnisa Mohamed Johari and Norshuhada Samudin", title = "An Automatic Robust Image Registration Algorithm for Aerial Mapping", journal = j-INT-J-IMAGE-GRAPHICS, volume = "15", number = "2", pages = "1540002", month = apr, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467815400021", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Apr 15 14:00:43 MDT 2015", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Hashimoto:2015:VCS, author = "Hideyuki Hashimoto and Yuki Fujibayashi and Hiroki Imamura", title = "{$3$D} Video Communication System by Using {Kinect} and Head Mounted Displays", journal = j-INT-J-IMAGE-GRAPHICS, volume = "15", number = "2", pages = "1540003", month = apr, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467815400033", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Apr 15 14:00:43 MDT 2015", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Iwasaki:2015:RRM, author = "Fumiya Iwasaki and Hiroki Imamura", title = "A Robust Recognition Method for Occlusion of Mini Tomatoes Based on Hue Information and the Curvature", journal = j-INT-J-IMAGE-GRAPHICS, volume = "15", number = "2", pages = "1540004", month = apr, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467815400045", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Apr 15 14:00:43 MDT 2015", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Engelbrecht:2015:DVI, author = "Louis Engelbrecht and Adele Botha and Ronell Alberts", title = "Designing the Visualization of Information", journal = j-INT-J-IMAGE-GRAPHICS, volume = "15", number = "2", pages = "1540005", month = apr, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467815400057", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Apr 15 14:00:43 MDT 2015", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Refaey:2015:BRL, author = "Mohammed A. A. Refaey", title = "Background Ruled-Lines Detection and Removal in Full-Colored Handwritten Image Documents", journal = j-INT-J-IMAGE-GRAPHICS, volume = "15", number = "2", pages = "1540006", month = apr, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467815400069", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Apr 15 14:00:43 MDT 2015", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Latha:2015:LFI, author = "Y. L. Malathi Latha and Munaga V. N. K. Prasad and Banoth Sammulal", title = "Local Feature Integration Method Using Phase Congruency for Palm Print Authentication", journal = j-INT-J-IMAGE-GRAPHICS, volume = "15", number = "3", pages = "1550008", month = jul, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467815500084", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Jun 12 10:01:17 MDT 2015", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Syamsuddin:2015:FFB, author = "Muhammad Rusdi Syamsuddin and Jimwook Kim and Sung-Hee Lee", title = "Force Field-Based Control of Dynamic Particles with User-Specified Paths", journal = j-INT-J-IMAGE-GRAPHICS, volume = "15", number = "3", pages = "1550009", month = jul, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467815500096", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Jun 12 10:01:17 MDT 2015", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Liu:2015:HIA, author = "Hao Liu and Hongbo Qian and Ning Dai and Jianning Zhao", title = "Heuristic Initialization for Active Contour Models in {CT\slash MRI} Image Processing", journal = j-INT-J-IMAGE-GRAPHICS, volume = "15", number = "3", pages = "1550010", month = jul, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467815500102", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Jun 12 10:01:17 MDT 2015", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Dehshibi:2015:GPW, author = "Mohammad Mahdi Dehshibi and Ali Shirmohammadi and Andrew Adamatzky", title = "On Growing {Persian} Words with {$L$}-Systems: Visual Modeling of {Neyname}", journal = j-INT-J-IMAGE-GRAPHICS, volume = "15", number = "3", pages = "1550011", month = jul, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467815500114", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Jun 12 10:01:17 MDT 2015", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Shreekanth:2015:HBT, author = "T. Shreekanth and V. Udayashankara", title = "A Histogram-Based Two-Stage Adaptive Character Segmentation for Transcription of Inter-Point {Hindi Braille} to Text", journal = j-INT-J-IMAGE-GRAPHICS, volume = "15", number = "3", pages = "1550012", month = jul, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467815500126", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Jun 12 10:01:17 MDT 2015", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Liu:2015:CMS, author = "Shiguang Liu and Dongfang Fan", title = "Computer Modeling and Simulation of Fruit Sunscald", journal = j-INT-J-IMAGE-GRAPHICS, volume = "15", number = "3", pages = "1550013", month = jul, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467815500138", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Jun 12 10:01:17 MDT 2015", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Sidram:2015:ENS, author = "M. H. Sidram and Nagappa U. Bhajantri", title = "An Exploration with Novel Shape Signature of {GMSC} Distance Function to Track the Object", journal = j-INT-J-IMAGE-GRAPHICS, volume = "15", number = "4", pages = "1550014", month = oct, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1142/S021946781550014X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Feb 26 05:50:06 MST 2016", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Bhattacharjee:2015:CBH, author = "Debjyoti Bhattacharjee and Ashish Bakshi and Kuntal Ghosh", title = "Comparison Between an {HVS} Inspired Linear Filter and the Bilateral Filter in Performing ``Vision at a Glance'' through Smoothing with Edge Preservation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "15", number = "4", pages = "1550015", month = oct, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467815500151", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Feb 26 05:50:06 MST 2016", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Peng:2015:UBT, author = "Chao Peng and Bing Fang and Francis Quek and Yong Cao and Seung In Park and Liguang Xie", title = "Upper Body Tracking and {$3$D} Gesture Reconstruction Using Agent-Based Architecture", journal = j-INT-J-IMAGE-GRAPHICS, volume = "15", number = "4", pages = "1550016", month = oct, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467815500163", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Feb 26 05:50:06 MST 2016", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Angadi:2015:LWT, author = "S. A. Angadi and M. M. Kodabagi", title = "A Light Weight Text Extraction Technique for Hand-Held Device", journal = j-INT-J-IMAGE-GRAPHICS, volume = "15", number = "4", pages = "1550017", month = oct, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467815500175", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Feb 26 05:50:06 MST 2016", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Carvalho:2015:IGB, author = "L. E. Carvalho and S. L. Mantelli Neto and A. C. Sobieranski and E. Comunello and A. von Wangenheim", title = "Improving Graph-Based Image Segmentation Using Nonlinear Color Similarity Metrics", journal = j-INT-J-IMAGE-GRAPHICS, volume = "15", number = "4", pages = "1550018", month = oct, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467815500187", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Feb 26 05:50:06 MST 2016", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Saini:2015:SVB, author = "Deepika Saini and Sanjeev Kumar", title = "Stereo Vision-Based Conic Reconstruction Using a Ray-Quadric Intersection", journal = j-INT-J-IMAGE-GRAPHICS, volume = "15", number = "4", pages = "1550019", month = oct, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467815500199", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Feb 26 05:50:06 MST 2016", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Anonymous:2015:AIV, author = "Anonymous", title = "Author Index (Volume 15)", journal = j-INT-J-IMAGE-GRAPHICS, volume = "15", number = "4", pages = "1599001", month = oct, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467815990016", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Feb 26 05:50:06 MST 2016", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Bandyopadhyay:2016:ASB, author = "Oishila Bandyopadhyay and Bhabatosh Chanda and Bhargab B. Bhattacharya", title = "Automatic Segmentation of Bones in {X}-ray Images Based on Entropy Measure", journal = j-INT-J-IMAGE-GRAPHICS, volume = "16", number = "1", pages = "1650001", month = jan, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467816500017", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Feb 26 05:50:07 MST 2016", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Cai:2016:PRL, author = "Haipeng Cai", title = "Parallel Rendering for Legible Illustrative Visualizations of Dense Geometries on Commodity {CPUs}", journal = j-INT-J-IMAGE-GRAPHICS, volume = "16", number = "1", pages = "1650002", month = jan, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467816500029", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Feb 26 05:50:07 MST 2016", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Yuan:2016:ADM, author = "Jianjun Yuan and Lipei Liu", title = "Anisotropic Diffusion Model Based on a New Diffusion Coefficient and Fractional Order Differential for Image Denoising", journal = j-INT-J-IMAGE-GRAPHICS, volume = "16", number = "1", pages = "1650003", month = jan, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467816500030", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Feb 26 05:50:07 MST 2016", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{McGraw:2016:SNN, author = "Tim McGraw and Jisun Kang and Donald Herring", title = "Sparse Non-Negative Matrix Factorization for Mesh Segmentation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "16", number = "1", pages = "1650004", month = jan, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467816500042", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Feb 26 05:50:07 MST 2016", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Aswatha:2016:IRS, author = "Shashaank M. Aswatha and Jayanta Mukherjee and Partha Bhowmick", title = "An Integrated Repainting System for Digital Restoration of {Vijayanagara} Murals", journal = j-INT-J-IMAGE-GRAPHICS, volume = "16", number = "1", pages = "1650005", month = jan, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467816500054", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Feb 26 05:50:07 MST 2016", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Manimehalai:2016:NRR, author = "P. Manimehalai and P. Arockia Jansi Rani", title = "A New Robust Reversible Blind Watermarking in Wavelet-Domain for Color Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "16", number = "2", pages = "1650006", month = apr, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467816500066", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu May 5 06:44:22 MDT 2016", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Pratihar:2016:FDP, author = "Sanjoy Pratihar and Partha Bhowmick", title = "Fast and Direct Polygonization for Gray-Scale Images Using Digital Straightness and Exponential Averaging", journal = j-INT-J-IMAGE-GRAPHICS, volume = "16", number = "2", pages = "1650007", month = apr, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467816500078", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu May 5 06:44:22 MDT 2016", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Bini:2016:IRU, author = "A. A. Bini and P. Jidesh", title = "Image Restoration Using Adaptive Region-Wise $p$-Norm Filter with Local Constraints", journal = j-INT-J-IMAGE-GRAPHICS, volume = "16", number = "2", pages = "1650008", month = apr, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1142/S021946781650008X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu May 5 06:44:22 MDT 2016", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Grim:2016:ART, author = "Anna Grim and Timothy O'Connor and Peter J. Olver and Chehrzad Shakiban and Ryan Slechta and Robert Thompson", title = "Automatic Reassembly of Three-Dimensional Jigsaw Puzzles", journal = j-INT-J-IMAGE-GRAPHICS, volume = "16", number = "2", pages = "1650009", month = apr, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467816500091", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu May 5 06:44:22 MDT 2016", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Fathimal:2016:SSS, author = "P. Mohamed Fathimal and P. Arockia Jansi Rani", title = "{$K$} out of {$N$} Secret Sharing Scheme for Multiple Color Images with Steganography and Authentication", journal = j-INT-J-IMAGE-GRAPHICS, volume = "16", number = "2", pages = "1650010", month = apr, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467816500108", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu May 5 06:44:22 MDT 2016", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/cryptography2010.bib; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Yuan:2016:PMM, author = "Jianjun Yuan and Jianjun Wang", title = "{Perona--Malik} Model with a New Diffusion Coefficient for Image Denoising", journal = j-INT-J-IMAGE-GRAPHICS, volume = "16", number = "2", pages = "1650011", month = apr, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1142/S021946781650011X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu May 5 06:44:22 MDT 2016", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Tomar:2016:LRP, author = "Divya Tomar and Sonali Agarwal", title = "Leaf Recognition for Plant Classification Using Direct Acyclic Graph Based Multi-Class Least Squares Twin Support Vector Machine", journal = j-INT-J-IMAGE-GRAPHICS, volume = "16", number = "3", pages = "1650012", month = jul, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467816500121", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Nov 16 05:43:38 MST 2016", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Sethi:2016:CAD, author = "Gaurav Sethi and B. S. Saini", title = "Computer Aided Diagnosis of Abdomen Diseases Using Curvelet Transform", journal = j-INT-J-IMAGE-GRAPHICS, volume = "16", number = "3", pages = "1650013", month = jul, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467816500133", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Nov 16 05:43:38 MST 2016", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Ladha:2016:RPC, author = "Shamsuddin N. Ladha and Kate Smith-Miles and Sharat Chandran", title = "Realistic Projection on Casual Dual-Planar Surfaces with Global Illumination Compensation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "16", number = "3", pages = "1650014", month = jul, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467816500145", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Nov 16 05:43:38 MST 2016", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Sowmyayani:2016:ETR, author = "S. Sowmyayani and P. Arockia Jansi Rani", title = "An Efficient Temporal Redundancy Transformation for Wavelet Based Video Compression", journal = j-INT-J-IMAGE-GRAPHICS, volume = "16", number = "3", pages = "1650015", month = jul, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467816500157", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Nov 16 05:43:38 MST 2016", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Gupta:2016:CAA, author = "Pooja Gupta and Kuldip Pahwa", title = "Clock Algorithm Analysis for Increasing Quality of Digital Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "16", number = "3", pages = "1650016", month = jul, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467816500169", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Nov 16 05:43:38 MST 2016", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Caetano:2016:VDU, author = "Felipe Andrade Caetano and Marcelo Bernardes Vieira and Rodrigo Luis de Souza da Silva", title = "A Video Descriptor Using Orientation Tensors and Shape-Based Trajectory Clustering", journal = j-INT-J-IMAGE-GRAPHICS, volume = "16", number = "4", pages = "1650017", month = oct, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467816500170", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 17 05:56:01 MST 2016", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Raja:2016:EMT, author = "S. P. Raja and A. Suruliandi", title = "Evaluating Multiscale Transform Based Image Compression Using Encoding Techniques", journal = j-INT-J-IMAGE-GRAPHICS, volume = "16", number = "4", pages = "1650018", month = oct, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467816500182", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 17 05:56:01 MST 2016", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/datacompression.bib; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{daSilva:2016:GFA, author = "Fl{\'a}vio Altinier Maximiano da Silva and Helio Pedrini", title = "Geometrical Features and Active Appearance Model Applied to Facial Expression Recognition", journal = j-INT-J-IMAGE-GRAPHICS, volume = "16", number = "4", pages = "1650019", month = oct, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467816500194", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 17 05:56:01 MST 2016", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Atiampo:2016:UIS, author = "Armand Kodjo Atiampo and Georges Laussane Loum", title = "Unsupervised Image Segmentation with Pairwise {Markov} Chains Based on Nonparametric Estimation of Copula Using Orthogonal Polynomials", journal = j-INT-J-IMAGE-GRAPHICS, volume = "16", number = "4", pages = "1650020", month = oct, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467816500200", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 17 05:56:01 MST 2016", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Singh:2016:IGZ, author = "Geetika Singh and Indu Chhabra", title = "Integrating Global {Zernike} and Local Discriminative {HOG} Features for Face Recognition", journal = j-INT-J-IMAGE-GRAPHICS, volume = "16", number = "4", pages = "1650021", month = oct, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467816500212", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 17 05:56:01 MST 2016", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Gambhir:2016:NFR, author = "Deepak Gambhir and Meenu Manchanda", title = "A Novel Fusion Rule for Medical Image Fusion in Complex Wavelet Transform Domain", journal = j-INT-J-IMAGE-GRAPHICS, volume = "16", number = "4", pages = "1650022", month = oct, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467816500224", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 17 05:56:01 MST 2016", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Anonymous:2016:AIV, author = "Anonymous", title = "Author Index (Volume 16)", journal = j-INT-J-IMAGE-GRAPHICS, volume = "16", number = "4", pages = "1699001", month = oct, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467816990011", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 17 05:56:01 MST 2016", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Al-Naji:2017:CCA, author = "Ali Al-Naji and Javaan Chahl", title = "Contactless Cardiac Activity Detection Based on Head Motion Magnification", journal = j-INT-J-IMAGE-GRAPHICS, volume = "17", number = "1", pages = "1750001", month = jan, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467817500012", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 26 07:01:04 MST 2017", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kavitha:2017:WBF, author = "J. Kavitha and P. Arockia Jansi Rani and S. Sowmyayani", title = "Wavelet-Based Feature Vector for Shot Boundary Detection", journal = j-INT-J-IMAGE-GRAPHICS, volume = "17", number = "1", pages = "1750002", month = jan, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467817500024", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 26 07:01:04 MST 2017", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kittisuwan:2017:TID, author = "P. Kittisuwan", title = "Textural Image Denoising Using {Gumbel} Random Vectors in {Gaussian} Noise", journal = j-INT-J-IMAGE-GRAPHICS, volume = "17", number = "1", pages = "1750003", month = jan, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467817500036", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 26 07:01:04 MST 2017", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Nnolim:2017:FBM, author = "Uche A. Nnolim", title = "{FPGA}-Based Multiplier-Less Log-Based Hardware Architectures for Hybrid Color Image Enhancement System", journal = j-INT-J-IMAGE-GRAPHICS, volume = "17", number = "1", pages = "1750004", month = jan, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467817500048", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 26 07:01:04 MST 2017", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Bhat:2017:MIF, author = "Aruna Bhat", title = "Makeup Invariant Face Recognition using Features from Accelerated Segment Test and Eigen Vectors", journal = j-INT-J-IMAGE-GRAPHICS, volume = "17", number = "1", pages = "1750005", month = jan, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1142/S021946781750005X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 26 07:01:04 MST 2017", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Cejnog:2017:WAR, author = "Luciano W. X. Cejnog and Fernando A. A. Yamada and Marcelo Bernardes Vieira", title = "Wide Angle Rigid Registration Using a Comparative Tensor Shape Factor", journal = j-INT-J-IMAGE-GRAPHICS, volume = "17", number = "1", pages = "1750006", month = jan, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467817500061", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Jan 26 07:01:04 MST 2017", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Tian:2017:ISC, author = "Chunwei Tian and Guanglu Sun and Qi Zhang and Weibing Wang and Teng Chen and Yuan Sun", title = "Integrating Sparse and Collaborative Representation Classifications for Image Classification", journal = j-INT-J-IMAGE-GRAPHICS, volume = "17", number = "2", pages = "1750007", month = apr, year = "2017", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Oct 24 06:24:12 MDT 2017", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Manchanda:2017:FTB, author = "Meenu Manchanda and Rajiv Sharma", title = "Fuzzy Transform-Based Fusion of Multiple Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "17", number = "2", pages = "1750008", month = apr, year = "2017", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Oct 24 06:24:12 MDT 2017", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kontakis:2017:SIC, author = "Konstantinos Kontakis and Athanasios G. Malamos and Malvina Steiakaki and Spyros Panagiotakis", title = "Spatial Indexing of Complex Virtual Reality Scenes in the {Web}", journal = j-INT-J-IMAGE-GRAPHICS, volume = "17", number = "2", pages = "1750009", month = apr, year = "2017", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Oct 24 06:24:12 MDT 2017", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Ghislain:2017:ATD, author = "Pandry Koffi Ghislain and Georges Lausanne Loum and Ouattara Nouho", title = "Adaptation of Telegraph Diffusion Equation for Noise Reduction on Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "17", number = "2", pages = "1750010", month = apr, year = "2017", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Oct 24 06:24:12 MDT 2017", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Shah:2017:NMI, author = "Said Khalid Shah", title = "Nonrigid Medical Image Registration Based on Curves", journal = j-INT-J-IMAGE-GRAPHICS, volume = "17", number = "2", pages = "1750011", month = apr, year = "2017", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Oct 24 06:24:12 MDT 2017", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Parseh:2017:NCF, author = "Mohammad Javad Parseh and Mojtaba Meftahi", title = "A New Combined Feature Extraction Method for {Persian} Handwritten Digit Recognition", journal = j-INT-J-IMAGE-GRAPHICS, volume = "17", number = "2", pages = "1750012", month = apr, year = "2017", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Oct 24 06:24:12 MDT 2017", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Maity:2017:ODC, author = "Santi P. Maity and Hirak Kumar Maity", title = "Optimality in Distortion Control in Reversible Watermarking Using Genetic Algorithms", journal = j-INT-J-IMAGE-GRAPHICS, volume = "17", number = "3", pages = "1750013", month = jul, year = "2017", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Oct 24 06:24:13 MDT 2017", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Farshi:2017:INR, author = "Taymaz Rahkar Farshi", title = "Image Noise Reduction Method Based on Compatibility with Adjacent Pixels", journal = j-INT-J-IMAGE-GRAPHICS, volume = "17", number = "3", pages = "1750014", month = jul, year = "2017", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Oct 24 06:24:13 MDT 2017", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Sarfraz:2017:QTN, author = "Muhammad Sarfraz and Shamaila Samreen and Malik Zawwar Hussain", title = "A Quadratic Trigonometric Nu Spline with Shape Control", journal = j-INT-J-IMAGE-GRAPHICS, volume = "17", number = "3", pages = "1750015", month = jul, year = "2017", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Oct 24 06:24:13 MDT 2017", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Prabhanjan:2017:DLA, author = "S. Prabhanjan and R. Dinesh", title = "Deep Learning Approach for {Devanagari} Script Recognition", journal = j-INT-J-IMAGE-GRAPHICS, volume = "17", number = "3", pages = "1750016", month = jul, year = "2017", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Oct 24 06:24:13 MDT 2017", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Abdelwahab:2017:IIS, author = "Ahmed A. Abdelwahab", title = "Inter-Image Similarity-Based Fast Adaptive Block Size Vector Quantizer for Image Coding", journal = j-INT-J-IMAGE-GRAPHICS, volume = "17", number = "3", pages = "1750017", month = jul, year = "2017", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Oct 24 06:24:13 MDT 2017", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Alcantara:2017:HAC, author = "Marlon F. Alcantara and Helio Pedrini and Yu Cao", title = "Human Action Classification Based on Silhouette Indexed Interest Points for Multiple Domains", journal = j-INT-J-IMAGE-GRAPHICS, volume = "17", number = "3", pages = "1750018", month = jul, year = "2017", CODEN = "????", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Oct 24 06:24:13 MDT 2017", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Maehara:2017:DOR, author = "Seiichi Maehara and Kazuo Ikeshiro and Hiroki Imamura", title = "A $3$-Dimensional Object Recognition Method Using Relationship of Distances and Angles in Feature Points", journal = j-INT-J-IMAGE-GRAPHICS, volume = "17", number = "4", pages = "1750019", month = oct, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1142/S021946781750019X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Oct 31 06:37:09 MDT 2017", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Fei:2017:EME, author = "Lunke Fei and Shaohua Teng and Jigang Wu and Imad Rida", title = "Enhanced Minutiae Extraction for High-Resolution Palmprint Recognition", journal = j-INT-J-IMAGE-GRAPHICS, volume = "17", number = "4", pages = "1750020", month = oct, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467817500206", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Oct 31 06:37:09 MDT 2017", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Yamada:2017:SBW, author = "F. A. A. Yamada and L. W. X. Cejnog and M. B. Vieira and R. L. S. da Silva", title = "A Shape-Based Weighting Strategy Applied to the Covariance Estimation on {ICP}", journal = j-INT-J-IMAGE-GRAPHICS, volume = "17", number = "4", pages = "1750021", month = oct, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467817500218", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Oct 31 06:37:09 MDT 2017", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Nnolim:2017:FBH, author = "Uche A. Nnolim", title = "{FPGA}-Based Hardware Architecture for Fuzzy Homomorphic Enhancement Based on Partial Differential Equations", journal = j-INT-J-IMAGE-GRAPHICS, volume = "17", number = "4", pages = "1750022", month = oct, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1142/S021946781750022X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Oct 31 06:37:09 MDT 2017", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Cheng:2017:HLM, author = "Ruzhong Cheng and Yongjun Zhang and Guoping Wang and Yong Zhao and Rahmatulloev Khusravsho", title = "{Haar}-Like Multi-Granularity Texture Features for Pedestrian Detection", journal = j-INT-J-IMAGE-GRAPHICS, volume = "17", number = "4", pages = "1750023", month = oct, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467817500231", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Oct 31 06:37:09 MDT 2017", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Li:2017:OAS, author = "Qianwen Li and Zhihua Wei and Cairong Zhao", title = "Optimized Automatic Seeded Region Growing Algorithm with Application to {ROI} Extraction", journal = j-INT-J-IMAGE-GRAPHICS, volume = "17", number = "4", pages = "1750024", month = oct, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467817500243", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Oct 31 06:37:09 MDT 2017", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Anonymous:2017:AIV, author = "Anonymous", title = "Author Index (Volume 17)", journal = j-INT-J-IMAGE-GRAPHICS, volume = "17", number = "4", pages = "1799001", month = oct, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467817990017", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Oct 31 06:37:09 MDT 2017", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Wei:2018:AQE, author = "Jie Wei and Lin Zhang and Bingmei M. Fu", title = "Automatic Quantification of Endothelial Nitric Oxide Levels in a Microvessel with and without Tumor Cell Adhesion", journal = j-INT-J-IMAGE-GRAPHICS, volume = "18", number = "1", pages = "1850001", month = jan, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467818500018", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Jan 24 07:10:26 MST 2018", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Wang:2018:IST, author = "Huan Wang and Fei Yang and Congcong Zhang and Mingwu Ren", title = "Infrared Small Target Detection Based on Patch Image Model with Local and Global Analysis", journal = j-INT-J-IMAGE-GRAPHICS, volume = "18", number = "1", pages = "1850002", month = jan, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1142/S021946781850002X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Jan 24 07:10:26 MST 2018", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Verma:2018:IDU, author = "Atul Kumar Verma and Barjinder Singh Saini and Taranjit Kaur", title = "Image Denoising using {Alexander} Fractional Hybrid Filter", journal = j-INT-J-IMAGE-GRAPHICS, volume = "18", number = "1", pages = "1850003", month = jan, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467818500031", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Jan 24 07:10:26 MST 2018", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Divakara:2018:HSA, author = "S. S. Divakara and Sudarshan Patilkulkarni and Cyril Prasanna Raj", title = "High Speed Area Optimized Hybrid {DA} Architecture for {$2$D-DTCWT}", journal = j-INT-J-IMAGE-GRAPHICS, volume = "18", number = "1", pages = "1850004", month = jan, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467818500043", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Jan 24 07:10:26 MST 2018", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Domadiya:2018:SFE, author = "Prashant Domadiya and Pratik Shah and Suman K. Mitra", title = "Shadow-Free, Expeditious and Precise, Moving Object Separation from Video", journal = j-INT-J-IMAGE-GRAPHICS, volume = "18", number = "1", pages = "1850005", month = jan, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467818500055", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Jan 24 07:10:26 MST 2018", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Choudhury:2018:SBC, author = "Bismita Choudhury and Patrick Then and Biju Issac and Valliappan Raman and Manas Kumar Haldar", title = "A Survey on Biometrics and Cancelable Biometrics Systems", journal = j-INT-J-IMAGE-GRAPHICS, volume = "18", number = "1", pages = "1850006", month = jan, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467818500067", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Jan 24 07:10:26 MST 2018", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Giangreco-Maidana:2018:CPS, author = "Alejandro J. Giangreco-Maidana and Horacio Legal-Ayala and Christian E. Schaerer and Waldemar Villamayor-Venialbo", title = "Contour-Point Signature Shape Descriptor for Point Correspondence", journal = j-INT-J-IMAGE-GRAPHICS, volume = "18", number = "2", pages = "1850007", month = apr, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467818500079", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Apr 7 18:25:20 MDT 2018", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Ghosh:2018:VVE, author = "Swarup Kr Ghosh and Anupam Ghosh and Amlan Chakrabarti", title = "{VEA}: Vessel Extraction Algorithm by Active Contour Model and a Novel Wavelet Analyzer for Diabetic Retinopathy Detection", journal = j-INT-J-IMAGE-GRAPHICS, volume = "18", number = "2", pages = "1850008", month = apr, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467818500080", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Apr 7 18:25:20 MDT 2018", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Chen:2018:VCE, author = "Liang-Hua Chen and Chih-Wen Su", title = "Video Caption Extraction Using Spatio-Temporal Slices", journal = j-INT-J-IMAGE-GRAPHICS, volume = "18", number = "2", pages = "1850009", month = apr, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467818500092", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Apr 7 18:25:20 MDT 2018", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Shreekanth:2018:NDI, author = "T. Shreekanth and M. R. Deeksha and Karthikeya R. Kaushik", title = "A Novel Data Independent Approach for Conversion of Hand Punched {Kannada} {Braille} Script to Text and Speech", journal = j-INT-J-IMAGE-GRAPHICS, volume = "18", number = "2", pages = "1850010", month = apr, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467818500109", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Apr 7 18:25:20 MDT 2018", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Ngom:2018:SDP, author = "Ndeye Fatou Ngom and Cheikh H. T. C. Ndiaye and Oumar Niang and Samba Sidibe", title = "Shape Descriptors for Porous Media Analysis Using Computed Tomography Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "18", number = "2", pages = "1850011", month = apr, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467818500110", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Apr 7 18:25:20 MDT 2018", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Wu:2018:IAB, author = "Zhaoqi Wu and Reziwanguli Xiamixiding and Atul Sajjanhar and Juan Chen and Quan Wen", title = "Image Appearance-Based Facial Expression Recognition", journal = j-INT-J-IMAGE-GRAPHICS, volume = "18", number = "2", pages = "1850012", month = apr, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467818500122", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Apr 7 18:25:20 MDT 2018", bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml; https://www.math.utah.edu/pub/tex/bib/ijig.bib", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Helmy:2018:GFS, author = "Tarek Helmy", title = "A Generic Framework for Semantic Annotation of Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "18", number = "3", pages = "??--??", month = jul, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467818500134", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Nov 9 06:55:48 MST 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467818500134", abstract = "Advanced digital capturing technologies have led to the explosive growth of images on the Web. To retrieve the desired image from a huge amount of images, textual query is handier to represent the user's interest than providing a visually similar image as a query. Semantic annotation of images' has been identified as an important step towards more efficient manipulation and retrieval of images. The aim of the semantic annotation of images is to annotate the existing images on the Web so that the images are more easily interpreted by searching programs. To annotate the images effectively, extensive image interpretation techniques have been developed to explore the semantic concept of images. But, due to the complexity and variety of backgrounds, effective image annotation is still a very challenging and open problem. Semantic annotation of Web contents manually is not feasible or scalable too, due to the huge amount and rate of emerging Web content. In this paper, we have surveyed the existing image annotation models and developed a hierarchical classification-based image annotation framework for image categorization, description and annotation. Empirical evaluation of the proposed framework with respect to its annotation accuracy shows high precision and recall compared with other annotation models with significant time and cost. An important feature of the proposed framework is that its specific annotation techniques, suitable for a particular image category, can be easily integrated and developed for other image categories.", acknowledgement = ack-nhfb, articleno = "1850013", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Benchaou:2018:FSB, author = "Soukaina Benchaou and M'Barek Nasri and Ouafae {El Melhaoui}", title = "Feature Selection Based on Evolution Strategy for Character Recognition", journal = j-INT-J-IMAGE-GRAPHICS, volume = "18", number = "3", pages = "??--??", month = jul, year = "2018", DOI = "https://doi.org/10.1142/S0219467818500146", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Nov 9 06:55:48 MST 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467818500146", abstract = "Handwriting, printed character recognition is an interesting area in image processing and pattern recognition. It consists of a number of phases which are preprocessing, feature extraction and classification. The phase of feature extraction is carried out by different techniques; zoning, profile projection, and ameliored Freeman. The high number of features vector can increase the error rate and the training time. So, to solve this problem, we present in this paper a new method of selecting attributes based on the evolution strategy in order to reduce the feature vector dimension and to improve the recognition rate. The proposed model has been applied to recognize numerals and it obtained a better results and showed more robustness than without the selection system.", acknowledgement = ack-nhfb, articleno = "1850014", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Roy:2018:HIR, author = "Aniket Roy and Arpan Kumar Maiti and Kuntal Ghosh", title = "An {HVS} Inspired Robust Non-blind Watermarking Scheme in {YCbCr} Color Space", journal = j-INT-J-IMAGE-GRAPHICS, volume = "18", number = "3", pages = "??--??", month = jul, year = "2018", DOI = "https://doi.org/10.1142/S0219467818500158", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Nov 9 06:55:48 MST 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/cryptography2010.bib; https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467818500158", abstract = "Digital Watermarking is an important tool for copyright protection. A good quality watermarking scheme should provide both perceptual transparency as well as robustness against attacks. Many efficient schemes exist for grayscale image watermarking, but relatively less attention has been paid to watermarking for color images. Moreover, the existing works do not provide adequate justification for the possible choice of color space. In this paper, justification is provided for the choice of YCbCr color space for watermark embedding. A human visual system (HVS)-inspired image-adaptive non-blind watermarking scheme in the YCbCr space has subsequently been proposed. This new algorithm has been referred to as the Additive Embedding Scheme (AES). It comprises of a modified watermarking strength parameter ( {\textalpha}mean {\textalpha}mean {\textalpha}mean ), in combination with the discrete wavelet transform and singular value decomposition (DWT-SVD). Experimental results demonstrate that the proposed watermarking scheme in YCbCr color space provides better perceptual quality as well as robustness against attacks as compared to existing schemes. We have further improvised the aforementioned scheme to come up with a Multiplicative Embedding Scheme (MES) for additional robustness against a special type of attack, viz. the Singular Value Exchange Attack.", acknowledgement = ack-nhfb, articleno = "1850015", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Sacht:2018:RTC, author = "Leonardo Sacht and Diego Nehab and Rodolfo Schulz de Lima", title = "Real-Time Continuous Image Processing", journal = j-INT-J-IMAGE-GRAPHICS, volume = "18", number = "3", pages = "??--??", month = jul, year = "2018", DOI = "https://doi.org/10.1142/S021946781850016X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Nov 9 06:55:48 MST 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S021946781850016X", abstract = "In this work, we propose a framework that performs a number of popular image-processing operations in the continuous domain. This is in contrast to the standard practice of defining them as operations over discrete sequences of sampled values. The guiding principle is that, in order to prevent aliasing, nonlinear image-processing operations should ideally be performed prior to prefiltering and sampling. This is of course impractical, as we may not have access to the continuous input. Even so, we show that it is best to apply image-processing operations over the continuous reconstruction of the input. This transformed continuous representation is then prefiltered and sampled to produce the output. The use of high-quality reconstruction strategies brings this alternative much closer to the ideal than directly operating over discrete values. We illustrate the advantages of our framework with several popular effects. In each case, we demonstrate the quality difference between continuous image-processing, their discrete counterparts and previous anti-aliasing alternatives. Finally, our GPU implementation shows that current graphics hardware has enough computational power to perform continuous image processing in real-time.", acknowledgement = ack-nhfb, articleno = "1850016", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Zhang:2018:ACF, author = "Jinpeng Zhang and Jinming Zhang", title = "An Analysis of {CNN} Feature Extractor Based on {KL} Divergence", journal = j-INT-J-IMAGE-GRAPHICS, volume = "18", number = "3", pages = "??--??", month = jul, year = "2018", DOI = "https://doi.org/10.1142/S0219467818500171", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Nov 9 06:55:48 MST 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467818500171", abstract = "Convolutional neural networks (CNNs) have brought in exciting progress in many computer vision tasks. But the feature extraction process executed by CNN still keeps a black box to us, and we have not fully understood its working mechanism. In this paper, we propose a method to evaluate CNN features and further to analyze the CNN feature extractor, which is inspired by Bayes Classification Theory and KL divergence (KLD). Experiments have shown that CNN can promote feature discrimativeness by gradually increasing the intra-class KLD, and meanwhile promote feature robustness by gradually decreasing the inner-class KLD during training. Experiments also reveal that, with the deepening of network, CNN can gradually improve separability information density in feature space and encode much more separability information into the final feature vectors.", acknowledgement = ack-nhfb, articleno = "1850017", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Lu:2018:FVS, author = "Yan Lu and Bin Liu and Weihai Li and Nenghai Yu", title = "Fast Video Stitching for Aerially Captured {HD} Videos", journal = j-INT-J-IMAGE-GRAPHICS, volume = "18", number = "3", pages = "??--??", month = jul, year = "2018", DOI = "https://doi.org/10.1142/S0219467818500183", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Nov 9 06:55:48 MST 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467818500183", abstract = "Videos captured from the air by flying devices like Unmanned Aerial Vehicles (UAVs) have great application prospects in many fields such as journalism, art, military and public security. Due to the difficulties such as vibration, needing for speed and high resolution and so on, it is non-trivial to apply traditional static image stitching algorithms to flying cameras. To this end, we propose a real-time video stitching system which is capable to stitch high definition (HD) videos captured by mobile aerial devices. In our work, we use scale invariant information to speed up the feature point extraction.", acknowledgement = ack-nhfb, articleno = "1850018", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Samanta:2018:LTB, author = "Sourav Samanta and Amartya Mukherjee and Amira S. Ashour and Nilanjan Dey and Jo{\~a}o Manuel R. S. Tavares and Wahiba {Ben Abdessalem Kar{\^a}a} and Redha Taiar and Ahmad Taher Azar and Aboul {Ella Hassanien}", title = "Log Transform Based Optimal Image Enhancement Using Firefly Algorithm for Autonomous Mini Unmanned Aerial Vehicle: An Application of Aerial Photography", journal = j-INT-J-IMAGE-GRAPHICS, volume = "18", number = "4", pages = "??--??", month = oct, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467818500195", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Nov 9 06:55:50 MST 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467818500195", abstract = "The Unmanned Aerial Vehicles (UAV) are widely used for capturing images in border area surveillance, disaster intensity monitoring, etc. An aerial photograph offers a permanent recording solution as well. But rapid weather change, low quality image capturing equipments results in low/poor contrast images during image acquisition by Autonomous UAV. In this current study, a well-known meta-heuristic technique, namely, Firefly Algorithm (FA) is reported to enhance aerial images taken by a Mini Unmanned Aerial Vehicle (MUAV) via optimizing the value of certain parameters. These parameters have a wide range as used in the Log Transformation for image enhancement. The entropy and edge information of the images is used as an objective criterion for evaluating the image enhancement of the proposed system. Inconsistent with the objective criterion, the FA is used to optimize the parameters employed in the objective function that accomplishes the superlative enhanced image. A low-light imaging has been performed at evening time to prove the effectiveness of the proposed algorithm. The results illustrate that the proposed method has better convergence and fitness values compared to Particle Swarm Optimization. Therefore, FA is superior to PSO, as it converges after a less number of iterations.", acknowledgement = ack-nhfb, articleno = "1850019", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Sharma:2018:CSS, author = "Himani Sharma and D. C. Mishra and R. K. Sharma and Naveen Kumar", title = "Crypto-stego System for Securing Text and Image Data", journal = j-INT-J-IMAGE-GRAPHICS, volume = "18", number = "4", pages = "??--??", month = oct, year = "2018", DOI = "https://doi.org/10.1142/S0219467818500201", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Nov 9 06:55:50 MST 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/cryptography2010.bib; https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467818500201", abstract = "Conventional techniques for security of data, designed by using only one of the security mechanisms, cryptography or steganography, are suitable for limited applications only. In this paper, we propose a crypto-stego system that would be appropriate for secure transmission of different forms of data. In the proposed crypto-stego system, we present a mechanism to provide secure transmission of data by multiple safety measures, firstly by applying encryption using Affine Transform and Discrete Cosine Transform (DCT) and then merging this encrypted data with an image, randomly chosen from a set of available images, and sending the image so obtained to the receiver at the other end through the network. The data to be sent over a communication channel may be a gray-scale or colored image, or a text document ({.doc}, {.txt}, or {.pdf} file). As it is encrypted and sent hidden in an image, it avoids any attention to itself by the observers in the network. At the receiver's side, reverse transformations are applied to obtain the original information. The experimental results, security analysis and statistical analysis for gray-scale images, RGB images, text documents ({.doc}, {.txt}, {.pdf} files), show robustness and appropriateness of the proposed crypto-stego system for secure transmission of the data through unsecured network. The security analysis and key space analysis demonstrate that the proposed technique is immune from cryptanalysis.", acknowledgement = ack-nhfb, articleno = "1850020", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Talbi:2018:SIW, author = "Mourad Talbi and Med Salim Bouhlel", title = "Secure Image Watermarking Based on {LWT} and {SVD}", journal = j-INT-J-IMAGE-GRAPHICS, volume = "18", number = "4", pages = "??--??", month = oct, year = "2018", DOI = "https://doi.org/10.1142/S0219467818500213", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Nov 9 06:55:50 MST 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/cryptography2010.bib; https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467818500213", abstract = "Nowadays, digital watermarking is employed for authentication and copyright protection. In this paper, a secure image watermarking scheme based on lifting wavelet transform (LWT) and singular value decomposition (SVD), is proposed. Both LWT and SVD are used as mathematical tools for embedding watermark in the host image. In this work, the watermark is a speech signal which is segmented into shorted portions having the same length. This length is equal to 256 and these different portions constitute the different columns of a speech image. The latter is then embedded into a grayscale or color image (the host image). This procedure is performed in order to insert into an image a confidential data which is in our case a speech signal. But instead of embedding this speech signal directly into the image, we transform it into a matrix and treated it as an image (``a speech image''). Of course, this speech signal transformation permits us to use LWT-2D and SVD to both the host image and the watermark (``a speech image''). The proposed technique is applied to a number of grayscale and color images. The obtained results from peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) computations show the performance of the proposed technique. Experimental evaluation also shows that the proposed scheme is able to withstand a number of attacks such as JPEG compression, mean and median attacks. In our evaluation of the proposed technique, we used another technique of secure image watermarking based on DWT-2D and SVD.", acknowledgement = ack-nhfb, articleno = "1850021", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Jangid:2018:HDS, author = "Mahesh Jangid and Sumit Srivastava", title = "Handwritten {Devanagari} Similar Character Recognition by {Fisher} Linear Discriminant and Pairwise Classification", journal = j-INT-J-IMAGE-GRAPHICS, volume = "18", number = "4", pages = "??--??", month = oct, year = "2018", DOI = "https://doi.org/10.1142/S0219467818500225", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Nov 9 06:55:50 MST 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467818500225", abstract = "The research works in Handwritten Devanagari Characters are continually evolving into new challenges, which exposed the new sources of further research work like, character normalization, gray-level normalization, a discrimination of the similar characters and many more. This paper discusses the discrimination of the similar characters, which is one of the major sources of classification error. The similar shape character has a very minute difference, which is called critical region and used to discriminate them by human beings. The primary goal of the current work is to identify the critical region of the similar character and use the same to generate additional features in order to minimize the classification errors in the end results. It is also quite challenging to identify the critical region as the characters are written in different handwriting styles and fonts. The paper suggests the Fisher linear discriminant model to detect the critical region, which is used to extract the additional feature. The experiments work was conducted on the standard database, which has 36 172 handwritten Devanagari characters and significant improvement has been recorded by the aforesaid technique.", acknowledgement = ack-nhfb, articleno = "1850022", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Salehi:2018:RHF, author = "Hadi Salehi and Javad Vahidi and Homayun Motameni", title = "A Robust Hybrid Filter Based on Evolutionary Intelligence and Fuzzy Evaluation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "18", number = "4", pages = "??--??", month = oct, year = "2018", DOI = "https://doi.org/10.1142/S0219467818500237", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Nov 9 06:55:50 MST 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467818500237", abstract = "In this paper, a novel denoising method based on wavelet, extended adaptive Wiener filter and the bilateral filter is proposed for digital images. Production of mode is accomplished by the genetic algorithm. The proposed extended adaptive Wiener filter has been developed from the adaptive Wiener filter. First, the genetic algorithm suggest some hybrid models. The attributes of images, including peak signal to noise ratio, signal to noise ratio and image quality assessment are studied. Then, in order to evaluate the model, the values of attributes are sent to the Fuzzy deduction system. Simulations and evaluations mentioned in this paper are accomplished on some standard images such as Lena, boy, fruit, mandrill, Barbara, butterfly, and boat. Next, weaker models are omitted by studying of the various models. Establishment of new generations performs in a form that a generation emendation is carried out, and final model has a more optimum quality compared to each two filters in order to obviate the noise. At the end, the results of this system are studied so that a comprehensive model with the best performance is to be found. Experiments show that the proposed method has better performance than wavelet, bilateral, Butterworth, and some other filters.", acknowledgement = ack-nhfb, articleno = "1850023", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kittisuwan:2018:TRD, author = "Pichid Kittisuwan", title = "Textural Region Denoising: Application in Agriculture", journal = j-INT-J-IMAGE-GRAPHICS, volume = "18", number = "4", pages = "??--??", month = oct, year = "2018", DOI = "https://doi.org/10.1142/S0219467818500249", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Nov 9 06:55:50 MST 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467818500249", abstract = "Geo-science and remote sensing technologies play enormous roles in agriculture nowadays, especially in analysis of data from aerial images such as satellite images and drone images. Most agricultural images contain more textural regions than non-textural regions. Therefore, data management in terms of textural regions is very important. Indeed, additive white Gaussian noise (AWGN) is the fundamental problem in digital image analysis. In wavelet transform, Bayesian estimation is useful in several noise reduction methods. The Bayesian technique requires a prior modeling of noise-free wavelet coefficients. In non-textural regions, the wavelet coefficients might be better modeled by super-Gaussian density such as Laplacian, Pearson type VII, Cauchy, and two-sided gamma distributions. However, the statistical model of textural regions is Gaussian model. Therefore, in agricultural images, we require flexible model between super-Gaussian and Gaussian models. In fact, the generalized Gaussian distribution (GGD) is the suitable model for this problem. Therefore, we present new maximum a posteriori (MAP) estimator for spacial case of GGD in AWGN. Here, we obtained the analytical form solution. Moreover, this research work will also describe limitations of GGD application in Bayesian estimator. The simulation results illustrate that our presented method outperforms the state-of-the-art methods qualitatively and quantitatively.", acknowledgement = ack-nhfb, articleno = "1850024", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Anonymous:2018:AIV, author = "Anonymous", title = "Author Index (Volume 18)", journal = j-INT-J-IMAGE-GRAPHICS, volume = "18", number = "4", pages = "??--??", month = oct, year = "2018", DOI = "https://doi.org/10.1142/S0219467818990012", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Nov 9 06:55:50 MST 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467818990012", acknowledgement = ack-nhfb, articleno = "1899001", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Arani:2019:HFW, author = "Seyed Ali Asghar Abbaszadeh Arani and Ehsanollah Kabir and Reza Ebrahimpour", title = "Handwritten {Farsi} Word Recognition Using {NN}-Based Fusion of {HMM} Classifiers with Different Types of Features", journal = j-INT-J-IMAGE-GRAPHICS, volume = "19", number = "1", pages = "??--??", month = jan, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467819500013", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Feb 14 06:31:30 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467819500013", abstract = "In this paper, an off-line method, based on hidden Markov model, HMM, is used for holistic recognition of handwritten words of a limited vocabulary. Three feature sets based on image gradient, black--white transition and contour chain code are used. For each feature set an HMM is trained for each word. In the recognition step, the outputs of these classifiers are combined through a multilayer perceptron, MLP. High number of connections in this network causes a computational complexity in the training. To avoid this problem, a new method is proposed. In the experiments on 16000 images of 200 names of Iranian cities, from ``Iranshahr 3'' dataset, the results of the proposed method are presented and compared with some similar methods. An error analysis on these results is also provided.", acknowledgement = ack-nhfb, articleno = "1950001", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Dutta:2019:ISB, author = "P. K. Dutta", title = "Image Segmentation Based Approach for the Purpose of Developing Satellite Image Spatial Information Extraction for Forestation and River Bed Analysis", journal = j-INT-J-IMAGE-GRAPHICS, volume = "19", number = "1", pages = "??--??", month = jan, year = "2019", DOI = "https://doi.org/10.1142/S0219467819500025", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Feb 14 06:31:30 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467819500025", abstract = "Classification of remote sensing spatial information from multi spectral satellite imagery can be used to obtain multiple representation of the image and capture different structure lineaments. Pixels are grouped using clustering and morphology based segmentation for region based spatial information. This is used to calculate the spatial features of the contiguous regions by classifying the region into the statistics of the pixel properties. In the proposed work, analysis of Google Earth images for identification of morphological patterns of the river flow is done for remote sensing image using graph-cuts. Multi-temporal satellite images acquired from Google Earth to identify the digital elevation is used to formulate the energy function from images to compare the displacement in pixel value using similarity measure. A method is proposed to solve non-rigid image transformation via graph-cuts algorithm by modeling the registration process as a discrete labeling problem. A displacement vector associated to each pixel in the source image indicates the corresponding position in the moving image. The transformation matrix produced from change in the intensity of the pixels for a region is then optimized for energy minimization by using the graph-cuts algorithm and demon registration technique. The proposed study enhances the advantages of regional segmentation in order to know homogeneous areas for optimal image segmentation and digital footprints for change in the river bed patterns by identifying the change in LANDSAT data from temporal satellite images. By applying the proposed multi-level registration method, the number of labels used in each level is greatly reduced due to lower image resolution being used in coarser levels. The results demonstrate that the lineament detection for better accuracy compared to traditional sources of lineament identification methods. It has provided better geotectonic understanding of Cudappah rock in Ahobhilam with Quartzite. The imprints of Eastern Ghat orogeny are seen in upper stream section through a graph cut based segmentation approach.", acknowledgement = ack-nhfb, articleno = "1950002", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Nnolim:2019:FFD, author = "Uche A. Nnolim", title = "Formulation of Fractional Derivative-Based De-Hazing Algorithm and Implementation on Mobile-Embedded Devices", journal = j-INT-J-IMAGE-GRAPHICS, volume = "19", number = "1", pages = "??--??", month = jan, year = "2019", DOI = "https://doi.org/10.1142/S0219467819500037", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Feb 14 06:31:30 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467819500037", abstract = "This paper presents the modification of a previously developed algorithm using fractional order calculus and its implementation on mobile-embedded devices such as smartphones. The system performs enhancement on three categories of images such as those exhibiting uneven illumination, faded features/colors and hazy appearance. The key contributions include the simplified scheme for illumination correction, contrast enhancement and de-hazing using fractional derivative-based spatial filter kernels. These are achieved without resorting to logarithmic image processing, histogram-based statistics and complex de-hazing techniques employed by conventional algorithms. The simplified structure enables ease of implementation of the algorithm on mobile devices as an image processing application. Results indicate that the fractional order version of the algorithm yields good results relative to the integer order version and other algorithms from the literature.", acknowledgement = ack-nhfb, articleno = "1950003", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Biswas:2019:CPM, author = "Biswajit Biswas and Biplab Kanti Sen", title = "Color {PET-MRI} Medical Image Fusion Combining Matching Regional Spectrum in Shearlet Domain", journal = j-INT-J-IMAGE-GRAPHICS, volume = "19", number = "1", pages = "??--??", month = jan, year = "2019", DOI = "https://doi.org/10.1142/S0219467819500049", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Feb 14 06:31:30 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467819500049", abstract = "The color PET-MRI medical image fusion is a growing research area in medical image processing domain. MRI imagery provides the picture of the anatomy of brain tissues without any functional information, while the color PET imagery gives the functional information of brain tissues with low spatial resolution. An ideal fusion model should maintain both the functional and spatial information of the images without any spatial distortion or color deformation. In this work, we present a novel fusion technique for color PET-MRI medical images using Two-Dimensional Discrete Fourier (2DFT)-Karhunen--Loeve transform (KLT) and singular value decomposition (SVD) in shearlet domain. This method decomposes the source images into multi-scaled and multi-directional sub-bands by shearlet transform (ST). Then, SVD is utilized to eliminate superfluous ST coefficients; later, the 2DFT and KLT are utilized to estimate optimal low-pass ST coefficients in each of the decomposed images. Later, we combine the largest low-pass ST coefficients using a novel fusion strategy. The process of decomposing the source image has been discussed in detail. Finally, we use the inverse shearlet transformation (IST) to obtain the fused image. Experimental results establish the excellence of our proposed method in terms of quantitative and qualitative evaluation criteria compared to other state-of-the-art techniques.", acknowledgement = ack-nhfb, articleno = "1950004", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Baig:2019:RTF, author = "M. Amir Baig and Athar A. Moinuddin and Ekram Khan", title = "Real-Time Fidelity Measurement of {JPEG2000} Coded Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "19", number = "1", pages = "??--??", month = jan, year = "2019", DOI = "https://doi.org/10.1142/S0219467819500050", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Feb 14 06:31:30 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467819500050", abstract = "The progressive nature of the JPEG2000 coded bitstream allows the reconstruction of images of different qualities from a single coded bitstream. This feature is utilized in this work to estimate the mean-squared-error (MSE) of reconstructed images without requiring the original image. It is based on the fact that if the MSE between the original image and a lower quality image is known, the MSE for higher quality images can be estimated from a quality scalable bitstream. The proposed method is highly accurate and is very simple as no complex statistical modeling is needed. Therefore, it is suitable to measure the fidelity of JPEG2000 decoded images at any desired quality in a real-time scenario.", acknowledgement = ack-nhfb, articleno = "1950005", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Halder:2019:SSP, author = "Amiya Halder and Sayan Halder and Samrat Chakraborty and Apurba Sarkar", title = "A Statistical Salt-and-Pepper Noise Removal Algorithm", journal = j-INT-J-IMAGE-GRAPHICS, volume = "19", number = "1", pages = "??--??", month = jan, year = "2019", DOI = "https://doi.org/10.1142/S0219467819500062", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Feb 14 06:31:30 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467819500062", abstract = "This paper proposes a novel approach to remove salt-and-pepper noise from a given noisy image. The proposed algorithm is based on statistical quantities such as mean and standard deviation. It determines the intensity to be placed on the impulse point by calculating the eligibility of the nearby points in a very simple way. This method works iteratively and removes all the impulse points restoring the edges and minute details. The proposed algorithm is very efficient and gives better results than various existing algorithms. The performance of the proposed method are compared with other existing methods with images of noise density as high as 99\% and is found to perform better.", acknowledgement = ack-nhfb, articleno = "1950006", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Oliveira:2019:NTB, author = "Walter Alexandre A. de Oliveira and Denise Guliato and Douglas {Coelho Braga de Oliveira} and Rodrigo Luis de {Souza da Silva} and Gilson Antonio Giraldi", title = "New Technique for Binary Morphological Shape-Based Interpolation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "19", number = "2", pages = "??--??", year = "2019", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467819500074", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri May 10 09:47:18 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467819500074", abstract = "In this paper we consider shape-based methods to generate additional slices in 3D binary volumes. The focused interpolation approaches, named SIMOL and BORS, are based on morphological and logical operators. Given two adjacent slices S1 and S2 of the binary image set, the methods iteratively generate a sequence of new slices showing a gradual transition between the corresponding shapes. First, we analyze the SIMOL and BORS techniques and highlight their problems. Then we present the main contribution of this paper: a new interpolation scheme, called SIMOL-NEW, that combines the iterative scheme of BORS and an interpolation kernel generated through SIMOL framework. Next, we compare SIMOL-NEW and BORS approaches using theoretical elements and computational experiments. The latter are executed using: (a) benchmark shapes; (b) simple volumes defined by sphere and paraboloid; (c) combination of ellipsoids; (d) a fork-like volume; (e) Cylinder Minus Sphere. The conclusion is that SIMOL-NEW performs closer to BORS for the cases (a) and (c) but it is more accurate than BORS in the tests (b) and (d). Besides, we offer comparisons of state-of-the-art approaches in shape-based interpolation and SIMOL-NEW using ground truth volumes (d) and (e). The computational experiment report that SIMOL-NEW gets outstanding results regarding the ability to recover the target volume.", acknowledgement = ack-nhfb, articleno = "1950007", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Dixit:2019:FBD, author = "Umesh D. Dixit and M. S. Shirdhonkar", title = "Fingerprint-Based Document Image Retrieval", journal = j-INT-J-IMAGE-GRAPHICS, volume = "19", number = "2", pages = "??--??", year = "2019", DOI = "https://doi.org/10.1142/S0219467819500086", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri May 10 09:47:18 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/cryptography2010.bib; https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467819500086", abstract = "Most of the documents use fingerprint impression for authentication. Property related documents, bank checks, application forms, etc., are the examples of such documents. Fingerprint-based document image retrieval system aims to provide a solution for searching and browsing of such digitized documents. The major challenges in implementing fingerprint-based document image retrieval are an efficient method for fingerprint detection and an effective feature extraction method. In this work, we propose a method for automatic detection of a fingerprint from given query document image employing Discrete Wavelet Transform (DWT)-based features and SVM classifier. In this paper, we also propose and investigate two feature extraction schemes, DWT and Stationary Wavelet Transform (SWT)-based Local Binary Pattern (LBP) features for fingerprint-based document image retrieval. The standardized Euclidean distance is employed for matching and ranking of the documents. Proposed method is tested on a database of 1200 document images and is also compared with current state-of-art. The proposed scheme provided 98.87\% of detection accuracy and 73.08\% of Mean Average Precision (MAP) for document image retrieval.", acknowledgement = ack-nhfb, articleno = "1950008", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{BinMortuza:2019:KCB, author = "Fahad {Bin Mortuza}", title = "Kernel-Coefficient-Based Feature Method for Face Detection", journal = j-INT-J-IMAGE-GRAPHICS, volume = "19", number = "2", pages = "??--??", year = "2019", DOI = "https://doi.org/10.1142/S0219467819500098", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri May 10 09:47:18 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467819500098", abstract = "A kernel-coefficient-based feature method is proposed to detect faces. The proposed method uses a mathematical expression and 26 different arrangements of kernel-coefficients of a kernel (testing region). The method manipulates the symmetric appearance of a face with respect to a rigid-kernel (fixed region). The expression, which is used to generate feature values, responds to pixels on edges of the image-objects only. For each distinct arrangement of kernel-coefficients, a feature-value is generated. The objective of the proposed kernel-coefficient-based feature method is to reduce the number of feature values required for face detection.", acknowledgement = ack-nhfb, articleno = "1950009", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Guillen-Reyes:2019:BLD, author = "Fernando O. Guill{\'e}n-Reyes and Francisco J. Dom{\'\i}nguez-Mota", title = "Boundary Layer Detection Techniques Applied to Edge Detection", journal = j-INT-J-IMAGE-GRAPHICS, volume = "19", number = "2", pages = "??--??", year = "2019", DOI = "https://doi.org/10.1142/S0219467819500104", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri May 10 09:47:18 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467819500104", abstract = "In this paper, we describe a novel algorithm for edge detection on a digital image, which is based locally on the directional averaged gradient properties of the intensity function, and produces very satisfactory results in high-resolution digital images in low execution time. Several examples show results which are comparable to those obtained by Canny and Sobel methods.", acknowledgement = ack-nhfb, articleno = "1950010", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Han:2019:RBV, author = "Myounghee Han and Yongjoo Kim and Jang Ryul Park and Benjamin J. Vakoc and Wang-Yuhl Oh and Sukyoung Ryu", title = "Retinal Blood Vessel Caliber Estimation for Optical Coherence Tomography Angiography Images Based on {$3$D} Superellipsoid Modeling", journal = j-INT-J-IMAGE-GRAPHICS, volume = "19", number = "2", pages = "??--??", year = "2019", DOI = "https://doi.org/10.1142/S0219467819500116", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri May 10 09:47:18 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467819500116", abstract = "Changes of retinal blood vessel calibers may reflect various retinal diseases and even several non-retinal diseases. We propose a new method to estimate retinal vessel calibers from 3D optical coherence tomography angiography (OCTA) images based on 3D modeling using superellipsoids. Taking advantage of 3D visualization of the retinal tissue microstructures in vivo provided by OCTA, our method can detect retinal blood vessels precisely, estimate their calibers reliably, and show the relative flow speed visually.", acknowledgement = ack-nhfb, articleno = "1950011", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Raja:2019:LPC, author = "S. P. Raja", title = "Line and Polygon Clipping Techniques on Natural Images --- A Mathematical Solution and Performance Evaluation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "19", number = "2", pages = "??--??", year = "2019", DOI = "https://doi.org/10.1142/S0219467819500128", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri May 10 09:47:18 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467819500128", abstract = "The objective of this paper is to apply clipping techniques on natural images and to analyze the performance of various clipping algorithms in computer graphics. The clipping techniques used in this paper is Cohen--Sutherland line clipping, Liang--Barsky line clipping, Nicholl--Lee--Nicholl line clipping and Sutherland--Hodgman polygon clipping. The clipping algorithms are evaluated by using the three parameters: time complexity, space complexity and image accuracy. Previously, there is no performance evaluation on clipping algorithms done. Motivating by this factor, in this paper an evaluation of clipping algorithms is made. The novelty of this paper is to apply the clipping algorithms on natural images. It is justified that the above mentioned clipping algorithms outperform well on clipping the natural images.", acknowledgement = ack-nhfb, articleno = "1950012", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Gill:2019:RES, author = "Jasmeen Gill and Akshay Girdhar and Tejwant Singh", title = "A Review of Enhancement and Segmentation Techniques for Digital Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "19", number = "3", pages = "??--??", year = "2019", CODEN = "????", DOI = "https://doi.org/10.1142/S021946781950013X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jul 23 06:58:38 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S021946781950013X", abstract = "Image enhancement and segmentation are the two imperative steps while processing digital images. The goal of enhancement is to improve the quality of images so as to nullify the effect of poor illumination conditions during image acquisition. Afterwards, segmentation is performed to extract region of interest (ROI) from the background details of the image. There is a vast literature available for both the techniques. Therefore, this paper is intended to summarize the basic as well as advanced enhancement and segmentation techniques under a single heading; to provide an insight for future researches in the field of pattern recognition.", acknowledgement = ack-nhfb, articleno = "1950013", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kumar:2019:IFB, author = "Manoj Kumar and Anuj Rani and Sangeet Srivastava", title = "Image Forensics Based on Lighting Estimation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "19", number = "3", pages = "??--??", year = "2019", DOI = "https://doi.org/10.1142/S0219467819500141", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jul 23 06:58:38 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467819500141", abstract = "Computer generated images are assumed to be a key part in each person's life in this era of information technology, where individuals effectively inhabit the advertisements, magazines, websites, televisions and many more. At the point when digital images played their role, the event of violations in terms of misrepresentation of information, use of their wrong doings winds up and also becomes easier with the help of image editing application programs. To be legitimate, if anyone does wrong anything then the proposed method can be used for a correct identification of the forgery and the imitations in the digital images. In existing techniques, researchers have suggested most well-known types of digital photographic manipulations based on source, meta-data, image copying, splicing and many more. The proposed approach is inspired by physics-based techniques and requires less human involvement. The presented approach works for images having any type of objects present in the scene, i.e. not only limited to human faces and selection of same intensity regions of the image. By assessing the lighting parameters, the proposed technique identifies the manipulated object and returns angle of incidence w.r.t light source direction. The demonstrated result produces forgery recognition rate of 92\% on an image dataset comprising of various types of manipulated images.", acknowledgement = ack-nhfb, articleno = "1950014", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Aditya:2019:ISF, author = "B. P. Aditya and U. G. K. Avaneesh and K. Adithya and Akshay Murthy and R. Sandeep and B. Kavyashree", title = "Invisible Semi-Fragile Watermarking and Steganography of Digital Videos for Content Authentication and Data Hiding", journal = j-INT-J-IMAGE-GRAPHICS, volume = "19", number = "3", pages = "??--??", year = "2019", DOI = "https://doi.org/10.1142/S0219467819500153", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jul 23 06:58:38 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/cryptography2010.bib; https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467819500153", abstract = "In the current digital age, the piracy of digital media content has become rampant. Illegal distribution of movies and video clips on a global scale causes a significant loss to the media industry. To prevent such theft and distribution of content, we use a watermarking technique for videos where copyright information is hidden inside the original video in the form of a watermark video. Using a video as the watermark facilitates the user in hiding a large amount of information. The watermarking scheme used in this paper is semi-fragile, such that tampering of videos can be detected with relative ease. To improve the robustness of the watermark, we embed the watermark in frequency domain, where we use DWT+DCT+SVD to embed the watermark. The original video and watermark video are transformed by using the DWT and DCT sequentially, then the singular values of the watermark with some embedding strength are added to the singular values of the original video thus obtaining a watermarked video. Some detection tools which are available today cannot detect the watermark video inside the original video. This method equalizes the frames of the watermark and original video to reduce time consumed as well as complexity. The effects of various attacks on the watermarked video have been analyzed using the calculated PSNR values.", acknowledgement = ack-nhfb, articleno = "1950015", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Miyazaki:2019:EVD, author = "Daisuke Miyazaki and Sayaka Taomoto and Shinsaku Hiura", title = "Extending the Visibility of Dichromats Using Histogram Equalization of Hue Value Defined for Dichromats", journal = j-INT-J-IMAGE-GRAPHICS, volume = "19", number = "3", pages = "??--??", year = "2019", DOI = "https://doi.org/10.1142/S0219467819500165", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jul 23 06:58:38 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467819500165", abstract = "Dichromats lack one of the three cone cells, which detects red, green, and blue lights. For example, red-green color blinds cannot distinguish the color between red, yellow, and green. In order to extend the ability of dichromats to recognize the color difference, we proposed a method to expand the color difference when observed by dichromats. We have defined a hue variable for dichromats and implemented to our algorithm. We applied the histogram equalization to the hue of dichromats in order to enlarge the color difference recognized by dichromats. We have applied our method to RGB color image, and shown its performance at the experimental section.", acknowledgement = ack-nhfb, articleno = "1950016", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Cheng:2019:LDC, author = "Lu Cheng and Yuan-Ke Zhang and Yun Song and Chen Li and Dao-Shun Guo", title = "Low-Dose {CT} Image Restoration Based on Adaptive Prior Feature Matching and Nonlocal Means", journal = j-INT-J-IMAGE-GRAPHICS, volume = "19", number = "3", pages = "??--??", year = "2019", DOI = "https://doi.org/10.1142/S0219467819500177", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jul 23 06:58:38 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467819500177", abstract = "Although the low-dose CT (LDCT) technique can reduce the radiation damage to patients, it will be highly detrimental to the reconstructed image quality. The normal-dose scan assisted algorithms have shown their potential in improving LDCT image quality by using a registered previously scanned normal-dose CT (NDCT) reference to regularize the corresponding LDCT target. The major drawback of such methods is the requirement of a previous patient-specific NDCT scan, which limits their clinical application. To address these problems, this paper proposed adaptive prior feature matching method for better restoration of the LDCT image. The innovation lies in construction of offline texture feature database and online adaptive prior feature matching integrated with the NLM regularization. Specifically, the prior features were extracted by the gray level co-occurrence matrix (GLCM) from regions of interest (ROIs) in existing NDCT scans of population patients. For online adaptive prior feature matching, ROIs with their texture features being similar to those of the current noisy target ROI are selected from the database as the references for the NLM regularization. The effectiveness of the proposed algorithm is validated by clinical lung cancer studies, the gain over traditional methods is noticeable in terms of both noise suppression and textures preservation.", acknowledgement = ack-nhfb, articleno = "1950017", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Veinidis:2019:EDH, author = "Christos Veinidis and Antonios Danelakis and Ioannis Pratikakis and Theoharis Theoharis", title = "Effective Descriptors for Human Action Retrieval from {$3$D} Mesh Sequences", journal = j-INT-J-IMAGE-GRAPHICS, volume = "19", number = "3", pages = "??--??", year = "2019", DOI = "https://doi.org/10.1142/S0219467819500189", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jul 23 06:58:38 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467819500189", abstract = "Two novel methods for fully unsupervised human action retrieval using 3D mesh sequences are presented. The first achieves high accuracy but is suitable for sequences consisting of clean meshes, such as artificial sequences or highly post-processed real sequences, while the second one is robust and suitable for noisy meshes, such as those that often result from unprocessed scanning or 3D surface reconstruction errors. The first method uses a spatio-temporal descriptor based on the trajectories of 6 salient points of the human body (i.e. the centroid, the top of the head and the ends of the two upper and two lower limbs) from which a set of kinematic features are extracted. The resulting features are transformed using the wavelet transformation in different scales and a set of statistics are used to obtain the descriptor. An important characteristic of this descriptor is that its length is constant independent of the number of frames in the sequence. The second descriptor consists of two complementary sub-descriptors, one based on the trajectory of the centroid of the human body across frames and the other based on the Hybrid static shape descriptor adapted for mesh sequences. The robustness of the second descriptor derives from the robustness involved in extracting the centroid and the Hybrid sub-descriptors. Performance figures on publicly available real and artificial datasets demonstrate our accuracy and robustness claims and in most cases the results outperform the state-of-the-art.", acknowledgement = ack-nhfb, articleno = "1950018", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Hamouda:2019:FAS, author = "Maissa Hamouda and Karim Saheb Ettabaa and Med Salim Bouhlel", title = "Framework for Automatic Selection of Kernels based on Convolutional Neural Networks and {CkMeans} Clustering Algorithm", journal = j-INT-J-IMAGE-GRAPHICS, volume = "19", number = "4", pages = "??--??", month = oct, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467819500190", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Feb 1 09:16:38 MST 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467819500190", abstract = "Convolutional neural networks (CNN) can learn deep feature representation for hyperspectral imagery (HSI) interpretation and attain excellent accuracy of classification if we have many training samples. Due to its superiority in feature representation, several works focus on it, among which a reliable classification approach based on CNN, used filters generated from cluster framework, like k Means algorithm, yielded good results. However, the kernels number to be manually assigned. To solve this problem, a HSI classification framework based on CNN, where the convolutional filters to be adaptatively learned from the data, by grouping without knowing the cluster number, has recently proposed. This framework, based on the two algorithms CNN and kMeans, showed high accuracy results. So, in the same context, we propose an architecture based on the depth convolutional neural networks principle, where kernels are adaptatively learned, using CkMeans network, to generate filters without knowing the number of clusters, for hyperspectral classification. With adaptive kernels, the proposed framework automatic kernels selection by CkMeans algorithm (AKSCCk) achieves a better classification accuracy compared to the previous frameworks. The experimental results show the effectiveness and feasibility of AKSCCk approach.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Montazeri:2019:MAI, author = "Mitra Montazeri", title = "Memetic Algorithm Image Enhancement for Preserving Mean Brightness Without Losing Image Features", journal = j-INT-J-IMAGE-GRAPHICS, volume = "19", number = "4", pages = "??--??", month = oct, year = "2019", DOI = "https://doi.org/10.1142/S0219467819500207", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Feb 1 09:16:38 MST 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467819500207", abstract = "In the image processing application, contrast enhancement is a major step. Conventional contrast enhancement methods such as Histogram Equalization (HE) do not have satisfactory results on many different low contrast images and they also cannot automatically handle different images. These problems result in specifying parameters manually to produce high contrast images. In this paper, an automatic image contrast enhancement on Memetic algorithm (MA) is proposed. In this study, simple exploiter is proposed to improve the current image contrast. The proposed method accomplishes multi goals of preserving brightness, retaining the shape features of the original histogram and controlling excessive enhancement rate, suiting for applications of consumer electronics. Simulation results shows that in terms of visual assessment, peak signal-to-noise (PSNR) and Absolute Mean Brightness Error (AMBE) the proposed method is better than the literature methods. It improves natural looking images specifically in images with high dynamic range and the output images were applicable for products of consumer electronic.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Nnolim:2019:ISI, author = "Uche A. Nnolim", title = "Improved Single Image De-Hazing Via Sky Region Detection, Classification and Illumination Refinement", journal = j-INT-J-IMAGE-GRAPHICS, volume = "19", number = "4", pages = "??--??", month = oct, year = "2019", DOI = "https://doi.org/10.1142/S0219467819500219", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Feb 1 09:16:38 MST 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467819500219", abstract = "This paper presents an automated sky detection technique based on statistical and fuzzy rule-based edge detection for improved hazy image contrast enhancement. This is significant since most conventional de-hazing approaches yield hazy images with over-enhanced sky regions and under-enhanced detail regions due to inability to adaptively determine and enhance such regions. Earlier and current schemes developed to remedy this issue are highly complex, usually require training with vast amount of images and manual tuning of one or several parameters. The proposed method utilizes standard deviation and fuzzy logic-based edge detection combined with thresholding algorithms to generate a homogeneity map identifying sky and non-sky regions. The areas of these regions are subsequently computed and used to obtain a homogeneity ratio. The ratio is then used to trigger a decision-based, switching scheme incorporated into a partial differential equation (PDE) de-hazing algorithm to improve results. Alternatively, a log illumination refinement method is proposed as a less complex alternative combined with the modified PDE algorithm to process hazy images without degrading sky regions, while yielding brighter images. Several image datasets from the literature were used to validate the proposed approaches and yielded mostly consistent and comparable results similar to or better than algorithms from the literature.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Khadilkar:2019:FIB, author = "Samrat P. Khadilkar and Sunil R. Das and Mansour H. Assaf and Satyendra N. Biswas", title = "Face Identification Based on Discrete Wavelet Transform and Neural Networks", journal = j-INT-J-IMAGE-GRAPHICS, volume = "19", number = "4", pages = "??--??", month = oct, year = "2019", DOI = "https://doi.org/10.1142/S0219467819500220", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Feb 1 09:16:38 MST 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib; https://www.math.utah.edu/pub/tex/bib/matlab.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467819500220", abstract = "The subject paper presents implementation of a new automatic face recognition system. To formulate an automated framework for the recognition of human faces is a highly challenging endeavor. The face identification problem is particularly very crucial in the context of today's rapid emergence of technological advancements with ever expansive requirements. It has also significant relevance in the related engineering disciplines of computer graphics, pattern recognition, psychology, image processing and artificial neural networks. This paper proposes a side-view face authentication approach based on discrete wavelet transform and artificial neural networks for the solution of the problem. A subset determination strategy that expands on the number of training samples and permits protection of the global information is discussed. The authentication technique involves image profile extraction, decomposition of the wavelets, splitting of the subsets and finally neural network verification. The procedure exploits the localization property of the wavelets in both the frequency and spatial domains, while maintaining the generalized properties of the neural networks. The realization strategy of the methodology was executed using MATLAB, demonstrating that the performance of the technique is quite satisfactory.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Mashaly:2019:PAS, author = "Ahmed S. Mashaly", title = "Performance Assessment of Sky Segmentation Approaches for {UAVs}", journal = j-INT-J-IMAGE-GRAPHICS, volume = "19", number = "4", pages = "??--??", month = oct, year = "2019", DOI = "https://doi.org/10.1142/S0219467819500232", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Feb 1 09:16:38 MST 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467819500232", abstract = "Image segmentation is one of the most challenging research fields for both image analysis and interpretation. The applications of image segmentation could be found as the primary step in various computer vision systems. Therefore, the choice of a reliable and accurate segmentation method represents a non-trivial task. Since the selected image segmentation method influences the overall performance of the remaining system steps, sky segmentation appears as a vital step for Unmanned Aerial Vehicle (UAV) autonomous obstacle avoidance missions. In this paper, we are going to introduce a comprehensive literature survey of the different types of image segmentation methodology followed by a detailed illustration of the general-purpose methods and the state-of-art sky segmentation approaches. In addition, we introduce an improved version of our previously published work for sky segmentation purpose. The performance of the proposed sky segmentation approach is compared with various image segmentation approaches using different parameters and datasets. For performance assessment, we test our approach under different situations and compare its performance with commonly used approaches in terms of several assessment indexes. From the experimental results, the proposed method gives promising results compared with the other image segmentation approaches.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Suryanarayana:2019:SIS, author = "Gunnam Suryanarayana and Ravindra Dhuli and Jie Yang", title = "Single Image Super-Resolution Algorithm Possessing Edge and Contrast Preservation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "19", number = "4", pages = "??--??", month = oct, year = "2019", DOI = "https://doi.org/10.1142/S0219467819500244", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Feb 1 09:16:38 MST 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467819500244", abstract = "In real time surveillance video applications, it is often required to identify a region of interest in a degraded low resolution (LR) image. State-of-the-art super-resolution (SR) techniques produce images with poor illumination and degraded high frequency details. In this paper, we present a different approach for SISR by correcting the dual-tree complex wavelet transform (DT-CWT) subbands using the multi-stage cascaded joint bilateral filter (MSCJBF) and singular value decomposition (SVD). The proposed method exploits geometric regularity for implementing the covariance-based interpolation in the spatial domain. We decompose the interpolated LR image into different image and wavelet coefficients by employing DT-CWT. To preserve edges, we alter the wavelet sub-bands with the high frequency details obtained from the MSCJBF. Simultaneously, we retain uniform illumination by improving the image coefficients using SVD. In addition, the wavelet sub-bands undergo Lanczos interpolation prior to the subband refinement. Experimental results demonstrate the effectiveness of our method.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Anonymous:2019:AIV, author = "Anonymous", title = "Author Index (Volume 19)", journal = j-INT-J-IMAGE-GRAPHICS, volume = "19", number = "4", pages = "??--??", month = oct, year = "2019", DOI = "https://doi.org/10.1142/S0219467819990018", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Feb 1 09:16:38 MST 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467819990018", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Nandal:2020:FOA, author = "Savita Nandal and Sanjeev Kumar", title = "Fractional-Order Anisotropic Diffusion for Defogging of {RGB} Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "20", number = "01", pages = "??--??", month = jan, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467820500011", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Feb 6 07:43:16 MST 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500011", abstract = "This paper proposes a novel and efficient algorithm for defogging of color (RGB) images. The fog in a scene is mostly due to the attenuation and airlight map, which decrease the quality of the image of the scene. To enhance such images from the visual point of view, a fractional-order anisotropic diffusion algorithm with p -Laplace norm is proposed for removing the fog effect. In particular, a coupling term is added in order to model the inter-channel correlations. The weights used in the coupling term stop the transmission of diffusion with in the edges, thus balances the inter-channel data in the diffusion procedure. Experimental results validate the better performance of the proposed algorithm over some of the existing anisotropic diffusion-based methods. The proposed method is independent of the measure of fog in the images, thus images with different amount of fog can be enhanced.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Yadav:2020:EIA, author = "Navneet Yadav and Navdeep Goel", title = "An Effective Image-Adaptive Hybrid Watermarking Scheme with Transform Coefficients", journal = j-INT-J-IMAGE-GRAPHICS, volume = "20", number = "01", pages = "??--??", month = jan, year = "2020", DOI = "https://doi.org/10.1142/S0219467820500023", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Feb 6 07:43:16 MST 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/cryptography2020.bib; https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500023", abstract = "Robust and invisible watermarking provides a feasible solution to prove the ownership of the genuine content owner. Different watermarking algorithms have been presented by the researchers in the past but no algorithm could be termed as perfect. Proposed work puts forward a novel image-adaptive method of embedding a binary watermark in the image in a transparent manner. Discrete wavelet transform (DWT), singular value decomposition (SVD) and discrete cosine transform (DCT) are used together in the proposed hybrid watermarking scheme. Image-adaptive nature of the scheme is reflected in the usage of only high entropy 8{\texttimes}8 blocks for the watermark embedding. Binary watermark is embedded in the DCT coefficients using a flexible strength derived from the means of the DCT coefficients. This flexible strength factor (SF) has different value for the DCT coefficients originated from different 8{\texttimes}8 blocks. Any desired level of visual quality could be obtained by varying the adjusting parameter of the flexible SF. Side information generated in the watermark embedding is used in the detection of watermark. The presented watermarking technique shows better robustness in comparison to the three contemporary watermarking techniques.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Ye:2020:MFM, author = "Dan Ye and Chiou-Shann Fuh", title = "{$3$D} Morphable Face Model for Face Animation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "20", number = "01", pages = "??--??", month = jan, year = "2020", DOI = "https://doi.org/10.1142/S0219467820500035", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Feb 6 07:43:16 MST 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500035", abstract = "This paper employs a new technology for modeling textured 3D faces. 3D faces can either be generated automatically from one or more photographs, or modeled directly through an intuitive user interface. Users are assisted in two key problems of computer-aided face modeling. It presents two algorithms for 3D face modeling from an image sequence. The first method works by creating an initial estimate using multiframe structure from motion (SfM) reconstruction framework, which is refined by comparing against a generic face model. The comparison is carried out using an energy-function optimization strategy. Results of 3D reconstruction algorithm are presented. The second method presented reconstructs a face model by adapting a generic model to contours of a face over all the frames of an image sequence. The algorithm for pose estimation and 3D face reconstruction relies solely on contours and the system does not require knowledge of rendering parameters (e.g. light direction and intensity). Results relying on finding accurate point correspondences across frames is presented.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Sakib:2020:RDB, author = "Mohammad Nazmus Sakib and Shuvashis Das Gupta and Satyendra N. Biswas", title = "A Robust {DWT}-Based Compressed Domain Video Watermarking Technique", journal = j-INT-J-IMAGE-GRAPHICS, volume = "20", number = "01", pages = "??--??", month = jan, year = "2020", DOI = "https://doi.org/10.1142/S0219467820500047", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Feb 6 07:43:16 MST 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/cryptography2020.bib; https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500047", abstract = "To achieve robustness and imperceptibility, an adaptive compressed domain blind video watermarking method based on Discrete Wavelet Transform (DWT) is proposed in this research. In this technique, multiple binary images derived from a single watermark image are first embedded in a video sequence. The spatial spread spectrum watermark is directly incorporated in the compressed bit streams by modifying the four sets of discrete wavelet coefficients. Comprehensive simulation experiments demonstrate that the developed approach is efficient and also robust against spatial attacks such as scaling and frame averaging, noise attacks such as Gaussian and salt pepper noise, and temporal attacks like frame dropping and shifting. Moreover, the proposed approach can also withstand against rotation attacks of arbitrary angle.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Bibi:2020:SPP, author = "Khalida Bibi and Ghazala Akram and Kashif Rehan", title = "Shape Preserving Properties with Constraints on the Tension Parameter of Binary Three-Point Approximating Subdivision Scheme", journal = j-INT-J-IMAGE-GRAPHICS, volume = "20", number = "01", pages = "??--??", month = jan, year = "2020", DOI = "https://doi.org/10.1142/S0219467820500059", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Feb 6 07:43:16 MST 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500059", abstract = "The paper analyzes conditions for preserving the shape properties from the initial data to the limit curves of the binary three-point approximating subdivision scheme. We provide suitable conditions on the initial data utilizing the tension parameter $ \omega $, thus the scheme can maintain three important shape properties, namely positivity, monotonicity and convexity in the limit curves. The use of derived conditions is illustrated in few examples, which offers more flexibility in the generation of smooth limit curves endowed with shape preserving properties.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Arora:2020:GFB, author = "Tanvi Arora and Renu Dhir", title = "Geometric Feature-Based Classification of Segmented Human Chromosomes", journal = j-INT-J-IMAGE-GRAPHICS, volume = "20", number = "01", pages = "??--??", month = jan, year = "2020", DOI = "https://doi.org/10.1142/S0219467820500060", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Feb 6 07:43:16 MST 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500060", abstract = "The chromosomes are the carriers of the geometric information, any alteration in the structure or number of these chromosomes is termed as genetic defect. These alterations cause malfunctioning in the proteins and are cause of the various underlying medical conditions that are hard to cure or detect by normal clinical procedures. In order to detect the underlying causes of these defects, the cells of the humans need to be imaged during the mitosis phase of cell division. During this phase, the chromosomes are the longest and can be easily studied and the alterations in the structure and count of the chromosomes can be analyzed easily. The chromosomes are non-rigid objects, due to which they appear in varied orientations, which makes them hard to be analyzed for the detection of structural defects. In order to detect the genetic abnormalities due to structural defects, the chromosomes need to be in straight orientation. Therefore, in this work, we propose to classify the segmented chromosomes from the metaspread images into straight, bent, touching overlapping or noise, so that the bent, touching, overlapping chromosomes can be preprocessed and straightened and the noisy objects be discarded. The classification has been done using a set of 17 different geometric features. We have proposed a Multilayer Perceptron-based classification approach to classify the chromosomes extracted from metaspread images into five distinct categories considering their orientation. The results of the classification have been analyzed using the segmented objects of the Advance Digital Imaging Research (ADIR) dataset. The proposed technique is capable of classifying the segmented chromosomes with 94.28\% accuracy. The performance of the proposed technique has been compared with seven other state-of-the-art classifiers and superior results have been achieved by the proposed method.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Girishwaingankar:2020:PNB, author = "Poorva Girishwaingankar and Sangeeta Milind Joshi", title = "The {PHY-NGSC}-Based {ORT} Run Length Encoding Scheme for Video Compression", journal = j-INT-J-IMAGE-GRAPHICS, volume = "20", number = "02", pages = "??--??", month = apr, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467820500072", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon May 11 09:44:18 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/datacompression.bib; https://www.math.utah.edu/pub/tex/bib/ijig.bib; https://www.math.utah.edu/pub/tex/bib/matlab.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500072", abstract = "This paper proposes a compression algorithm using octonary repetition tree (ORT) based on run length encoding (RLE). Generally, RLE is one type of lossless data compression method which has duplication problem as a major issue due to the usage of code word or flag. Hence, ORT is offered instead of using a flag or code word to overcome this issue. This method gives better performance by means of compression ratio, i.e. 99.75\%. But, the functioning of ORT is not good in terms of compression speed. For that reason, physical-next generation secure computing (PHY-NGSC) is hybridized with ORT to raise the compression speed. It uses an MPI-open MP programming paradigm on ORT to improve the compression speed of encoder. The planned work achieves multiple levels of parallelism within an image such as MPI and OpenMP for parallelism across a group of pictures level and slice level, respectively. At the same time, wide range of data compression like multimedia, executive files and documents are possible in the proposed method. The performance of the proposed work is compared with other methods like accordian RLE, context adaptive variable length coding (CAVLC) and context-based arithmetic coding (CBAC) through the implementation in Matlab working platform.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Raja:2020:WBI, author = "S. P. Raja", title = "Wavelet-Based Image Compression Encoding Techniques --- A Complete Performance Analysis", journal = j-INT-J-IMAGE-GRAPHICS, volume = "20", number = "02", pages = "??--??", month = apr, year = "2020", DOI = "https://doi.org/10.1142/S0219467820500084", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon May 11 09:44:18 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/datacompression.bib; https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500084", abstract = "This paper presents a complete analysis of wavelet-based image compression encoding techniques. The techniques involved in this paper are embedded zerotree wavelet (EZW), set partitioning in hierarchical trees (SPIHT), wavelet difference reduction (WDR), adaptively scanned wavelet difference reduction (ASWDR), set partitioned embedded block coder (SPECK), compression with reversible embedded wavelet (CREW) and spatial orientation tree wavelet (STW). Experiments are done by varying level of the decomposition, bits per pixel and compression ratio. The evaluation is done by taking parameters like peak signal to noise ratio (PSNR), mean square error (MSE), image quality index (IQI) and structural similarity index (SSIM), average difference (AD), normalized cross-correlation (NK), structural content (SC), maximum difference (MD), Laplacian mean squared error (LMSE) and normalized absolute error (NAE).", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Soni:2020:TRE, author = "Rituraj Soni and Bijendra Kumar and Satish Chand", title = "Text Region Extraction From Scene Images Using {AGF} and {MSER}", journal = j-INT-J-IMAGE-GRAPHICS, volume = "20", number = "02", pages = "??--??", month = apr, year = "2020", DOI = "https://doi.org/10.1142/S0219467820500096", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon May 11 09:44:18 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500096", abstract = "The natural scene images contain text as an integral part of that image that supplies paramount knowledge about it. This information and knowledge can be used in the variety of purposes like image-based searching, automatic number plate recognition, robot navigation, etc. but text region extraction and detection in scenery images could be quite a challenging job due to image blur, distortion, noise, etc. In this paper, we discuss a method for extraction of text regions by generating prospective components by applying maximally stable extremal regions (MSER) and boundary smoothing by Alternating guided image filter, which is one of the newest filters to deal with noise and halo effect elimination. The separation of non-text \& text components is achieved by AdaBoost classifier that separates text and non-text on the basis of the three text specific features namely maximum stroke width ratio, compactness, color divergence. The proposed method assist in extracting text regions from the blurred and low contrast natural scene images effectively. The ICDAR 2013 training and testing dataset is applied for the experiments and evaluation of the method. The evaluation is carried out using deteval software for calculating precision, f-measure, recall for the detected, and extracted text regions.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Nnolim:2020:PMS, author = "Uche A. Nnolim", title = "Probabilistic, Multi-Scale Fractional Tonal Correction Bilateral Filter-Based Hazy Image Enhancement", journal = j-INT-J-IMAGE-GRAPHICS, volume = "20", number = "02", pages = "??--??", month = apr, year = "2020", DOI = "https://doi.org/10.1142/S0219467820500102", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon May 11 09:44:18 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500102", abstract = "This paper describes an algorithm utilizing a modified multi-scale fractional order-based operator combined with a probabilistic tonal operator, adaptive color enhancement and bilateral filtering to process hazy and underwater images. The multi-scale algorithm complements the tonal operator by enhancing edges, preventing overexposure of bright image regions, while enhancing details in the dark areas. The addition of a previously developed global enhancement operator removes color cast and improves global contrast in underwater images. The color enhancement function augments the color results of the dehazing algorithm without distorting image intensity. Furthermore, the bilateral filter suppresses noise while preserving enhanced details/edges due to the multi-scale algorithm. Experimental results indicate that the proposed system yields comparable or better results than other algorithms from the literature.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Surajkanta:2020:RIA, author = "Yumnam Surajkanta and Shyamosree Pal", title = "Recognition of Isothetic Arc Using Number Theoretic Properties", journal = j-INT-J-IMAGE-GRAPHICS, volume = "20", number = "02", pages = "??--??", month = apr, year = "2020", DOI = "https://doi.org/10.1142/S0219467820500114", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon May 11 09:44:18 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500114", abstract = "In this paper, we propose an arc recognition method based on the number theoretic properties of isothetic covers. A definition of digital circles is given based on the dilation of Euclidean circles with unit squares. We show that a variant of the digital circle is equivalent to the grid centers between the isothetic covers of disks. Number theoretic properties of the isothetic covers of disks are explored and show that the distribution of square numbers can be used to find the run lengths of the isothetic covers. Arc recognition algorithms are developed based on the number theoretic properties.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Fuchsberger:2020:SAT, author = "Alexander Fuchsberger and Brian Ricks and Zhicheng Chen", title = "A Semi-Automated Technique for Transcribing Accurate Crowd Motions", journal = j-INT-J-IMAGE-GRAPHICS, volume = "20", number = "02", pages = "??--??", month = apr, year = "2020", DOI = "https://doi.org/10.1142/S0219467820500126", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon May 11 09:44:18 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500126", abstract = "We present a novel technique for transcribing crowds in video scenes that allows extracting the positions of moving objects in video frames. The technique can be used as a more precise alternative to image processing methods, such as background-removal or automated pedestrian detection based on feature extraction and classification. By manually projecting pedestrian actors on a two-dimensional plane and translating screen coordinates to absolute real-world positions using the cross ratio, we provide highly accurate and complete results at the cost of increased processing time. We are able to completely avoid most errors found in other automated annotation techniques, resulting from sources such as noise, occlusion, shadows, view angle or the density of pedestrians. It is further possible to process scenes that are difficult or impossible to transcribe by automated image processing methods, such as low-contrast or low-light environments. We validate our model by comparing it to the results of both background-removal and feature extraction and classification in a variety of scenes.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Omari:2020:CPA, author = "Mohammed Omari and Yamina Ouled Jaafri and Rekia Dlim", title = "Comparative Performance Analysis of Enhancement Methods Applied to {Arabic} Manuscripts", journal = j-INT-J-IMAGE-GRAPHICS, volume = "20", number = "02", pages = "??--??", month = apr, year = "2020", DOI = "https://doi.org/10.1142/S0219467820500138", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon May 11 09:44:18 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500138", abstract = "The ancient Arabic manuscripts are considered to be more complex regarding enhancement compared to others written in other languages. Complexity comes from character degradation, stains, low-quality images, curves of the text, character overlapping, etc. To facilitate the restoration, a set of well-known binarization techniques designed for historical document images is presented in this paper. Existing binarization techniques focus on either finding an appropriate global threshold for the whole image or adapting a local threshold for each area to achieve better enhancement quality. This improvement aims to remove noises, strains, uneven illumination, etc. The goal of our work is to assess these methods when applied to Arabic manuscripts in terms of readability, elimination of original spots and production of unwanted noise. Results show that no techniques work well for all types of manuscripts, but some techniques work better than others for particular types. Experimental results also indicate that Nick and Wolf techniques performed the best in terms of readability in most of the processed manuscripts.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Arunlal:2020:DIC, author = "S. L. Arunlal and N. Santhi and K. Ramar", title = "Design and Implementation of Content-Based Natural Image Retrieval Approach Using Feature Distance", journal = j-INT-J-IMAGE-GRAPHICS, volume = "20", number = "02", pages = "??--??", month = apr, year = "2020", DOI = "https://doi.org/10.1142/S021946782050014X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon May 11 09:44:18 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S021946782050014X", abstract = "Generally, the database is a gathering of data that is arranged for simple storage, retrieval and modernize. This data comprises of numerous structures like text, table, and image, outline and chart and so on. Content-based image retrieval (CBIR) is valuable for calculating the huge amount of image databases and records and for distinguishes retrieving similar images. Rather than text-based searching, CBIR effectively recovers images that are similar like query image. CBIR assumes a significant role in various areas including restorative finding, industry estimation, geographical information satellite frameworks (GIS frameworks), and biometrics; online searching and authentic research, etc. Here different medical database images are considered to the CBIR procedure is done by the proposed strategy. The proposed method considers the input features are shape, texture feature, wavelet feature, and SIFT feature. To retrieve the input image based on the features, the suggested method utilizes artificial neural network (ANN) structure. Back-propagation technique, which is an organizational structure for learning is utilized for training the neural network framework. Trial demonstrates that the proposed work improves the results of the retrieval system. From the outcomes minimizes the image retrieval time and maximum Precision 87.3\% in distance based ANN process.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Sinduja:2020:EET, author = "A. Sinduja and A. Suruliandi and S. P. Raja", title = "Empirical Evaluation of Texture Features and Classifiers for Liver Disease Diagnosis", journal = j-INT-J-IMAGE-GRAPHICS, volume = "20", number = "02", pages = "??--??", month = apr, year = "2020", DOI = "https://doi.org/10.1142/S0219467820500151", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon May 11 09:44:18 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500151", abstract = "The liver cancer is one of the most common fatal diseases worldwide, and its early detection through medical imaging is a major contributor to the reduction in mortality from certain cancer. This paves the way to work on diagnosing liver diseases effectively. An accurate diagnosis of liver disease in CT image requires an efficient description of textures and classification methods. This paper performs comparative analysis on proposed texture feature descriptor with the different existing texture features with various classifiers to classify six types of diffused and focal liver diseases. The classification of liver diseases is done in two stages. In first stage, features like segmentation based fractal texture analysis, counting label occurrence matrix, local configuration pattern, eXtended center-symmetric local binary pattern and the proposed local symmetric tetra pattern are used for extracting information from the CT liver structure and classifiers like support vector machine, k -nearest neighbor, and naive Bayes are used for classifying the pathologic liver. When pathologic conditions are detected, the best feature descriptors and classifiers are used to classify the results into any of six exclusive pathologic liver diseases, in second stage. The experiments are carried out in medically validated liver datasets containing normal and six-disease category of liver. The first experiment is analyzed using sensitivity, specificity, and accuracy. The second experiment is evaluated using precision, recall, BCR, and F-measure. The results demonstrate that the local symmetric tetra pattern with k -nearest neighbor classifier culminates in a state-of-the-art performance for diagnosing liver diseases.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kumar:2020:FFB, author = "Sandeep Kumar and L. Suresh", title = "Fruit Fly-Based Artificial Neural Network Classifier with Kernel-Based Fuzzy $c$-Means Clustering for Satellite Image Classification", journal = j-INT-J-IMAGE-GRAPHICS, volume = "20", number = "02", pages = "??--??", month = apr, year = "2020", DOI = "https://doi.org/10.1142/S0219467820500163", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon May 11 09:44:18 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500163", abstract = "Image segmentation and classification are the major challenges to satellite imagery. Also, the identification of unique objects in the satellite image is a significant aspect in the application of remote sensing. Many satellite image classification techniques have been presented earlier. However, the accuracy of the image classification has to be further improved. So that, optimal artificial neural network with kernel-based fuzzy c-means ( KFCM+OANN ) clustering based satellite image classification is proposed in this paper. Initially, the images are segmented with the help of KFCM algorithm. Then, color features and gray level co-occurrence matrix (GLCM) features to be extracted from the segmented regions. Then, these extracted features are given to the OANN classifier. Based on these features, segmented regions are classified as building, road, shadow, and tree. To enhance the performance of the classifier, the weight values are optimally selected with the help of fruit fly algorithm. Simulation results show that the performance of proposed classifier outperforms that of the existing filters in terms of accuracy.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Divakara:2020:NDI, author = "S. S. Divakara and Sudarshan Patilkulkarni and Cyril Prasanna Raj", title = "Novel {DWT\slash IDWT} Architecture for {$3$D} with Nine Stage {$2$D} Parallel Processing using Split Distributed Arithmetic", journal = j-INT-J-IMAGE-GRAPHICS, volume = "20", number = "03", pages = "??--??", month = jul, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467820500175", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed May 5 11:23:10 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500175", abstract = "Novel high-speed memory optimized distributed arithmetic (DA)-based architecture is developed and modeled for 3D discrete wavelet transform (DWT). The memory requirement for the proposed architecture is designed to 9{\texttimes}9N+36 pixel dynamic memory space and 52W ROM. The proposed 3D-DWT architecture implements 9/7 Daubechies wavelet filters, synthesizes 7127 bytes of memory for temporary storage and uses 758 adders, 36 multiplexers of 16:1 and 36 up counter to realize the 3D-DWT hardware. The 3D-DWT engine is implemented and tested in a Xilinx FPGA Vertex5 XC5VLX155T with high area and power efficiency. The maximum delay in the timing path is 2.676 ns and the 3D-DWT works at maximum frequency of 381 MHz clock.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Shrivastava:2020:ASR, author = "Neeraj Shrivastava and Jyoti Bharti", title = "Automatic Seeded Region Growing Image Segmentation for Medical Image Segmentation: a Brief Review", journal = j-INT-J-IMAGE-GRAPHICS, volume = "20", number = "03", pages = "??--??", month = jul, year = "2020", DOI = "https://doi.org/10.1142/S0219467820500187", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed May 5 11:23:10 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500187", abstract = "In the domain of computer technology, image processing strategies have become a part of various applications. A few broadly used image segmentation methods have been characterized as seeded region growing (SRG), edge-based image segmentation, fuzzy k -means image segmentation, etc. SRG is a quick, strongly formed and impressive image segmentation algorithm. In this paper, we delve into different applications of SRG and their analysis. SRG delivers better results in analysis of magnetic resonance images, brain image, breast images, etc. On the other hand, it has some limitations as well. For example, the seed points have to be selected manually and this manual selection of seed points at the time of segmentation brings about wrong selection of regions. So, a review of some automatic seed selection methods with their advantages, disadvantages and applications in different fields has been presented.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Savakar:2020:ERU, author = "Dayanand G. Savakar and Ravi Hosur", title = "The {$3$D} Emotion Recognition Using {SVM} and {HoG} Features", journal = j-INT-J-IMAGE-GRAPHICS, volume = "20", number = "03", pages = "??--??", month = jul, year = "2020", DOI = "https://doi.org/10.1142/S0219467820500199", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed May 5 11:23:10 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500199", abstract = "Emotion recognition is becoming commercially popular due to the major role of analytics in various aspects of marketing and strategy management. Several papers have been proposed in emotion recognition. They are mainly classified in the past under 2D and 3D emotion recognition, out of which 2D emotion recognition has been more popular. Various aspects like facial posture, light intensity variations and sensor-independent recognition have been studied by different authors in the past. However, in reality, 3D emotion recognition has been found to be more efficient which has a broader area of use. In this paper, a 3D tracking plane with 2D feature points has enabled us to recognize emotions by statistical voting method from all planes having over threshold number of points in their respective contour area. The proposed technique's results are comparable to existing methods in terms of time, space complexity and accuracy improvement.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Salehi:2020:UID, author = "Hadi Salehi and Javad Vahidi", title = "An Ultrasound Image Despeckling Method Based on Weighted Adaptive Bilateral Filter", journal = j-INT-J-IMAGE-GRAPHICS, volume = "20", number = "03", pages = "??--??", month = jul, year = "2020", DOI = "https://doi.org/10.1142/S0219467820500205", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed May 5 11:23:10 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500205", abstract = "Images are widely used in engineering. Unfortunately, ultrasound images are mainly degraded by an intrinsic noise called speckle. Therefore, de-speckling is a critical preprocessing step. Therefore, a robust despeckling method and accurate evaluation of images are suggested. We suggest three phases and a three-step denoising filter. In the first phase, the coefficients of variation are computed from the noisy image. The second phase is a three-step denoising filter. The first step is denoising of extreme levels of homogeneous regions, based on fuzzy homogeneous regions. The second step is a proposed adaptive bilateral filter (ABF). The ABF helps for better denoising based on the three regions which are edge, detail and homogeneous regions. The next step, a weight, is applied to the ABF. This step is for isolated noise denoising. Next, in the third phase, the output image is evaluated by the fuzzy logic approach. The proposed method is compared with other filters in the literature. The experimental outcomes show that the proposed method has better performance than the other filters. That proposed denoising algorithm is able to preserve image details and edges when compared with other denoising methods.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Nikesh:2020:DVB, author = "P. Nikesh and G. Raju", title = "Directional Vector-Based Skin Lesion Segmentation --- a Novel Approach to Skin Segmentation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "20", number = "03", pages = "??--??", month = jul, year = "2020", DOI = "https://doi.org/10.1142/S0219467820500217", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed May 5 11:23:10 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500217", abstract = "Efficient skin lesion segmentation algorithms are required for computer aided diagnosis of skin cancer. Several algorithms were proposed for skin lesion segmentation. The existing algorithms are short of achieving ideal performance. In this paper, a novel semi-automatic segmentation algorithm is proposed. The fare concept of the proposed is 8-directional search based on threshold for lesion pixel, starting from a user provided seed point. The proposed approach is tested on 200 images from PH2 and 900 images from ISBI 2016 datasets. In comparison to a chosen set of algorithms, the proposed approach gives high accuracy and specificity values. A significant advantage of the proposed method is the ability to deal with discontinuities in the lesion.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Debnath:2020:UAS, author = "Saswati Debnath and Pinki Roy", title = "User Authentication System Based on Speech and Cascade Hybrid Facial Feature", journal = j-INT-J-IMAGE-GRAPHICS, volume = "20", number = "03", pages = "??--??", month = jul, year = "2020", DOI = "https://doi.org/10.1142/S0219467820500229", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed May 5 11:23:10 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500229", abstract = "With the increasing demand for security in many fastest growing applications, biometric recognition is the most prominent authentication system. User authentication through speech and face recognition is the important biometric technique to enhance the security. This paper proposes a speech and facial feature-based multi-modal biometric recognition technique to improve the authentication of any system. Mel Frequency Cepstral Coefficients (MFCC) is extracted from audio as speech features. In visual recognition, this paper proposes cascade hybrid facial (visual) feature extraction method based on static, dynamic and key-point salient features of the face and it proves that the proposed feature extraction method is more efficient than the existing method. In this proposed method, Viola--Jones algorithm is used to detect static and dynamic features of eye, nose, lip, Scale Invariant Feature Transform (SIFT) algorithm is used to detect some stable key-point features of face. In this paper, a research on the audio-visual integration method using AND logic is also made. Furthermore, all the experiments are carried out using Artificial Neural Network (ANN) and Support Vector Machine (SVM). An accuracy of 94.90\% is achieved using proposed feature extraction method. The main objective of this work is to improve the authenticity of any application using multi-modal biometric features. Adding facial features to the speech recognition improve system security because biometric features are unique and combining evidence from two modalities increases the authenticity as well as integrity of the system.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Prashar:2020:NCA, author = "Navdeep Prashar and Meenakshi Sood and Shruti Jain", title = "Novel Cardiac Arrhythmia Processing using Machine Learning Techniques", journal = j-INT-J-IMAGE-GRAPHICS, volume = "20", number = "03", pages = "??--??", month = jul, year = "2020", DOI = "https://doi.org/10.1142/S0219467820500230", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed May 5 11:23:10 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500230", abstract = "Electrocardiography (ECG) signals provides assistance to the cardiologists for identification of various cardiovascular diseases (CVD). ECG machine records the electrical activity of the heart with the assistance of electrodes placed on the patient's body. Qualitative characterization of ECG signal reflects its sensitiveness towards distinct artifacts that resulted in low diagnostic accuracy and may lead to incorrect decision of the clinician. The artifacts are removed utilizing a robust noise estimator employing DTCWT using various threshold values and functions. The segments and intervals of ECG signals are calculated using the peak detection algorithm followed by particle swarm optimization (PSO) and the proposed optimization technique to select the best features from a considerable pool of features. Out of the 12 features, the best four features are selected using PSO and the proposed optimization technique. Comparative analysis with other feature selection methods and state-of-the-art techniques demonstrated that the proposed algorithm precisely selects principle features for handling the ECG signal and attains better classification utilizing distinctive machine learning algorithms. The obtained accuracy using our proposed optimization technique is 95.71\% employing k -NN and neural networks. Also, 4\% and 10\% improvements have been observed while using k -NN over ANN and SVM, respectively, when the PSO technique is executed. Similarly, a 14.16\% improvement is achieved while using k -NN and ANN over the SVM machine learning technique for the proposed optimization technique. Heart rate is calculated using the proposed estimator and optimization technique, which is in consensus with the gold standard.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Jiji:2020:DST, author = "G. Wiselin Jiji and A. Rajesh and P. Johnson Durai Raj", title = "Decision Support Techniques for Dermatology Using Case-Based Reasoning", journal = j-INT-J-IMAGE-GRAPHICS, volume = "20", number = "03", pages = "??--??", month = jul, year = "2020", DOI = "https://doi.org/10.1142/S0219467820500242", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed May 5 11:23:10 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500242", abstract = "Identification of skin disease has become a challenging task with the origination of various skin diseases. This paper presents a case-based reasoning (CBR) decision support system to enhance dermatological diagnosis for rural and remote communities. In this proposed work, an automated way is introduced to deal with the inconsistency problem in CBRs. This new hybrid architecture is to support the diagnosis in multiple skin diseases. The architecture used case-based reasoning terminology facilitates the medical diagnosis. Case based reasoning system retrieves the data which contains symptoms and treatment plan of the disease from the data repository by the way of matching visual contents of the image, such as shape, texture, and color descriptors. The extracted feature vector is fed into a framework to retrieve the data. The results proved using ROC curve that the proposed architecture yields high contribution to the computer-aided diagnosis of skin lesions. In experimental analysis, the system yields a specificity of 95.25\% and a sensitivity of 86.77\%. Our empirical evaluation has a superior retrieval and diagnosis performance when compared to the performance of other works.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Nisha:2020:STP, author = "S. Shajun Nisha and S. P. Raja and A. Kasthuri", title = "Static Thresholded Pulse Coupled Neural Networks in Contourlet Domain --- a New Framework for Medical Image Denoising", journal = j-INT-J-IMAGE-GRAPHICS, volume = "20", number = "03", pages = "??--??", month = jul, year = "2020", DOI = "https://doi.org/10.1142/S0219467820500254", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed May 5 11:23:10 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500254", abstract = "Image denoising, a significant research area in the field of medical image processing, makes an effort to recover the original image from its noise corrupted image. The Pulse Coupled Neural Networks (PCNN) works well against denoising a noisy image. Generally, image denoising techniques are directly applied on the pixels. From the literature review, it is reported that denoising after frequency domain transformation is performing better since noise removal is applied over the coefficients. Motivated by this, in this paper, a new technique called the Static Thresholded Pulse Coupled Neural Network (ST-PCNN) is proposed by combining PCNN with traditional filtering or threshold shrinkage technique in Contourlet Transform domain. Four different existing PCNN architectures, such as Neuromime Structure, Intersecting Cortical Model, Unit-Linking Model and Multichannel Model are considered for comparative analysis. The filters such as Wiener, Median, Average, Gaussian and threshold shrinkage techniques such as Sure Shrink, HeurShrink, Neigh Shrink, BayesShrink are used. For noise removal, a mixture of Speckle and Gaussian noise is considered for a CT skull image. A mixture of Rician and Gaussian noise is considered for MRI brain image. A mixture of Speckle and Salt and Pepper noise is considered for a Mammogram image. The Performance Metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Image Quality Index (IQI), Universal Image Quality Index (UQI), Image Enhancement Filter (IEF), Structural Content (SC), Correlation Coefficient (CC), and Weighted Signal-to-Noise Ratio (WSNR) and Visual Signal-to-Noise Ratio (VSNR) are used to evaluate the performance of denoising.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Araujo:2020:ECQ, author = "Leonardo C. Araujo and Joao P. H. Sansao and Mario C. S. Junior", title = "Effects of Color Quantization on {JPEG} Compression", journal = j-INT-J-IMAGE-GRAPHICS, volume = "20", number = "03", pages = "??--??", month = jul, year = "2020", DOI = "https://doi.org/10.1142/S0219467820500266", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed May 5 11:23:10 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/datacompression.bib; https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500266", abstract = "This paper analyzes the effects of color quantization on standard JPEG compression. Optimized color palettes were used to quantize natural images, using dithering and chroma subsampling as optional. The resulting variations on file size and quantitative quality measures were analyzed. Preliminary results, using a small image database, show that file size suffered an average 20\% increase and a concomitant loss in quality was perceived ( {\textminus} 6dB PSNR, {\textminus} 0.16 SSIM and {\textminus} 9.6 Butteraugli). Color quantization present itself as an ineffective tool on JPEG compression but if necessarily imposed, on high quality compressed images, it might lead to a negligible increase in data size and quality loss. In addition dithering seems to always decrease JPEG compression ratio.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{MacIel:2020:SOF, author = "Luiz Maur{\'\i}lio {da Silvad Maciel} and Marcelo Bernardes Vieira", title = "Sparse Optical Flow Computation Using Wave Equation-Based Energy", journal = j-INT-J-IMAGE-GRAPHICS, volume = "20", number = "04", pages = "??--??", month = oct, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467820500278", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed May 5 11:23:11 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500278", abstract = "Identification of motion in videos is a fundamental task for several computer vision problems. One of the main tools for motion identification is optical flow, which estimates the projection of the 3D velocity of the objects onto the plane of the camera. In this work, we propose a differential optical flow method based on the wave equation. The optical flow is computed by minimizing a functional energy composed by two terms: a data term based on brightness constancy and a regularization term based on energy of the wave. Flow is determined by solving a system of linear equations. The decoupling of the pixels in the solution allows solving the system by a direct or iterative approach and makes the method suitable for parallelization. We present the convergence conditions for our method since it does not converge for all the image points. For comparison purposes, we create a global video descriptor based on histograms of optical flow for the problem of action recognition. Despite its sparsity, results show that our method improves the average motion estimation, compared with classical methods. We also evaluate optical flow error measures in image sequences of a classical dataset for method comparison.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Mondal:2020:COD, author = "Ajoy Mondal", title = "Camouflaged Object Detection and Tracking: a Survey", journal = j-INT-J-IMAGE-GRAPHICS, volume = "20", number = "04", pages = "??--??", month = oct, year = "2020", DOI = "https://doi.org/10.1142/S021946782050028X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed May 5 11:23:11 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S021946782050028X", abstract = "Moving object detection and tracking have various applications, including surveillance, anomaly detection, vehicle navigation, etc. The literature on object detection and tracking is rich enough, and there exist several essential survey papers. However, the research on camouflage object detection and tracking is limited due to the complexity of the problem. Existing work on this problem has been done based on either biological characteristics of the camouflaged objects or computer vision techniques. In this paper, we review the existing camouflaged object detection and tracking techniques using computer vision algorithms from the theoretical point of view. This paper also addresses several issues of interest as well as future research direction in this area. We hope this paper will help the reader to learn the recent advances in camouflaged object detection and tracking.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Brahme:2020:EVV, author = "Aparna Brahme and Umesh Bhadade", title = "Effect of Various Visual Speech Units on Language Identification Using Visual Speech Recognition", journal = j-INT-J-IMAGE-GRAPHICS, volume = "20", number = "04", pages = "??--??", month = oct, year = "2020", DOI = "https://doi.org/10.1142/S0219467820500291", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed May 5 11:23:11 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500291", abstract = "In this paper, we describe our work in Spoken language Identification using Visual Speech Recognition (VSR) and analyze the effect of various visual speech units used to transcribe the visual speech on language recognition. We have proposed a new approach of word recognition followed by the word N-gram language model (WRWLM), which uses high-level syntactic features and the word bigram language model for language discrimination. Also, as opposed to the traditional visemic approach, we propose a holistic approach of using the signature of a whole word, referred to as a ``Visual Word'' as visual speech unit for transcribing visual speech. The result shows Word Recognition Rate (WRR) of 88\% and Language Recognition Rate (LRR) of 94\% in speaker dependent cases and 58\% WRR and 77\% LRR in speaker independent cases for English and Marathi digit classification task. The proposed approach is also evaluated for continuous speech input. The result shows that the Spoken Language Identification rate of 50\% is possible even though the WRR using Visual Speech Recognition is below 10\%, using only 1s of speech. Also, there is an improvement of about 5\% in language discrimination as compared to traditional visemic approaches.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Nair:2020:RCA, author = "Arun T. Nair and K. Muthuvel", title = "Research Contributions with Algorithmic Comparison on the Diagnosis of Diabetic Retinopathy", journal = j-INT-J-IMAGE-GRAPHICS, volume = "20", number = "04", pages = "??--??", month = oct, year = "2020", DOI = "https://doi.org/10.1142/S0219467820500308", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed May 5 11:23:11 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500308", abstract = "The medical field has been revolutionized by the medical imaging system, which plays a key role in providing information on the early life-saving detection of dreadful diseases. Diabetic retinopathy is a chronic visual disease that is the primary reason for the vision loss in most of the patients, who left undiagnosed at the initial stage. As the count of the diabetic retinopathy affected people kept on increasing, there is a necessity to have an automated detection method. The accuracy of the diagnosis of the automatic detection model is related to image acquisition as well as image interpretation. In contrast to this, the analysis of medical images by using computerized models is still a limited task. Thus, different kinds of detection methods are being developed for early detection of diabetic retinopathy. Accordingly, this paper focuses on the various literature analyses on different detection algorithms and techniques for diagnosing diabetic retinopathy. Here, it reviews several research papers and exhibits the significance of each detection method. This review deals with the analysis on the segmentation as well as classification algorithms that are included in each of the researches. Besides, the adopted environment, database collection and the tool for each of the research are portrayed. It provides the details of the performance analysis of the various diabetic detection models and reveals the best value in the case of each performance measure. Finally, it widens the research issues that can be accomplished by future researchers in the detection of diabetic retinopathy.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Rukundo:2020:NEP, author = "Olivier Rukundo", title = "Non-Extra Pixel Interpolation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "20", number = "04", pages = "??--??", month = oct, year = "2020", DOI = "https://doi.org/10.1142/S021946782050031X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed May 5 11:23:11 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S021946782050031X", abstract = "A non-extra pixel interpolation NPI is introduced for efficient image upscaling purposes. The NPI algorithm uses extended-triangular and linear scaling functions to match the pixel coordinates. The triangular function uses a modulo-operator with only two variables representing image pixels and scaling ratio. Every two variables of the linear scaling function represent the source/destination image pixels and scaling ratio. The traditional ceil function is used to round off non-integer pixel coordinates. The {\em circshift\/} and {\em padarray\/} functions are used to circularly shift the elements in array output by $k$-amount in each dimension and pad elements of the $d$-th {\em columns/rows\/} by {\em g-padsize\/} in the shifted array, respectively. The $k$, $d$ and $g$ values are determined with respect to integer scaling ratios by a vector of $n$-elements. The Exactness, Peak Signal-to-Noise Ratio, Signal-to-Noise Ratio and Discrete Fourier Transform techniques were used for objective evaluation purposes. Experiments demonstrated comparable results as well as the need for further researches.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Bania:2020:ATM, author = "Rubul Kumar Bania and Anindya Halder", title = "Adaptive Trimmed Median Filter for Impulse Noise Detection and Removal with an Application to Mammogram Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "20", number = "04", pages = "??--??", month = oct, year = "2020", DOI = "https://doi.org/10.1142/S0219467820500321", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed May 5 11:23:11 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500321", abstract = "Mammography imaging is one of the most widely used techniques for breast cancer screening and analysis of abnormalities. However, due to some technical difficulties during the time of acquisition and digital storage of mammogram images, impulse noise may be present. Therefore, detection and removals of impulse noise from the mammogram images are very essential for early detection and further diagnosis of breast cancer. In this paper, a novel {\em adaptive trimmed median filter\/} (ATMF) is proposed for impulse noise (salt & pepper (SNP)) detection and removal with an application to mammogram image denoising. Automatic switching mechanism for updating the {\em Window of Interest\/} (WoI) size from ( 3{\texttimes}3 ) to ( 5{\texttimes}5 ) or ( 7{\texttimes}7 ) is performed. The proposed method is applied on publicly available mammogram images corrupted with varying SNP noise densities in the range 5\%--90\%. The performance of the proposed method is measured by various quantitative indices like {\em peak signal to noise ratio\/} (PSNR), {\em mean square error\/} (MSE), {\em image enhancement factor\/} (IEF) and {\em structural similarity index measure\/} (SSIM). The comparative analysis of the proposed method is done with respect to other state-of-the-art noise removal methods viz., {\em standard median filter\/} (SMF), {\em decision based median filter\/} (DMF), {\em decision based unsymmetric trimmed median filter\/} (DUTMF), {\em modified decision based unsymmetric trimmed median filter\/} (MDUTMF) and {\em decision based unsymmetric trimmed winsorized mean filter\/} (DUTWMF). The superiority of the proposed method over other compared methods is well evident from the experimental results in terms of the quantitative indices (viz., PSNR, IEF and SSIM) and also from the visual quality of the denoised images. Paired {\em t-test\/} confirms the statistical significance of the higher PSNR values achieved by the proposed method as compared to the other counterpart techniques. The proposed method turned out to be very effective in denoising both high and low density noises present in (mammogram) images.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kiley:2020:WMF, author = "Matthew R. Kiley and Md Shafaeat Hossain", title = "Who are My Family Members? {A} Solution Based on Image Processing and Machine Learning", journal = j-INT-J-IMAGE-GRAPHICS, volume = "20", number = "04", pages = "??--??", month = oct, year = "2020", DOI = "https://doi.org/10.1142/S0219467820500333", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed May 5 11:23:11 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500333", abstract = "Image creation and retention are growing at an exponential rate. Individuals produce more images today than ever in history and often these images contain family. In this paper, we develop a framework to detect or identify family in a face image dataset. The ability to identify family in a dataset of images could have a critical impact on finding lost and vulnerable children, identifying terror suspects, social media interactions, and other practical applications. We evaluated our framework by performing experiments on two facial image datasets, the Y-Face and KinFaceW, comprising 37 and 920 images, respectively. We tested two feature extraction techniques, namely PCA and HOG, and three machine learning algorithms, namely {\em K\/} -Means, agglomerative hierarchical clustering, and {\em K\/} nearest neighbors. We achieved promising results with a maximum detection rate of 94.59\% using {\em K\/} -Means, 89.18\% with agglomerative clustering, and 77.42\% using {\em K\/} -nearest neighbors.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Jiji:2020:FSE, author = "G. Wiselin Jiji and A. Rajesh", title = "Food Sustenance Estimation Using Food Image", journal = j-INT-J-IMAGE-GRAPHICS, volume = "20", number = "04", pages = "??--??", month = oct, year = "2020", DOI = "https://doi.org/10.1142/S0219467820500345", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed May 5 11:23:11 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500345", abstract = "The upcoming generation is at high risk of developing many health issues like heart diseases, metabolic diseases and other life-threatening problems with high mortality as a consequence of obesity due to intake of unhealthy food which is totally deviated from a normal balanced diet with appropriate calories, proteins, vitamins and carbohydrates. In this work, the nutrient intake is calculated using food image. Our system provides efficient segmentation algorithms for separating food items from the plate. The given 2D image of food is converted into 3D image by generating its depth map for volume generation and color, texture and shape features are extracted. These features are fed as input into multi-class support vector machine classifier for learning. The learning phase involves training of various mixed and non mixed food items. The testing phase includes query image segmentation and classification for identifying the type of food and then finding calories using the nutrition data table. We have also estimated the ingredient and decay of food items. Our result shows accurate calorie estimation for various kinds of food items.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Dhariwal:2020:INW, author = "Sumit Dhariwal and Sellappan Palaniappan", title = "Image Normalization and Weighted Classification Using an Efficient Approach for {SVM} Classifiers", journal = j-INT-J-IMAGE-GRAPHICS, volume = "20", number = "04", pages = "??--??", month = oct, year = "2020", DOI = "https://doi.org/10.1142/S0219467820500357", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed May 5 11:23:11 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500357", abstract = "The content of massive image changing the brightest brightness is an impasse between most tests of sorted image realizations with low-resolution representation. I have done this research through image security, which will help curb crime in the coming days, and we propose a novel receipt for their strong and effective counterpart. Image classification using low levels of the image is a difficult method, so for this, I have adopted the method of automating the semantic image classification of this research and used it with different SVM classifiers, based on the normalized weighted feature support vector machine for semantic image classification. This is a novel approach given that weighted feature or normalized biased feature is applied and it is found that the normalized method is the best. It also uses normalized weighted features to compute kernel functions and train SVM. The trained SVM is then used to classify new images. During training and generalization, we displayed a decrease of identification error rate and there have been many benefits of using SVM with better performance in normalized image-cataloging systems. The importance of this technique and its role will be highlighted in the years to come.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Tripathi:2020:SAH, author = "Kirthi Tripathi and Harsh Sohal and Shruti Jain", title = "Statistical Analysis of {HRV} Parameters for the Detection of Arrhythmia", journal = j-INT-J-IMAGE-GRAPHICS, volume = "20", number = "04", pages = "??--??", month = oct, year = "2020", DOI = "https://doi.org/10.1142/S0219467820500369", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed May 5 11:23:11 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500369", abstract = "The repolarization and depolarization in heart generate electrical signals in the form of an ECG wave. The condition of the heart can be indicated by using Heart Rate Variability (HRV) features. In this work, FIR filter is used at the pre-processing phase for denoising, and then statistical analysis is applied for time-domain HRV feature extraction and selection. This algorithm is evaluated on different records of MIT/BIH Normal Sinus Rhythm and Arrhythmia database. The t -test implementation in both databases shows that there are significant variations in HRV features, where meanRR and HR have suggestive significant ( {0.05$<$ p}{\textlessequal}0.10 ) changes, while maxRR, minRR, maxminRR, and SDNN have strongly significant ( p{\textlessequal}0.01 ) changes. To validate the statistical analysis of HRV, feature classification has been done using SVM and kNN classifiers. A significant improvement of 2\% and 14.02\% has been observed in the overall accuracy of SVM and kNN classifiers after feature selection, respectively. These HRV features can be used for the early prediction of various Cardio-Vascular Diseases (CVD).", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Anonymous:2020:AIV, author = "Anonymous", title = "Author Index (Volume 20)", journal = j-INT-J-IMAGE-GRAPHICS, volume = "20", number = "04", pages = "??--??", month = oct, year = "2020", DOI = "https://doi.org/10.1142/S0219467820990016", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed May 5 11:23:11 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467820990016", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Zaghloul:2021:FSI, author = "Rawan I. Zaghloul and Hazem Hiary", title = "A Fast Single Image Fog Removal Method Using Geometric Mean Histogram Equalization", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "01", pages = "??--??", month = jan, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467821500017", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed May 5 11:23:12 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500017", abstract = "Fog is a natural phenomenon that affects scene visibility, it reduces the contrast of the image and causes color-fade. While various works in the literature have addressed this issue, a fast effective model is still lacking. In this paper, a single image fog removal based on Geometric Mean Histogram Equalization (GMHE) is proposed. In particular, the proposed method is composed of three steps. The primary step is to adaptively tune the performance of GMHE according to the properties of the color histogram of the foggy image. The obtained result then enters two levels of chromaticity enhancement using the Hue Saturation Value (HSV) and rotors color transformations, respectively. Extensive experiments demonstrate that the proposed method attains high performance compared to the state-of-the-art methods in terms of quality and execution time. The evaluation is performed qualitatively by visual assessment, and quantitatively using a set of full reference and no-reference-based measures. As well, we suggest an assessment criterion to combine the results of the standard measures in a single score to facilitate the comparisons between the different fog removal methods.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Ravikumar:2021:PPB, author = "K. P. Ravikumar and H. S. Manjunatha Reddy", title = "Pixel Prediction-Based Image Steganography Using Crow Search Algorithm-Based Deep Belief Network Approach", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "01", pages = "??--??", month = jan, year = "2021", DOI = "https://doi.org/10.1142/S0219467821500029", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed May 5 11:23:12 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/cryptography2020.bib; https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500029", abstract = "Securing the confidentiality of patient information using the image steganography process has gained more attention in the research community. However, embedding the patient information is a major task in the steganography process due to the complexity in identifying the pixel features. Thus, an effective Crow Search Algorithm-based deep belief network (CSA-DBN) is proposed for embedding the information in the medical image. Initially, the appropriate pixels and the features, like pixel coverage, wavelet energy, edge information, and texture features, such as local binary pattern (LBP) and local directional pattern (LDP), are extracted from each pixel. The proposed CSA-DBN utilizes the feature vector and identifies the suitable pixels used for embedding. The patient information is embedded into the image by using the embedding strength and the DWT coefficient. Finally, the embedded information is extracted using the DWT coefficient. The analysis of the proposed CSA-DBN approach is done based on the performance metrics, such as correlation coefficient, peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM) that acquired the average values as 0.9471, 24.836 dB, and 0.4916 in the presence of salt and pepper noise and 0.9741, 57.832 dB, and 0.9766 in the absence of noise.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Jagdale:2021:MRO, author = "Rohita H. Jagdale and Sanjeevani K. Shah", title = "Modified Rider Optimization-Based {V} Channel Magnification for Enhanced Video Super Resolution", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "01", pages = "??--??", month = jan, year = "2021", DOI = "https://doi.org/10.1142/S0219467821500030", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed May 5 11:23:12 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500030", abstract = "In video Super Resolution (SR), the problem of cost expense concerning the attainment of enhanced spatial resolution, computational complexity and difficulties in motion blur makes video SR a complex task. Moreover, maintaining temporal consistency is crucial to achieving an efficient and robust video SR model. This paper plans to develop an intelligent SR model for video frames. Initially, the video frames in RGB format will be transformed into HSV. In general, the improvement in video frames is done in V-channel to achieve High-Resolution (HR) videos. In order to enhance the RGB pixels, the current window size is enhanced to high-dimensional window size. As a novelty, this paper intends to formulate a high-dimensional matrix with enriched pixel intensity in V-channel to produce enhanced HR video frames. Estimating the enriched pixels in the high-dimensional matrix is complex, however in this paper, it is dealt in a significant way by means of a certain process: (i) motion estimation (ii) cubic spline interpolation and deblurring or sharpening. As the main contribution, the cubic spline interpolation process is enhanced via optimization in terms of selecting the optimal resolution factor and different cubic spline parameters. For optimal tuning, this paper introduces a new modified algorithm, which is the modification of the Rider Optimization Algorithm (ROA) named Mean Fitness-ROA (MF-ROA). Once the HR image is attained, it combines the HSV and converts to RGB, which obtains the enhanced output RGB video frame. Finally, the performance of the proposed work is compared over other state-of-the-art models with respect to BRISQUE, SDME and ESSIM measures, and proves its superiority over other models.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Hassan:2021:CEM, author = "Mohd Fikree Hassan", title = "Color Enhancement Method to Improve the Colors of the Images Perceived by the Elderly People", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "01", pages = "??--??", month = jan, year = "2021", DOI = "https://doi.org/10.1142/S0219467821500042", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed May 5 11:23:12 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500042", abstract = "Smartphones and tablets present data and information in color images. Due to factors such as yellowing pigmentation and miosis filter, elderly people may experience difficulties and confusion when looking at the color images on smartphones and tablets. In this paper, we propose a color enhancement method to improve the color perceived by elderly people. This method is based on the color perception of the elderly simulated using the uniform yellowing pigmentation method. The proposed method enhances the colors of the images to compensate for the effect of yellowing pigmentation and miosis filter. This is achieved by utilizing the error parameters between the original colors and colors perceived by the elderly. Implementing an adaptation matrix, the error parameters are modified and distributed back into the original colors iteratively. Experimental results showed that the proposed method improves the colors perceived by the elderly.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Jiji:2021:CRF, author = "G. Wiselin Jiji and A. Rajesh and P. Johnson Durai Raj", title = "{CBI + R}: a Fusion Approach to Assist Dermatological Diagnoses", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "01", pages = "??--??", month = jan, year = "2021", DOI = "https://doi.org/10.1142/S0219467821500054", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed May 5 11:23:12 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500054", abstract = "With the emerge of advanced technologies such as high-resolution cameras and computational power, it seems to ease to built a better dermatological diagnostic system. However, the identification of skin disease is still a challenging problem with the origination of various skin diseases. In this paper, we proposed a new fusion architecture --- CBI + R to support the diagnosis in multiple skin diseases. The architecture combines Content-Based Image Retrieval (CBIR) and Case-Based Reasoning (CBR) technology together to facilitate medical diagnosis. CBIR is to retrieve digital dermoscopy images from a data repository using the shape, texture and color features. Along with these features, CBR is incorporated which contains symptoms, case history and treatment plan of the disease. Experiments on a set of 1210 images yielded an accuracy of 98.2\%. This was a superior retrieval and diagnosis performance in comparison with the state-of-the-art works.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Joshi:2021:SDI, author = "Anand B. Joshi and Dhanesh Kumar and D. C. Mishra", title = "Security of Digital Images Based on {$3$D} {Arnold} Cat Map and Elliptic Curve", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "01", pages = "??--??", month = jan, year = "2021", DOI = "https://doi.org/10.1142/S0219467821500066", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed May 5 11:23:12 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/cryptography2020.bib; https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500066", abstract = "Security of digital data is an important task in the present era. In this paper, we propose a new scheme of digital image encryption and decryption method based on three-dimensional (3D) Arnold cat map (ACM) and elliptic curve. In this proposed encryption method, we have applied 3D ACM on the digital color image which performs the dual encryption first, it performs the permutation and second, it performs the substitution of image pixels. After that, elliptic curve cryptography (ECC) is used to encrypt the image, for this a mapping method is proposed to convert the pixels of the image as points on the elliptic curve. Further, a mapping inverting method is proposed for decryption and then 3D inverse Arnold cat map (iACM) is applied to get the original image. The statistical and security analyses are done on various images and the experimental results show the robustness of the proposed method.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Akrour:2021:FHI, author = "Leila Akrour and Soltane Ameur and Mourad Lahdir and R{\'e}gis Fournier and Amine Nait Ali", title = "Fast Hyperspectral Image Encoder Based on Supervised Multimodal Scheme", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "01", pages = "??--??", month = jan, year = "2021", DOI = "https://doi.org/10.1142/S0219467821500078", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed May 5 11:23:12 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500078", abstract = "Many compression methods, lossy or lossless, were developed for 3D hyperspectral images, and various standards have emerged and applied to these amounts of data in order to achieve the best rate-distortion performance. However, high-dimensional data volume of hyperspectal images is problematic for compression and decompression time. Nowadays, fast compression and especially fast decompression algorithms are of primary importance in image data applications. In this case, we present a lossy hyperspectral image compression based on supervised multimodal scheme in order to improve the compression results. The supervised multimodal method is used to reduce the amount of data before their compression with the 3D-SPIHT encoder based on 3D wavelet transform. The performance of the Supervised Multimodal Compression (SMC-3D-SPIHT encoder) has been evaluated on AVIRIS hyperspectral images. Experimental results indicate that the proposed algorithm provides very promising performance at low bit-rates while reducing the encoding/decoding time.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Suryanarayana:2021:SRB, author = "Gunnam Suryanarayana and Kandala N. V. P. S. Rajesh and Jie Yang", title = "Super-Resolution Based on Residual Learning and Optimized Phase Stretch Transform", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "01", pages = "??--??", month = jan, year = "2021", DOI = "https://doi.org/10.1142/S021946782150008X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed May 5 11:23:12 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S021946782150008X", abstract = "High resolution infrared (IR) images are often required in military and industrial applications. Due to the limited properties of IR imaging sensors and camera lens, IR images exhibit poor spatial resolution with a blur phenomenon in the edge regions. In this correspondence, we develop a new super-resolution (SR)-IR image reconstruction method using the residual learning network in the wavelet domain (WRESNET) and optimized phase stretch transform (PST). Our algorithm first transforms the input low resolution (LR)-IR image into its low-frequency and high-frequency subbands using the discrete wavelet decomposition. Subsequently, we introduce the optimized PST to operate on the LR-IR image and extract the intrinsic edge structure. The PST behaves differently at low-frequency and high-frequency regions, thus capturing the intensity variations for edge detection. We incorporate the PST extracted edge map in the wavelet subbands to preserve the intrinsic structure of images. The resultant subbands are further refined based on the missing residuals obtained using the WRESNET. The proposed method is validated through quantitative and qualitative evaluations against the conventional and state-of-art SR methods. Results reveal that the proposed method outperforms the existing methods.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Pradhan:2021:ALE, author = "Ashis Pradhan and Mohan P. Pradhan", title = "Automatic Localization of Elevation Values in a Poor Quality Topographic Map", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "01", pages = "??--??", month = jan, year = "2021", DOI = "https://doi.org/10.1142/S0219467821500091", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed May 5 11:23:12 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500091", abstract = "A topographic sheet hosts various morphological features that effectively describe the terrain. This multi-faced information content not only elevates human perception but also provides ample direction for research initiatives. Out of all possible attributes based on utility, contours have wide set of application. A contour is characterized by its coordinate system and most importantly, its elevation detail. Upon, successful attainment of these two attributes, creating a fully automatic 3D projection system may be achieved with relative ease. In contrast to the traditional manual approach, this research initiative puts forward a novel mechanism for automatically localizing contour and its attributes including coordinate pattern and elevation value in a referenced map. To accomplish the aforementioned objectives, the proposed mechanism relies on various image processing techniques based on morphological operations. Further, the extracted details can be used to project the contours in a 3D space. This projection is also called Digital Elevation Model (DEM). DEM is crucial for various applications such as Terrain Modeling, Hydrological Modeling, Path Optimization, to name a few. Automatically and accurately created DEM from topographic sheet could contribute a lot in many Geographical Information System (GIS) applications. This paper focuses mainly on elevation value localization associated with specific contour.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Silva:2021:CMA, author = "Rodrigo Dalvit C. Silva and Thomas R. Jenkyn", title = "Classification of Mammogram Abnormalities Using {Legendre} Moments", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "01", pages = "??--??", month = jan, year = "2021", DOI = "https://doi.org/10.1142/S0219467821500108", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed May 5 11:23:12 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500108", abstract = "In this paper, the issue of classifying mammogram abnormalities using images from an mammogram image analysis society (MIAS) database is discussed. We compare a feature extractor based on Legendre moments (LMs) with six other feature extractors. To determine the best feature extractor, the performance of each was compared in terms of classification accuracy rate and extraction time using a k -nearest neighbors ( k -NN) classifier. This study shows that feature extraction using LMs performed best with an accuracy rate over 84\% and requiring relatively little time for feature extraction, on average only 1 s.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Jiji:2021:DPD, author = "G. Wiselin Jiji and A. Rajesh and P. Johnson Durai Raj", title = "Diagnosis of {Parkinson}'s Disease Using {SVM} Classifier", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "02", pages = "??--??", month = apr, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1142/S021946782150011X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed May 5 11:23:13 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S021946782150011X", abstract = "Parkinson's disease (PD) is the most common disease that affects aged people which leads to dopamine-producing cells in substantia nigra to be damaged when motor system degenerates. Clinical Diagnosis of Parkinson's disease at the earlier stage is very difficult. This work is carried out to find the significance of cognition function of basal ganglia (BG) region and speech data values. The BG can be segmented using morphological operation and active contour algorithm. Co-occurrences features are extracted and out of 720 features, the promising 110 features are selected using variance method. More promising 22 features are selected in speech data and both features are individually classified using SVM to find out the efficiency in Diagnosis. The outcome shows cognition function of BG performing a major role in early diagnosis of Parkinson's disease when compared to speech data.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Boudraa:2021:ECS, author = "Omar Boudraa and Walid Khaled Hidouci and Dominique Michelucci", title = "An Efficient Cooperative Smearing Technique for Degraded Historical Document Image Segmentation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "02", pages = "??--??", month = apr, year = "2021", DOI = "https://doi.org/10.1142/S0219467821500121", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed May 5 11:23:13 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500121", abstract = "Segmentation is one of the critical steps in historical document image analysis systems that determines the quality of the search, understanding, recognition and interpretation processes. It allows isolating the objects to be considered and separating the regions of interest (paragraphs, lines, words and characters) from other entities (figures, graphs, tables, etc.). This stage follows the thresholding, which aims to improve the quality of the document and to extract its background from its foreground, also for detecting and correcting the skew that leads to redress the document. Here, a hybrid method is proposed in order to locate words and characters in both handwritten and printed documents. Numerical results prove the robustness and the high precision of our approach applied on old degraded document images over four common datasets, in which the pair (Recall, Precision) reaches approximately 97.7\% and 97.9\%.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Khosravi:2021:NIR, author = "Javanshir Khosravi and Mohammad Shams Esfand Abadi and Reza Ebrahimpour", title = "A Novel Iterative Rigid Image Registration Algorithm Based on the {Newton} Method", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "02", pages = "??--??", month = apr, year = "2021", DOI = "https://doi.org/10.1142/S0219467821500133", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed May 5 11:23:13 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500133", abstract = "In recent years, Image Registration has attracted lots of attention due to its capabilities and numerous applications. Various methods have been exploited to map two images with the same concept but different conditions. Considering the finding of the mentioned map as an optimization problem, mathematical-based optimization methods have been extensively employed due to their real-time performances. In this paper, we employed the Newton method to optimize two defined cost functions. These cost functions are Sum of Square Difference and Cross-Correlation. These presented algorithms have fast convergence and accurate features. Also, we propose an innovative treatment in order to attend to one of the free parameter-rotations or scale as a sole variable and the other one as the constant value. The assignment is replaced through the iterations for both parameters. The intuition is to turn a two-variable optimization problem into a single variable one in every step. Our simulation on benchmark images by the means of Root Mean Square Error and Mutual Information as the goodness criteria, that have been extensively used in similar studies, has shown the robustness and affectivity of the proposed method.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Poonkuntran:2021:SIC, author = "S. Poonkuntran and P. Alli and T. M. Senthil Ganesan and S. Manthira Moorthi and M. P. Oza", title = "Satellite Image Classification Using Cellular Automata", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "02", pages = "??--??", month = apr, year = "2021", DOI = "https://doi.org/10.1142/S0219467821500145", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed May 5 11:23:13 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500145", abstract = "The satellite image classification plays a vital role in remote sensing for analyzing the images and recognizing the patterns. Supervised classification is one of the methods in which pixels of an image are grouped based on training samples. The uncertainty is one of the major issues in a supervised classification, where the pixel is classified into more than one class. This is happened due to the use of spectral values without considering contextual values in classification. Hence, this paper proposes Cellular Automata (CA)-based Classifier for Satellite Images Classification, where spectral values are combined with contextual values to improve the accuracy of the classifier. The proposed CA classifier combines the spectral values with contextual values in iteration until the uncertainty is resolved. Thereby, the proposed scheme improves the accuracy of the classical supervised classifier of parallel piped, minimum distance, DSVM and KNN classifier by 7\% at an average.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Siddiqui:2021:CBV, author = "Tanveer J. Siddiqui and Ashish Khare", title = "Chaos-based Video Steganography Method in Discrete Cosine Transform Domain", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "02", pages = "??--??", month = apr, year = "2021", DOI = "https://doi.org/10.1142/S0219467821500157", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed May 5 11:23:13 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500157", abstract = "Due to the technological advancements in digital communication, the amount of multimedia content over the internet has increased manifold in past decade. This has renewed the internet of researchers in the area of privacy and secure communication. This paper presents a secure and robust video steganography method in discrete cosine transform (DCT) domain. In order to enhance the security of the proposed algorithm, the frame selection process is randomized and the secret data are pre-treated using Arnold's cat map. The secret data are embedded in the middle band DCT coefficient using two pseudo random sequences. These sequences are generated using a chaotic map. We analyze the proposed algorithm in terms of peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), multi-scale structural similarity index (MSSIM) and video quality metric (VQM). The evaluation has been done on 107 video sequences. The experimental results demonstrate that the algorithm maintains acceptable video quality. The robustness of the proposed method is tested under Gaussian and salt and pepper noise attack using correlation between original and recovered images. The proposed algorithm is able to recover 90.60\% data without error under salt and pepper noise ( D=0.001 ) attack and 87.23\% data correctly under Gaussian noise attack with mean $ = 0 $ and variance $ = 0.001 $.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Jameel:2021:MLT, author = "Samer Kais Jameel and Sezgin Aydin and Nebras H. Ghaeb", title = "Machine Learning Techniques for Corneal Diseases Diagnosis: a Survey", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "02", pages = "??--??", month = apr, year = "2021", DOI = "https://doi.org/10.1142/S0219467821500169", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed May 5 11:23:13 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500169", abstract = "Machine learning techniques become more related to medical researches by using medical images as a dataset. It is categorized and analyzed for ultimate effectiveness in diagnosis or decision-making for diseases. Machine learning techniques have been exploited in numerous researches related to corneal diseases, contribution to ophthalmologists for diagnosing the diseases and comprehending the way automated learning techniques act. Nevertheless, confusion still exists in the type of data used, whether it is images, data extracted from images or clinical data, the course reliant on the type of device for obtaining them. In this study, the researches that used machine learning were reviewed and classified in terms of the kind of utilized machine for capturing data, along with the latest updates in sophisticated approaches for corneal disease diagnostic techniques.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Budhiraja:2021:IVI, author = "Sumit Budhiraja and Iftisam Rummy and Sunil Agrawal and Balwinder Singh Sohi", title = "Infrared and Visible Image Fusion Based on Sparse Representation and Spatial Frequency in {DTCWT} Domain", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "02", pages = "??--??", month = apr, year = "2021", DOI = "https://doi.org/10.1142/S0219467821500170", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed May 5 11:23:13 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500170", abstract = "Infrared and visible image fusion is a key area of research in multi-sensor image fusion. The main purpose of this fusion is to combine thermal information of the infrared image and texture information of the visible image. This paper presents an image fusion framework, based on parallel arrangement of sparse representation (SR) and spatial frequency (SF). In the proposed framework, an efficient edge-aware filter, i.e. guided filter, is first employed on the visible image. Then dual-tree complex wavelet transform (DTCWT) is used to obtain low-pass and high-pass coefficients of images, as it is shift-invariant and has high directional selectivity. The low-pass coefficients are fused using the SR- and SF-based fusion rules in parallel, which enhances the regional features of the images. The simulation results show that the proposed technique has better performance when compared with conventional techniques in both subjective and objective evaluations.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Choudhary:2021:MBB, author = "Swati K. Choudhary and Ameya K. Naik", title = "Multimodal Biometric-Based Authentication with Secured Templates", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "02", pages = "??--??", month = apr, year = "2021", DOI = "https://doi.org/10.1142/S0219467821500182", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed May 5 11:23:13 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500182", abstract = "This paper proposes a multimodal biometric based authentication (verification and identification) with secured templates. Multimodal biometric systems provide improved authentication rate over unimodal systems at the cost of increased concern for memory requirement and template security. The proposed framework performs person authentication using face and fingerprint. Biometric templates are protected by hiding fingerprint into face at secret locations, through blind and key-based watermarking. Face features are extracted from approximation sub-band of Discrete Wavelet Transform, which reduces the overall working plane. The proposed method also shows high robustness of biometric templates against common channel attacks. Verification and identification performances are evaluated using two chimeric and one real multimodal dataset. The same systems, working with compressed templates provides considerable reduction in overall memory requirement with negligible loss of authentication accuracies. Thus, the proposed framework offers positive balance between authentication performance, template robustness and memory resource utilization.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Pathade:2021:SMC, author = "Manasi Pathade and Madhuri Khambete", title = "Supervised Method for Congestion Detection at Entry and Exit Corridors of Public Places", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "02", pages = "??--??", month = apr, year = "2021", DOI = "https://doi.org/10.1142/S0219467821500194", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed May 5 11:23:13 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500194", abstract = "Continuous monitoring and automatic detection of crowd activities is extremely helpful for management at public places to avoid any possible disaster. Analysis of crowded scene is a critical task as it typically involves poor resolution of objects, occlusions and complex dynamics. In this paper, we propose a novel, systematic and generalized method based on global motion analysis of people to detect Congestion situation in crowded scenes at entry/exit corridors. Our approach is tested on video footages acquired from surveillance cameras installed at exit corridors of public places. The results show the expediency of our approach.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Manchanda:2021:ICB, author = "Meenu Manchanda and Deepak Gambhir", title = "Improvement in {CNN}-Based Multifocus Image Fusion Algorithm with Triangulated Fuzzy Filter", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "02", pages = "??--??", month = apr, year = "2021", DOI = "https://doi.org/10.1142/S0219467821500200", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed May 5 11:23:13 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500200", abstract = "Multifocus image fusion is a demanding research field due to the utilization of modern imaging devices. Generally, the scene to be captured contains objects at different distances from these devices and so a set of multifocus images of the scene is captured with different objects in-focus. However, to improve the situational awareness of the captured scene, these sets of images are required to be fused together. Therefore, a multifocus image fusion algorithm based on Convolutional Neural Network (CNN) and triangulated fuzzy filter is proposed. A CNN is used to extract information regarding focused pixels of input images and the same is used as fusion rule for fusing the input images. The focused information so extracted may still need to be refined near the boundaries. Therefore, asymmetrical triangular fuzzy filter with the median center (ATMED) is employed to correctly classify the pixels near the boundary. The advantage of using this filter is to rely on precise detection results since any misdetection may considerably degrade the fusion quality. The performance of the proposed algorithm is compared with the state-of-art image fusion algorithms, both subjectively and objectively. Various parameters such as edge strength ( Q ), fusion loss (FL), fusion artifacts (FA), entropy ( H ), standard deviation (SD), spatial frequency (SF), structural similarity index measure (SSIM) and feature similarity index measure (FSIM) are used to evaluate the performance of the proposed algorithm. Experimental results proved that the proposed fusion algorithm produces a fused image that contains all-in-one focused pixels and is better than those obtained using other popular and latest image fusion works.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Sharma:2021:RIB, author = "Urvashi Sharma and Meenakshi Sood and Emjee Puthooran", title = "Region of Interest-Based Coding Technique of Medical Images Using Varying Grading of Compression", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "02", pages = "??--??", month = apr, year = "2021", DOI = "https://doi.org/10.1142/S0219467821500212", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed May 5 11:23:13 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/datacompression.bib; https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500212", abstract = "A region of interest (ROI)-based compression method for medical image datasets is a requirement to maintain the quality of the diagnostically important region of the image. It is always a better option to compress the diagnostic important region in a lossless manner and the remaining portion of the image with a near-lossless compression method to achieve high compression efficiency without any compromise of quality. The predictive ROI-based compression on volumetric CT medical image is proposed in this paper; resolution-independent gradient edge detection (RIGED) and block adaptive arithmetic encoding (BAAE) are employed to ROI part for prediction and encoding that reduce the interpixel and coding redundancy. For the non-ROI portion, RIGED with an optimal threshold value, quantizer with optimal q -level and BAAE with optimal block size are utilized for compression. The volumetric 8-bit and 16-bit standard CT image dataset is utilized for the evaluation of the proposed technique, and results are validated on real-time CT images collected from the hospital. Performance of the proposed technique in terms of BPP outperforms existing techniques such as JPEG 2000, M-CALIC, JPEG-LS, CALIC and JP3D by 20.31\%, 19.87\%, 17.77\%, 15.58\% and 13.66\%, respectively.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Panwar:2021:FES, author = "Kirtee Panwar and Ravindra Kumar Purwar and Garima Srivastava", title = "A Fast Encryption Scheme Suitable for Video Surveillance Applications Using {SHA-256} Hash Function and {$1$D} Sine--Sine Chaotic Map", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "02", pages = "??--??", month = apr, year = "2021", DOI = "https://doi.org/10.1142/S0219467821500224", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed May 5 11:23:13 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/cryptography2020.bib; https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500224", abstract = "This paper proposes an image encryption technique which is fast and secure. The encryption scheme is designed for secure transmission of video surveillance data (keyframes) over insecure network. The image encryption technique employs 1D Sine--Sine system with better chaotic properties than its seed map and faster than higher-dimensional chaotic systems. Further, design of encryption scheme is based on two permutation rounds, which employs pixel swapping operation and diffusion operation which is simple and provides required security against plaintext, differential and various other attacks. Three separate chaotic sequences are generated using 1D Sine--Sine system which enhances the key space of the encryption scheme. Secret keys are updated dynamically with SHA-256 hash value obtained from plain image. Hash values of plain image are efficiently used without loss of any hash value information. This makes the encryption scheme plaintext sensitive and secure against plaintext attacks. Performance and security aspects of encryption scheme is analyzed both quantitatively using predefined security metrics and qualitatively by scrutinizing the internal working of encryption scheme. Computational complexity of encrypting a plain image of size \( rows{\texttimes} columns \) is {$ \mathcal {O} $} \( rows{\texttimes}columns \) and is suitable for encrypting keyframes of video for secure surveillance applications.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kamath:2021:DSI, author = "Priya R. Kamath and Kedarnath Senapati and P. Jidesh", title = "Despeckling of {SAR} Images Using Shrinkage of Two-Dimensional Discrete Orthonormal {$S$}-Transform", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "02", pages = "??--??", month = apr, year = "2021", DOI = "https://doi.org/10.1142/S0219467821500236", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed May 5 11:23:13 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500236", abstract = "Speckles are inherent to SAR. They hide and undermine several relevant information contained in the SAR images. In this paper, a despeckling algorithm using the shrinkage of two-dimensional discrete orthonormal S-transform (2D-DOST) coefficients in the transform domain along with shock filter is proposed. Also, an attempt has been made as a post-processing step to preserve the edges and other details while removing the speckle. The proposed strategy involves decomposing the SAR image into low and high-frequency components and processing them separately. A shock filter is used to smooth out the small variations in low-frequency components, and the high-frequency components are treated with a shrinkage of 2D-DOST coefficients. The edges, for enhancement, are detected using a ratio-based edge detection algorithm. The proposed method is tested, verified, and compared with some well-known models on C-band and X-band SAR images. A detailed experimental analysis is illustrated.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kalaivani:2021:EBI, author = "A. Kalaivani and K. Swetha", title = "An Enhanced Bidirectional Insertion Sort Over Classical Insertion Sort", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "02", pages = "??--??", month = apr, year = "2021", DOI = "https://doi.org/10.1142/S0219467821500248", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed May 5 11:23:13 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500248", abstract = "Sorting is a technique which is used to arrange the data in specific order. A sorting technique is applied to rearrange the elements in numerical order as ascending order or descending order or for words in alphabetical order. In this paper, we propose an efficient sorting algorithm known as Enhanced Bidirectional Insertion Sorting algorithm which is developed from insertion sort concept. A comparative analysis is done for the proposed Enhanced Bidirectional Insertion Sort algorithm with the selection sort and insertion sort algorithms. When compared to insertion sort algorithm the proposed algorithm outperforms with less number of comparisons in worst case and average case computing time. The proposed algorithm works efficiently for duplicated elements which is the advanced improvement and the results are proved.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Shaikh:2021:STB, author = "Ayesha S. Shaikh and Vibha D. Patel", title = "Significance of the Transition to Biometric Template Protection: Explore the Future", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "02", pages = "??--??", month = apr, year = "2021", DOI = "https://doi.org/10.1142/S021946782150025X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed May 5 11:23:13 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S021946782150025X", abstract = "The IT security paradigm evolves from secret-based to biometric identity-based. Biometric identification has gradually become more popular in recent years for handheld devices. Privacy-preserving is a key concern when biometrics is used in authentication systems in the present world today. Nowadays, the declaration of biometric traits has been imposed not only by the government but also by many private entities. There are no proper mechanisms and assurance that biometric traits will be kept safe by such entities. The encryption of biometric traits to avoid privacy attacks is a giant problem. Hence, state-of-the-art safety and security technological solutions must be devised to prevent the loss and misuse of such biometric traits. In this paper, we have identified different cancelable biometrics methods with the possible attacks on the biometric traits and directions on possible countermeasures in order to design a secure and privacy-preserving biometric authentication system. We also proposed a highly secure method for cancelable biometrics using a non-invertible function based on Discrete Cosine Transformation and Index of max hashing. We tested and evaluated the proposed novel method on a standard dataset and achieved good results.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kiran:2021:CSI, author = "S. Shashi Kiran and K. V. Suresh", title = "Challenges in Sparse Image Reconstruction", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "03", pages = "??--??", month = jul, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467821500261", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon Jul 5 15:21:12 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500261", abstract = "Handling huge amount of data from different sources more so in the images is the latest challenge. One of the solutions to this is sparse representation. The idea of sparsity has been receiving much attention recently from many researchers in the areas such as satellite image processing, signal processing, medical image processing, microscopy image processing, pattern recognition, neuroscience, seismic imaging, etc. Many algorithms have been developed for various areas of sparse representation. The main objective of this paper is to provide a comprehensive study and highlight the challenges in the area of sparse representation which will be helpful for researchers. Also, the current challenges and opportunities of applying sparsity to image reconstruction, namely, image super-resolution, image denoising and image restoration are discussed. This survey on sparse representation categorizes the existing methods into three groups: dictionary learning approach, greedy strategy approximation approach and deep learning approach.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Sowmyayani:2021:MTC, author = "S. Sowmyayani and V. Murugan", title = "Multi-Type Classification Comparison of Mammogram Abnormalities", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "03", pages = "??--??", month = jul, year = "2021", DOI = "https://doi.org/10.1142/S0219467821500273", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon Jul 5 15:21:12 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500273", abstract = "Cancer is a life-threatening disease which reduces the lifespan of humans. If the disease is treated early, the lifespan can be extended. This paper provides a useful method for detecting the abnormalities in the mammograms. The proposed method uses four phases such as pre-processing, segmentation, feature extraction and classification. In the pre-processing phase, median filter is utilized to enhance the quality of an image. The pre-processed image is then segmented by fuzzy C means (FCM). Three different features such as Gaussian--Hermite moments (GHM), Jacobi moments and pseudo Zernike moments (PZM) are extracted from the segmented image. Finally, extreme learning machine (ELM) classifier identifies the normal, malignant and benign kinds of cancer. This method is compared with four different classifiers. The proposed method is tested on mammographic image analysis society (MIAS) dataset and the performance is evaluated against several analogous approaches in terms of accuracy, sensitivity and specificity. The proposed approach substantially provides the best result.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Mondal:2021:SGS, author = "Md. Abdul Mannan Mondal and Mohammad Haider Ali", title = "Self-guided Stereo Correspondence Estimation Algorithm", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "03", pages = "??--??", month = jul, year = "2021", DOI = "https://doi.org/10.1142/S0219467821500285", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon Jul 5 15:21:12 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500285", abstract = "This paper introduces an innovative algorithm, ``Self-guided Stereo Correspondence'' (SGSC), that is directed by photometric properties of the candidate pixels. As the photometric properties of reference image (left image) pixel and its neighbor's pixel are similar in most cases, so the upcoming corresponding pixel exists in the surrounding of the previous matching pixel. Searching performance is greatly improved by utilizing this photometric property of the candidate pixels. The searching performance is further improved by applying the pioneering threshold technique. These two key techniques sufficiently reduced the computational cost with no degradation of accuracy. The achievements of the proposed method are testified on Middlebury standard Stereo Datasets of 2003 and 2006 and the Middlebury latest Optical Flow Dataset. Finally, the proposed method is compared with present state-of-the-art methods and our SGSC outdoes the latest methods in terms of speed, visualization of hidden ground truth, 3D reconstruction and accuracy.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Basheera:2021:GMS, author = "Shaik Basheera and M. Satya Sai Ram", title = "{Gray} Matter Segmentation of Brain {MRI} Using Hybrid Enhanced Independent Component Analysis", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "03", pages = "??--??", month = jul, year = "2021", DOI = "https://doi.org/10.1142/S0219467821500297", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon Jul 5 15:21:12 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500297", abstract = "One of the primary pre-processing tasks of medical image analysis is segmentation; it is used to diagnose the abnormalities in the tissues. As the brain is a complex organ, anatomical segmentation of brain tissues is a challenging task. Segmented gray matter is analyzed for early diagnosis of neurodegenerative disorders. In this endeavor, we used enhanced independent component analysis to perform segmentation of gray matter in noise-free and noisy environments. We used modified k -means, expectation--maximization and hidden Markov random field to provide better spatial relation to overcome inhomogeneity, noise and low contrast. Our objective is achieved using the following two steps: (i) Irrelevant tissues are stripped from the MRI using skull stripping algorithm. In this algorithm, sequence of threshold, morphological operations and active contour are applied to strip the unwanted tissues. (ii) Enhanced independent component analysis is used to perform segmentation of gray matter. The proposed approach is applied on both T1w MRI and T2w MRI images at different noise environments such as salt and pepper noise, speckle noise and Rician noise. We evaluated the performance of the approach using Jaccard index, Dice coefficient and accuracy. The parameters are further compared with existing frameworks. This approach gives better segmentation of gray matter for the diagnosis of atrophy changes in brain MRI.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Rawal:2021:DMW, author = "Kirti Rawal and Gaurav Sethi", title = "Design of Matched Wavelet Using Improved Genetic Algorithm for Heart Rate Variability Analysis of the Menstrual Cycle", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "03", pages = "??--??", month = jul, year = "2021", DOI = "https://doi.org/10.1142/S0219467821500303", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon Jul 5 15:21:12 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500303", abstract = "The matched wavelet is designed in this paper using an improved genetic algorithm for detecting the Heart Rate Variability (HRV) variations within phases of the menstrual cycle accurately. The idea of an improved genetic algorithm is to use an optimization technique like least mean square (LMS) before the genetic algorithm. The advantage of using the LMS prior to the genetic algorithm is to optimize the data before giving to the genetic algorithm, thereby limiting the area of the search for an optimal solution. The results show that matched wavelets created using an improved genetic algorithm can detect the HRV variations accurately in the standing and laying postures.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Wagdy:2021:DDI, author = "Marian Wagdy and Khaild Amin and Mina Ibrahim", title = "Dewarping Document Image Techniques: Survey and Comparative Study", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "03", pages = "??--??", month = jul, year = "2021", DOI = "https://doi.org/10.1142/S0219467821500315", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon Jul 5 15:21:12 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500315", abstract = "In recent years, everyone has his/her own handheld digital devices such as PDAs and camera phones which are used to capture any documents, for example, posters, magazine and books. This is the simplest way to disseminating and collecting information. Unfortunately, the snapshot of this document in an uncontrolled environment has been suffering from different perspectives and geometric distortions, especially when a picture is taken from rolled document, page of thick book, multi-folded documents and crumpled pages. In such cases, the most common distortion appeared is warping text lines. In this paper, we present a survey and a comparative study of document image dewarping techniques which aim to solve the curled lines and geometric distortion problems. We introduce a new classification of the available dewarping document image techniques and investigate their available datasets. Finally, we present the evaluation metric to test these techniques.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Nnolim:2021:SIH, author = "Uche A. Nnolim", title = "Single Image De-Hazing via Multiscale Wavelet Decomposition and Estimation with Fractional Gradient-Anisotropic Diffusion Fusion", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "03", pages = "??--??", month = jul, year = "2021", DOI = "https://doi.org/10.1142/S0219467821500327", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon Jul 5 15:21:12 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500327", abstract = "This paper presents algorithms based on fractional multiscale gradient fusion and multilevel wavelet decomposition for underwater and hazy image enhancement. The algorithms utilize partial differential equation (PDE)-generated low- and high-frequency images fused via gradient domain and anisotropic diffusion. Furthermore, wavelet multi-level decomposition, estimation and adjustment of detail and approximation coefficients are employed in improving local and global enhancement. Solutions to halo effect are also developed using compressive bilateral filters or other nonlinear/nonlocal means filter. Ultimately, experimental comparisons indicate that the proposed methods surpass or are comparable to several algorithms from the literature.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Arora:2021:CHM, author = "Tanvi Arora", title = "Classification of Human Metaspread Images Using Convolutional Neural Networks", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "03", pages = "??--??", month = jul, year = "2021", DOI = "https://doi.org/10.1142/S0219467821500339", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon Jul 5 15:21:12 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500339", abstract = "Chromosomes are the genetic information carriers. Any modification to the structure or the number of chromosomes results in a medical condition termed as genetic defect. In order to uncover the genetic defects, the chromosomes are imaged during the cell division process. The images thus generated are termed as metaspread images and are used for identifying the genetic defects. It has been observed that the metaspread images generally suffer from intensity inhomogeneity and the chromosomes are also present in varied orientations, and as a result finding genetic defects from such images is a tedious process. Therefore, cytogeneticists manually select the images that can be used for the purpose of uncovering the genetic defects and the generation of the karyotype. In the proposed approach, a novel method is being presented using DenseNet architecture of the convolutional neural networks-based classifier, which classifies the human metaspread images into two distinct categories, namely, analyzable and non-analyzable based on the orientation of the chromosomes present in the metaspread images. This classification process will help to select the most prominent metaspread images for karyotype generation that has least amount of touching and overlapping chromosomes. The proposed method is novel in comparison to the earlier methods as it works on any type of image, be it G band images, MFISH images or the Q-banded images. The proposed method has been trained by using a ground truth of 156{\nobreakspace}40750 metaspread images. The proposed classifier has been able to achieve an error rate of 1.46\%.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Gudise:2021:MBI, author = "Sandhya Gudise and Giri Babu Kande and T. Satya Savithri", title = "{MR} Brain Image Segmentation to Detect White Matter, Gray Matter, and Cerebro Spinal Fluid Using {TLBO} Algorithm", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "03", pages = "??--??", month = jul, year = "2021", DOI = "https://doi.org/10.1142/S0219467821500340", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon Jul 5 15:21:12 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500340", abstract = "This paper proposes an advanced and precise technique for the segmentation of Magnetic Resonance Image (MRI) of the brain. Brain MRI segmentation is to be familiar with the anatomical structure, to recognize the deformities, and to distinguish different tissues which help in treatment planning and diagnosis. Nature's inspired population-based evolutionary algorithms are extremely popular for a wide range of applications due to their best solutions. Teaching Learning Based Optimization (TLBO) is an advanced population-based evolutionary algorithm designed based on Teaching and Learning process of a classroom. TLBO uses common controlling parameters and it won't require algorithm-specific parameters. TLBO is more appropriate to optimize the real variables which are fuzzy valued, computationally efficient, and does not require parameter tuning. In this work, the pixels of the brain image are automatically grouped into three distinct homogeneous tissues such as White Matter (WM), Gray Matter (GM), and Cerebro Spinal Fluid (CSF) using the TLBO algorithm. The methodology includes skull stripping and filtering in the pre-processing stage. The outcomes for 10 MR brain images acquired by utilizing the proposed strategy proved that the three brain tissues are segmented accurately. The segmentation outputs are compared with the ground truth images and high values are obtained for the measure's sensitivity, specificity, and segmentation accuracy. Four different approaches, namely Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Bacterial Foraging Algorithm (BFA), and Electromagnetic Optimization (EMO) are likewise implemented to compare with the results of the proposed methodology. From the results, it can be proved that the proposed method performed effectively than the other.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Shivsharan:2021:DRD, author = "Nitin Shivsharan and Sanjay Ganorkar", title = "Diabetic Retinopathy Detection Using Optimization Assisted Deep Learning Model: Outlook on Improved Grey Wolf Algorithm", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "03", pages = "??--??", month = jul, year = "2021", DOI = "https://doi.org/10.1142/S0219467821500352", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon Jul 5 15:21:12 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500352", abstract = "In recent days, study on retinal image remains a significant area for analysis. Several retinal diseases are identified by examining the differences occurring in the retina. Anyhow, the major shortcoming between these analyses was that the identification accuracy is not satisfactory. The adopted framework includes two phases namely; (i) feature extraction and (ii) classification. Initially, the input fundus image is subjected to the feature extraction process, where the features like Local Binary Pattern (LBP), Local Vector Pattern (LVP) and Local Tetra Patterns (LTrP) are extracted. These extracted features are subjected to the classification process, where the Deep Belief Network (DBN) is used as the classifier. In addition, to improve the accuracy, the activation function and hidden neurons of DBN are optimally tuned by means of the Self Improved Grey Wolf Optimization (SI-GWO). Finally, the performance of implemented work is compared and proved over the conventional models.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Salehi:2021:NHF, author = "Hadi Salehi and Javad Vahidi", title = "A Novel Hybrid Filter for Image Despeckling Based On Improved Adaptive {Wiener} Filter, Bilateral Filter and Wavelet Filter", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "03", pages = "??--??", month = jul, year = "2021", DOI = "https://doi.org/10.1142/S0219467821500364", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon Jul 5 15:21:12 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500364", abstract = "Images are widely used in engineering. But, some images such as medical ultrasound images are mainly degraded by an intrinsic noise called speckle. Therefore, de-speckling is a main pre-processing stage for degraded images. In this paper, we suggest three phases and three denoising filters. In the first phase, the coefficient of variation is computed from the noisy image. Next, fuzzy c-means (FCM) is applied to the coefficients of variation. Applying FCM leads to the fuzzy classification of image regions. Next, the second phase is a hybrid of the three denoising filters. Fast bilateral filter (BF) for homogeneous regions, improved the adaptive wiener filters (AWFs) and wavelet filter that are applied on homogeneous, detail and edge regions, respectively. The proposed improved AWF has been developed from the AWF. In the third phase, the output image is evaluated by the fuzzy logic approach. Thus, with three phases, the proposed method has a better image detail preservation compared to some other standard methods. The experimental outcomes show that the proposed denoising algorithm is able to preserve image details and edges compared with other de-speckling methods.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Rema:2021:EHC, author = "N. R. Rema and P. Mythili", title = "Extremely High Compression and Identification of Fingerprint Images Using {SA4} Multiwavelet Transform", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "03", pages = "??--??", month = jul, year = "2021", DOI = "https://doi.org/10.1142/S0219467821500376", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon Jul 5 15:21:12 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500376", abstract = "The aim of any fingerprint image compression technique is to achieve a maximum amount of compression with an adequate quality compressed image which is suitable for fingerprint recognition. Currently available techniques in the literature provide 100\% recognition only up to a compression ratio of 180:1. The performance of any identification technique inherently depends on the techniques with which images are compressed. To improve the identification accuracy while the images are highly compressed, a multiwavelet-based identification approach is proposed in this paper. Both decimated and undecimated coefficients of SA4 (Symmetric Antisymmetric) multiwavelet are used as features for identification. A study is conducted on the identification performance of the multiwavelet transform with various sizes of images compressed using both wavelets and multiwavelets for fair comparison. It was noted that for images with size power of 2, the decimated multiwavelet-based compression and identification give a better performance compared to other combinations of compression/identification techniques whereas for images with size not a power of 2, the undecimated multiwavelet transform gives a better performance compared to other techniques. A 100\% identification accuracy was achieved for the images from NIST-4, NITGEN, FVC2002DB3\_B, FVC2004DB2\_B and FVC2004DB1\_B databases for compression ratios up to 520:1, 210:1, 445:1, 545:1 and 1995:1, respectively.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Zarif:2021:VIC, author = "Sameh Zarif and Mina Ibrahim", title = "Video Inpainting: A Complete Framework", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "03", pages = "??--??", month = jul, year = "2021", DOI = "https://doi.org/10.1142/S0219467821500388", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon Jul 5 15:21:12 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500388", abstract = "Reconstructing and repairing of corrupted or missing parts after object removal in digital video is an important trend in artwork restoration. Video inpainting is an active subject in video processing, which deals with the recovery of the corrupted or missing data. Most previous video inpainting approaches consume more time in extensive search to find the best patch to restore the damaged frames. In addition to that, most of them cannot handle the gradual and sudden illumination changes, dynamic background, full object occlusion, and object changes in scale. In this paper, we present a complete video inpainting framework without the extensive search process. The proposed framework consists of a segmentation stage based on low-resolution version and background subtraction. A background inpainting stage is applied to restore the damaged background regions after static or moving object removal based on the gray-level co-occurrence matrix (GLCM). A foreground inpainting stage is based on objects repository. GLCM is used to complete the moving occluded objects during the occlusion. The proposed method reduces the inpainting time from hours to a few seconds and maintains the spatial and temporal consistency. It works well when the background has clutter or fake motion, and it can handle the changes in object size and in posture. Moreover, it is able to handle full occlusion and illumination changes.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Siri:2021:ALB, author = "Sangeeta K. Siri and S. Pramod Kumar and Mrityunjaya V. Latte", title = "Accurate Liver Border Identification Model in {CT} Scan Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "03", pages = "??--??", month = jul, year = "2021", DOI = "https://doi.org/10.1142/S021946782150039X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon Jul 5 15:21:12 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S021946782150039X", abstract = "The liver is an important organ in human body with certain variations in its edges, color, shape and pixel intensity distribution. These uncertainties may be because of various liver pathologies, hereditary or both. Along with it, liver has close proximity to its nearby organs. Hence, identifying liver in scanned images is a challenging step in image processing. This task becomes more imprecise when liver diseases are present at the edges. The liver segmentation is prerequisite for liver volumetry, computer-based surgery planning, liver surgery modelling, surgery training, 3D view generation, etc. The proposed hybrid segmentation method overcomes the problems and identifies liver boundary in Computed-Tomography (CT) scan images accurately. In this paper, the first step is to study statistics of pixel intensity distribution within liver image, and novel methodology is designed to obtain thresholds. Then, threshold-based segmentation is applied which separates the liver from abdominal CT scan images. In the second step, liver edge is corrected using improved chain code and Bresenham pixel interconnection methods. This provides a precise liver image. The initial points are located inside the liver region without user interventions. These initial points evolve outwardly using Fast Marching Method (FMM), identifying the liver boundary accurately in CT abdominal scan images.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Bentahar:2021:HTB, author = "Tarek Bentahar and Atef Bentahar and Riad Saidi and Hichem Mayache and Karim Ferroudji", title = "Hybrid Technique of the Branch-Cut and the Quality-Guided for {inSAR} Phase Unwrapping", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "03", pages = "??--??", month = jul, year = "2021", DOI = "https://doi.org/10.1142/S0219467821500406", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon Jul 5 15:21:12 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500406", abstract = "Phase unwrapping is a key step for interferometric synthetic aperture radar imaging. It is widely used for earth mapping and surface change detection. Several residue-immune phase unwrapping algorithms have been proposed; among them, we find branch-cut and quality-guided in the path-following category. Branch-cut methods are usually faster than the quality-guided techniques; however, the accuracy of their unwrapped phase images is lower. In this paper, a hybrid model which combines both algorithms is proposed in order to establish a satisfactory compromise between processing time and accuracy. In order to verify the usefulness of the proposed hybridization, it is tested on simulated and real inSAR data. The obtained results are compared with the two methods under several relevant metrics.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Brown:2021:CSF, author = "Kyle Brown and Nikolaos Bourbakis", title = "Curve and Surface Fitting Techniques in Computer Vision", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "04", pages = "??--??", month = oct, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467821500418", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon Dec 27 07:10:54 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500418", abstract = "Curve and surface-fitting are classic problems of approximation that find use in many fields, including computer vision. There are two broad approaches to the problem --- interpolation, which seeks to fit points exactly, and regression, which seeks a rougher approximation which is more robust to noise. This survey looks at several techniques of both kinds, with a particular focus on applications in computer vision. We make use of an empirical first-level evaluation approach which scores the techniques on multiple features based on how important they are to users of the technique and developers. This provides a quick summary of the broad applicability of the technique to most situations, rather than a deep evaluation of the performance and accuracy of the technique obtained by running it on several datasets.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Pervej:2021:RTC, author = "Masud Pervej and Sabuj Das and Md. Parvez Hossain and Md. Atikuzzaman and Md. Mahin and Muhammad Aminur Rahaman", title = "Real-Time Computer Vision-Based {Bangla} Vehicle License Plate Recognition using Contour Analysis and Prediction Algorithm", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "04", pages = "??--??", month = oct, year = "2021", DOI = "https://doi.org/10.1142/S021946782150042X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon Dec 27 07:10:54 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S021946782150042X", abstract = "Computer vision-based recognition of Bangle vehicle license plates (LPs) is an arduous task in dirty and muddy situations. This paper proposes an efficient method for real-time computer vision-based recognition of Bangla vehicle LPs using contour analysis and prediction algorithms. The method initially applies gray scaling the input RGB images, histogram equalization to improve the grayscale image quality, edge detection using Sobel edge detector, and adaptive thresholding to convert it to a binary image. The system localizes the vehicle LP based on the maximum rectangular contour area and converts it into a predefined size. Noise removal technique using morphological dilation and erosion operation is used, followed by Gaussian filtering on binary image to improve the image quality further. The system clusters the two-lined LP into seven clusters. The sub-clustering is applied on specific clusters and makes 68 individual sub-clusters. The system extracts vector contour (VC) from each 68 individual classes. After VC extraction, the system normalizes it into a q predefined length. The system applies inter co-relation function (ICF) to categorize each sub-cluster to its previously defined individual class. For that, it calculates the maximum similarity between test and previously trained VCs. The system applies the dependency prediction algorithm in parallel to predict the whole string (district name) in the cluster-1 based on previously categorized class or classes (starting character or characters of the district part). (Metro) or (null) from cluster-2, ``-'' (hyphen) from cluster-3 and 6 are predicted automatically as their positions are fixed. The system is trained using 68 classes in which each class contains 100 samples generated by the augmentation technique. The system is tested using another set of 68 classes with a total of 68{\texttimes}100=6800 images acquiring the recognition accuracy of 96.62\% with the mean computational cost of 8.363 ms/f. The system is also tested using 500 vehicle whole Bangla LPs achieving the mean whole LP recognition accuracy of 95.41\% with a mean computational cost of 35.803 ms/f.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Patel:2021:ELB, author = "Krina Patel and Dippal Israni and Dweepna Garg", title = "An Efficient Local Block {Sobolev} Gradient and {Laplacian} Approach for Elimination of Atmospheric Turbulence", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "04", pages = "??--??", month = oct, year = "2021", DOI = "https://doi.org/10.1142/S0219467821500431", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon Dec 27 07:10:54 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500431", abstract = "A long range observing systems can be sturdily affected by scintillations. These scintillations are caused by changes in atmospheric conditions. In recent years, various turbulence mitigation approaches for turbulence mitigation have been exhibiting a promising nature. In this paper, we propose an effectual method to alleviate the effects of atmospheric distortion on observed images and video sequences. These sequences are mainly affected through floating air turbulence which can severely degrade the image quality. The existing algorithms primarily focus on the removal of turbulence and provides a solution only for static scenes, where there is no moving entity (real motion). As in the traditional SGL algorithm, the updated frame is iteratively used to correct the turbulence. This approach reduces the turbulence effect. However, it imposes some artifacts on the real motion that blurs the object. The proposed method is an alteration of the existing Sobolev Gradient and Laplacian (SGL) algorithm to eliminate turbulence. It eliminates the ghost artifact formed on moving object in the existing approach. The proposed method alleviates turbulence without harming the moving objects in the scene. The method is demonstrated on significantly distorted sequences provided by OTIS and compared with the SGL technique. The information conveyed in the scene becomes clearly visible through the method on exclusion of turbulence. The proposed approach is evaluated using standard performance measures such as MSE, PSNR and SSIM. The evaluation results depict that the proposed method outperforms the existing state-of-the-art approaches for all three standard performance measures.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Nivedita:2021:ICV, author = "M. Nivedita and Priyanka Chandrashekar and Shibani Mahapatra and Y. Asnath Victy Phamila and Sathish Kumar Selvaperumal", title = "Image Captioning for Video Surveillance System using Neural Networks", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "04", pages = "??--??", month = oct, year = "2021", DOI = "https://doi.org/10.1142/S0219467821500443", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon Dec 27 07:10:54 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500443", abstract = "Security has always been of paramount importance to humans. In the absence of a sense of security at one's workplace, home or anywhere else, people feel uneasy and vulnerable. With the improvement of modern technology, along with the lack of time at hand, the need for faster, efficient, accurate as well as low-cost security techniques is more than ever. Image Captioning for Video Surveillance System is proposed to develop visual systems that generate contextual descriptions about objects in images, and then use these descriptions to provide information of the scene that needs to be secured. The proposed system uses a neural network model composed of a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to caption the incoming video feed. The main significance of this paper is in integrating the system with Discrete Wavelet Transform (DWT), which is applied on the incoming video feed, so that the compressed LL band frames transferred wirelessly to the model are smaller in comparison, leading to less transfer time and faster processing by the model.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Pal:2021:RMD, author = "Tannistha Pal", title = "A Robust Method for Dehazing of Single Image with Sky Region Detection and Segmentation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "04", pages = "??--??", month = oct, year = "2021", DOI = "https://doi.org/10.1142/S0219467821500455", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon Dec 27 07:10:54 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500455", abstract = "In recent times, there has been a tremendous progress in image dehazing for computer vision applications, while the sky region processed by these algorithms tends to degrade by noise and color distortion. In this paper, an improved dark channel prior algorithm is proposed which detects the sky region first and divides the image into sky region and non-sky region and then estimates the transmission of two parts separately, followed by combining with refining step. The proposed algorithm also accurately corrects the transmission of the sky region to avoid noise and color distortion. Experimental results show a greater quality improvement in the output images than the existing strategies.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kumar:2021:ECB, author = "Gangavarapu Venkata Satya Kumar and Pillutla Gopala Krishna Mohan", title = "Enhanced Content-Based Image Retrieval Using Information Oriented Angle-Based Local Tri-Directional {Weber} Patterns", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "04", pages = "??--??", month = oct, year = "2021", DOI = "https://doi.org/10.1142/S0219467821500467", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon Dec 27 07:10:54 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500467", abstract = "In diverse computer applications, the analysis of image content plays a key role. This image content might be either textual (like text appearing in the images) or visual (like shape, color, texture). These two image contents consist of image's basic features and therefore turn out to be as the major advantage for any of the implementation. Many of the art models are based on the visual search or annotated text for Content-Based Image Retrieval (CBIR) models. There is more demand toward multitasking, a new method needs to be introduced with the combination of both textual and visual features. This paper plans to develop the intelligent CBIR system for the collection of different benchmark texture datasets. Here, a new descriptor named Information Oriented Angle-based Local Tri-directional Weber Patterns (IOA-LTriWPs) is adopted. The pattern is operated not only based on tri-direction and eight neighborhood pixels but also based on four angles 0\textdegree, 45\textdegree, 90\textdegree, and 135\textdegree. Once the patterns concerning tri-direction, eight neighborhood pixels, and four angles are taken, the best patterns are selected based on maximum mutual information. Moreover, the histogram computation of the patterns provides the final feature vector, from which the new weighted feature extraction is performed. As a new contribution, the novel weight function is optimized by the Improved MVO on random basis (IMVO-RB), in such a way that the precision and recall of the retrieved image is high. Further, the proposed model has used the logarithmic similarity called Mean Square Logarithmic Error (MSLE) between the features of the query image and trained images for retrieving the concerned images. The analyses on diverse texture image datasets have validated the accuracy and efficiency of the developed pattern over existing.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kang:2021:GIB, author = "Henry Kang and Ioannis Stamoulis", title = "{Gaussian} Image Binarization", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "04", pages = "??--??", month = oct, year = "2021", DOI = "https://doi.org/10.1142/S0219467821500479", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon Dec 27 07:10:54 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500479", abstract = "Line drawing and screentoning are two distinct areas of study in non-photorealistic rendering, where the former emphasizes object contours, while the latter conveys tone and shading information on object surfaces. As these two problems are concerned with different yet equally important features, either method seldom delivers a complete description of the scene when used alone. Yet, research community has largely treated them as separate problems and thus resulted in two entirely different sets of solutions, complicating both implementation and usage. In this paper, we present a stylistic image binarization method called {\em hybrid difference of Gaussians (HDoG)\/} that performs both line drawing and screentoning in a unified framework. Our method is based upon two different extensions of DoG operator: one for line extraction, and the other for tone description. In particular, we propose an extension called {\em adaptive DoG}, that uses luminance as weight to automatically generate screentone that adapts to the local tone. Experimental results demonstrate that our hybrid method effectively generates aesthetically pleasing image binarizations that encompass both line drawing and screentoning, closely resembling professional pen-and-ink illustrations. Also, being based on Gaussian filtering, our method is very fast and also easy to implement.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Swamy:2021:HIC, author = "A. S. Anand Swamy and N. Shylashree", title = "{HDR} Image Compression by Multi-Scale down Sampling of Intensity Levels", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "04", pages = "??--??", month = oct, year = "2021", DOI = "https://doi.org/10.1142/S0219467821500480", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon Dec 27 07:10:54 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500480", abstract = "HDR images are inherently very large in size compared to normal images. Hence, storage and communication overheads of HDR images are expensive to be used in mobile devices. Hence, invariably image compression is adopted for HDR images. In this paper, HDR image compression is achieved by down sampling the intensity levels while maintaining the dynamic range same as that of the original. This aspect retains the edge information of the images almost intact. Spatial down-sampling process is used to reduce the number of intensity samples. Consequently, this operation lowers the bit depth required to store the corresponding index file which in turn results in image compression.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Panigrahi:2021:JBF, author = "Susant Kumar Panigrahi and Supratim Gupta", title = "Joint Bilateral Filter for Signal Recovery from Phase Preserved Curvelet Coefficients for Image Denoising", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "04", pages = "??--??", month = oct, year = "2021", DOI = "https://doi.org/10.1142/S0219467821500492", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon Dec 27 07:10:54 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500492", abstract = "Thresholding of Curvelet Coefficients, for image denoising, drains out subtle signal component in noise subspace. In effect, it also produces ringing artifacts near edges. We found that the noise sensitivity of Curvelet phases{\nobreakspace}40 --- in contrast to their magnitude{\nobreakspace}40 --- reduces with higher noise level. Thus, we preserved the phase of the coefficients below threshold at coarser scale and estimated the corresponding magnitude by Joint Bilateral Filtering (JBF) technique. In contrast to the traditional hard thresholding, the coefficients in the finest scale is estimated using Bilateral Filtering (BF). The proposed filtering approach in the finest scale exhibits better connectedness among the edges, while removing the granular artifacts in the denoised image due to hard thresholding. Finally, the use of Guided Image Filter (GIF) on the Curvelet-based reconstructed image (initial denoised image in spatial domain) ensures the preservation of small image information with sharper edges and textures detail in the final denoised image. The lower noise sensitivity of Curvelet phase at higher noise strength accelerates the performance of proposed method over several state-of-the-art techniques and provides comparable outcome at lower noise levels.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Ramwala:2021:RNC, author = "Ojas A. Ramwala and Smeet A. Dhakecha and Chirag N. Paunwala and Mita C. Paunwala", title = "Reminiscent Net: Conditional {GAN}-based Old Image De-Creasing", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "04", pages = "??--??", month = oct, year = "2021", DOI = "https://doi.org/10.1142/S0219467821500509", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon Dec 27 07:10:54 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500509", abstract = "Documents are an essential source of valuable information and knowledge, and photographs are a great way of reminiscing old memories and past events. However, it becomes difficult to preserve the quality of such ancient documents and old photographs for an extremely long time, as these images usually get damaged or creased due to various extrinsic effects. Utilizing image editing software like Photoshop to manually reconstruct such old photographs and documents is a strenuous and an enduring process. This paper attempts to leverage the generative modeling capabilities of Conditional Generative Adversarial Networks by utilizing specialized architectures for the Generator and the Discriminator. The proposed Reminiscent Net has a U-Net-based Generator with numerous feature maps for complete information transfer with the incorporation of location and contextual details, and the absence of dense layers allows utilization of diverse sized images. Implementation of the PatchGAN-based Discriminator that penalizes the image at the scale of patches has been proposed. NADAM optimizer has been implemented to enable faster and better convergence of the loss function. The proposed method produces visually appealing de-creased images, and experiments indicate that the architecture performs better than various novel approaches, both qualitatively and quantitatively.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kuzhali:2021:AID, author = "S. Elavaar Kuzhali and D. S. Suresh", title = "Automated Image Denoising Model: Contribution Towards Optimized Internal and External Basis", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "04", pages = "??--??", month = oct, year = "2021", DOI = "https://doi.org/10.1142/S0219467821500510", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon Dec 27 07:10:54 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500510", abstract = "For handling digital images for various applications, image denoising is considered as a fundamental pre-processing step. Diverse image denoising algorithms have been introduced in the past few decades. The main intent of this proposal is to develop an effective image denoising model on the basis of internal and external patches. This model adopts Non-local means (NLM) for performing the denoising, which uses redundant information of the image in pixel or spatial domain to reduce the noise. While performing the image denoising using NLM, ``denoising an image patch using the other noisy patches within the noisy image is done for internal denoising and denoising a patch using the external clean natural patches is done for external denoising''. Here, the selection of optimal block from the entire datasets including internal noisy images and external clean natural images is decided by a new hybrid optimization algorithm. The two renowned optimization algorithms Chicken Swarm Optimization (CSO), and Dragon Fly Algorithm (DA) are merged, and the new hybrid algorithm Rooster-based Levy Updated DA (RLU-DA) is adopted. The experimental results in terms of some relevant performance measures show the promising results of the proposed model with remarkable stability and high accuracy.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Ehsaeyan:2021:MIT, author = "Ehsan Ehsaeyan and Alireza Zolghadrasli", title = "A Multilevel Image Thresholding Method Using the {Darwinian} Cuckoo Search Algorithm", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "04", pages = "??--??", month = oct, year = "2021", DOI = "https://doi.org/10.1142/S0219467821500522", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon Dec 27 07:10:54 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500522", abstract = "Image segmentation is a prime operation to understand the content of images. Multilevel thresholding is applied in image segmentation because of its speed and accuracy. In this paper, a novel multilevel thresholding algorithm based on Cuckoo search (CS) is introduced. One of the major drawbacks of metaheuristic algorithms is the stagnation phenomenon which leads to a fall into local optimums and premature convergence. To overcome this shortcoming, the idea of Darwinian theory is incorporated with CS algorithm to increase the diversity and quality of the individuals without decreasing the convergence speed of CS algorithm. A policy of encouragement and punishment is considered to lead searching agents in the search space and reduce the computational time. The algorithm is implemented based on dividing the population into specified groups and each group tries to find a better location. Ten test images are selected to verify the ability of our algorithm using the famous energy curve method. Two popular entropies criteria, Otsu and Kapur, are employed to evaluate the capability of the introduced algorithm. Eight different search algorithms are also implemented and compared with our method. Experimental results manifest that DCS is a powerful tool for multilevel thresholding and the obtained results outperform the CS algorithm and other heuristic search methods.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Pan:2021:SID, author = "Yongpeng Pan and Zhenxue Chen and Xianming Li and Weikai He", title = "Single-Image Dehazing via Dark Channel Prior and Adaptive Threshold", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "04", pages = "??--??", month = oct, year = "2021", DOI = "https://doi.org/10.1142/S0219467821500534", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon Dec 27 07:10:54 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500534", abstract = "Due to the haze weather, the outdoor image quality is degraded, which reduces the image contrast, thereby reducing the efficiency of computer vision systems such as target recognition. There are two aspects of the traditional algorithm based on the principle of dark channel to be improved. First, the restored images obviously contain color distortion in the sky region. Second, the white regions in the scene easily affect the atmospheric light estimated. To solve the above problems, this paper proposes a single-image dehazing and image segmentation method via dark channel prior (DCP) and adaptive threshold. The sky region of hazing image is relatively bright, so sky region does not meet the DCP. The sky part is separated by the adaptive threshold, then the scenery and the sky area are dehazed, respectively. In order to avoid the interference caused by white objects to the estimation of atmospheric light, we estimate the value of atmospheric light using the separated area of the sky. The algorithm in this paper makes up for the shortcoming that the algorithm based on the DCP cannot effectively process the hazing image with sky region, avoiding the effect of white objects on estimating atmospheric light. Experimental results show the feasibility and effectiveness of the improved algorithm.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Chalamalasetty:2021:RPT, author = "Sai Pratheek Chalamalasetty and Srinivasa Rao Giduturi", title = "Research Perception Towards Copy-Move Image Forgery Detection: Challenges and Future Directions", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "04", pages = "??--??", month = oct, year = "2021", DOI = "https://doi.org/10.1142/S0219467821500546", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon Dec 27 07:10:54 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500546", abstract = "In digital images, Copy-Move Forgery is a general kind of forgery techniques. The process of replicating one part of the image within the same image is termed as copy-move forgery. An effective and reliable approach needs to be developed for identifying these forgeries for restoring the image trustworthiness. The main intent of this paper is to sort out the diverse copy-move image forgery detection models. This survey makes an effective literature analysis on a set of literal works from the past 10 years. The analysis is focused on categorizing the models based on transformation models, machine learning algorithms, and other advanced techniques. The main contribution and limitations of the works are clearly pointed out. In addition, the types of datasets and the simulation platforms utilized by different copy-move forgery detection (CMFD) models are analyzed. The performance measures evaluated by different contributions have been observed for making a concluding decision. The utilization of optimization algorithms on copy-move image forgery detection has also been studied. Finally, the research gaps and challenges with future direction are discussed, which is helpful for researchers in developing an efficient CMFD that could attain high performance.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Hamroun:2021:NCB, author = "Mohamed Hamroun and Karim Tamine and Frederic Claux and Mourad Zribi", title = "A New Content-Based Image Retrieval System Using Deep Visual Features", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "04", pages = "??--??", month = oct, year = "2021", DOI = "https://doi.org/10.1142/S0219467821500558", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon Dec 27 07:10:54 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500558", abstract = "Content-based image retrieval (CBIR) is a technique for images retrieval based on their visual features, i.e. induced by their pixels. The images are, classically, described by the image feature vectors. Those vectors reflect the texture, color or a combination of them. The accuracy of the CBIR system is highly influenced by the (i) definition of the image feature vector describing the image, (ii) indexing and (iii) retrieval process. In this paper, we propose a new CBIR system entitled ISE (Image Search Engine). Our ISE system defines the optimum combination of color and texture features as an image feature vector, including the Particle Swarm Optimization (PSO) algorithm and employing an Interactive Genetic Approach (GA) for the indexing process. The performance analysis shows that our suggested PCM (Proposed Combination Method) upgrades the average precision metric from 66.6\% to 89.30\% for the ``Food'' category color histogram, from 77.7\% to 100\% concerning CCVs (Color Coherence Vectors) for the ``Flower'' category and from 58\% to 87.65\% regarding the DCD (Dominant Color Descriptor) for the ``Building'' category using the Corel dataset. Besides, our ISE system showcases an average precision of 98.23\%, which is significantly higher than other CBIR systems presented in related works.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Anonymous:2021:AIV, author = "Anonymous", title = "Author Index (Volume 21)", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "04", pages = "??--??", month = oct, year = "2021", DOI = "https://doi.org/10.1142/S0219467821990011", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon Dec 27 07:10:54 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821990011", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Naveenkumar:2021:STJ, author = "M. Naveenkumar and S. Domnic", title = "Spatio Temporal Joint Distance Maps for Skeleton-Based Action Recognition Using Convolutional Neural Networks", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "05", pages = "??--??", month = dec, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467821400015", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon Dec 27 07:10:56 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821400015", abstract = "Skeleton-based action recognition has become popular with the recent developments in sensor technology and fast pose estimation algorithms. The existing research works have attempted to address the action recognition problem by considering either spatial or temporal dynamics of the actions. But, both the features (spatial and temporal) would contribute to solve the problem. In this paper, we address the action recognition problem using 3D skeleton data by introducing eight Joint Distance Maps, referred to as Spatio Temporal Joint Distance Maps (ST-JDMs), to capture spatio temporal variations from skeleton data for action recognition. Among these, four maps are defined in spatial domain and remaining four are in temporal domain. After construction of ST-JDMs from an action sequence, they are encoded into color images. This representation enables us to fine-tune the Convolutional Neural Network (CNN) for action classification. The empirical results on the two datasets, UTD MHAD and NTU RGB+D, show that ST-JDMs outperforms the other state-of-the-art skeleton-based approaches by achieving recognition accuracies 91.63\% and 80.16\%, respectively.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", remark = "Special Issue on Deep Neural Networks for Medical Image Detection, Segmentation, and Localization", } @Article{Sharma:2021:DDS, author = "Moolchand Sharma and Bhanu Jain and Chetan Kargeti and Vinayak Gupta and Deepak Gupta", title = "Detection and Diagnosis of Skin Diseases Using Residual Neural Networks {(RESNET)}", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "05", pages = "??--??", month = dec, year = "2021", DOI = "https://doi.org/10.1142/S0219467821400027", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon Dec 27 07:10:56 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821400027", abstract = "Skin diseases have become prevalent in the present times. It has been observed in a study that every year the percentage of global population suffering from skin diseases is 1.79\%. These diseases have a potential to become extremely dangerous if they are not treated in the nascent stages. It is extremely important that skin diseases are detected and diagnosed at the starting stages so that serious risks to life are avoided. Often, exhaustive tests are required so as to arrive on a conclusion regarding skin condition, the patient may be affected with. Thus, an expert system is required that has the ability to identify diseases and propose the required diagnosis. Presently, only a few solutions are available for diagnosis of skin diseases using computerized system but this is an era which is under extensive research and can be developed further. As the existing system has certain loopholes, this system attempts to override the present problems by applying a different approach. As a result of comparison of results from numerous research papers, an expert system has been developed by choosing residual neural networks (ResNet) and this system can be used to aid skin specialists in identifying and diagnosing various major diseases of skin like (Eczema, Psoriasis & Lichen Planus, Benign Tumors, Fungal Infections and Viral Infections) in more effective and efficient manner. The causes for identified skin disease can be outlined through this system and treatment can be provided. We have used Python language for implementing the proposed expert system that uses a 50-layer ResNets for training a dataset that has been taken from DERMNET. We achieved an accuracy of 95\% using ResNet for training of the model and prediction of results at an epoch value of 10.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", remark = "Special Issue on Deep Neural Networks for Medical Image Detection, Segmentation, and Localization", } @Article{Gupta:2021:DCB, author = "Isha Gupta and Sheifali Gupta and Swati Singh", title = "Different {CNN}-based Architectures for Detection of Invasive Ductal Carcinoma in Breast Using Histopathology Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "05", pages = "??--??", month = dec, year = "2021", DOI = "https://doi.org/10.1142/S0219467821400039", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon Dec 27 07:10:56 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821400039", abstract = "In recent years, many improvements have been made in image processing techniques which aid pathologists to identify cancer cells. Nowadays, convolutional neural networks (CNNs), also known as deep learning algorithms have become popular for the applications of image processing and examination in histopathology image (tissue and cell images). This study aims to present the detection of histopathology images associated to detection of invasive ductal carcinoma (IDC) and non-IDC in breast. However, detection of IDC is a challenging task in histopathology image which needs deep examination as cancer comprises of minor entities with a diversity of forms which can be easily mixed up with different objects or facts contained in image. Hence, the proposed study suggests three types of CNN architectures which is called 8-layer CNNs, 9-layer CNNs and 19-layer CNNs, respectively, in the detecting IDC using histopathology images. The purpose of the study is to identify IDC from histopathology images by taking proper layer in deep layer CNNs with the maximum accuracy, highest sensitivity, precision and least classification error. The result shows better performance for deep layer-convolutional neural networks architecture by using 19-layer CNNs.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", remark = "Special Issue on Deep Neural Networks for Medical Image Detection, Segmentation, and Localization", } @Article{Verma:2021:CBD, author = "Parag Verma and Ankur Dumka and Anuj Bhardwaj and Mukesh Chandra Kestwal", title = "Classifying Breast Density in Mammographic Images Using Wavelet-Based and Fine-Tuned Sensory Neural Networks", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "05", pages = "??--??", month = dec, year = "2021", DOI = "https://doi.org/10.1142/S0219467821400040", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon Dec 27 07:10:56 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821400040", abstract = "In this modern world of biomedical medicine, the classification of breast density has been considered a very important part of the process of breast diagnosis. Furthering the same research, this research aims to determine the patient's breast density by mammogram image with the help of modern techniques such as computerized devices and machine learning algorithms, which will greatly help the radiologist. To carry out this process, this research paper introduces a Convolutional Neural Network (CNN) model of deep learning that will work as a basis for waveform conversion and fine-tune. This deep learning model will prove effective in automatically classifying a patient's breast density. With the help of this method, the last two layers which are fully connected are removed and joined with two newly formed layers. This would have helped in addressing a pre-trained AlexNet model that further improved the classification process. In this model, the original or preprocessed images are used at level 1 of the input (which is in sharp contrast to the usual methods based on the CNN model), which also makes the model compatible with the use of redundant wavelet coefficients. Because in the field of radiologists it is very important to underline the difference between scattered density and heterogeneous density, so the main objective of this research is focused on this end. As the proposed method has an accuracy of 82.2\%, it shows a better performance. This research paper further compares the effectiveness and performance of the proposed method to traditional fine-tuning CNN models, with satisfactory results. The comparative results of the proposed method suggest that the proposed method is in the field of radiologists representing a helpful tool. This method may be intended to act as a second eye for doctors in the medical field with the intention of classifying the categories of breast density in the patient during breast cancer screening.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", remark = "Special Issue on Deep Neural Networks for Medical Image Detection, Segmentation, and Localization", } @Article{Tunga:2021:UNM, author = "P. Prakash Tunga and Vipula Singh and V. Sri Aditya and N. Subramanya", title = "{U-Net} Model-Based Classification and Description of Brain Tumor in {MRI} Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "21", number = "05", pages = "??--??", month = dec, year = "2021", DOI = "https://doi.org/10.1142/S0219467821400052", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Mon Dec 27 07:10:56 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821400052", abstract = "In this paper, we discuss the classification of the brain tumor in Magnetic Resonance Imaging (MRI) images using the U-Net model, then evaluate parameters that indicate the performance of the model. We also discuss the extraction of the tumor region from brain image and description of the tumor regarding its position and size. Here, we consider the case of Gliomas, one of the types of brain tumors, which occur in common and can be fatal depending on their position and growth. U-Net is a model of Convolutional Neural Network (CNN) which has U-shaped architecture. MRI employs a non-invasive technique and can very well provide soft-tissue contrast and hence, for the detection and description of the brain tumor, this imaging method can be beneficial. Manual delineation of tumors from brain MRI is laborious, time-consuming and can vary from expert to expert. Our work forms a computer aided technique which is relatively faster and reproducible, and the accuracy is very much on par with ground truth. The results of the work can be used for treatment planning and further processing related to storage or transmission of images.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", remark = "Special Issue on Deep Neural Networks for Medical Image Detection, Segmentation, and Localization", } @Article{Shrivastava:2022:BTD, author = "Neeraj Shrivastava and Jyoti Bharti", title = "Breast Tumor Detection in {MR} Images Based on Density", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "01", pages = "??--??", month = jan, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467822500012", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Feb 9 07:11:50 MST 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500012", abstract = "Breast cancer is dangerous in women. It is generally found after the symptoms appear. Detecting the breast cancer at an early stage and understanding the treatment are the most important strategies to prevent death from cancer. Generally, for detection of breast cancer, breast Magnetic Resonance Image (MRI) takes place. It is one of the best approaches to detect tumor in women. In this research paper, a combination of selection methods for seed region growing image segmentation is suggested to detect breast tumor. The suggested method has been divided into following parts: First, the pre-processing of breast image is performed. Second, the automatic threshold for binarization process is calculated. Third, the number of seed points and its position in the breast image are determined automatically using density of pixels value. Fourth, a method for calculation of threshold value is proposed for the purpose of region creation in seed region growing. For the evaluation purpose, the proposed method was applied and tested on the RIDER MRI breast dataset from National Biomedical Imaging Archive (NBIA). After the test was performed, it was observed that proposed algorithm gives 90\% accuracy, 88\% True Negative Fraction, 91\% True Positive Fraction, 10\% Misclassification Rate, 94\% Precision and 86\% Relative Overlap which is better than other existing methods. It not only gives better evaluation measure but also provides segmentation method for multiple tumor detection.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Padhy:2022:CLR, author = "Rajalaxmi Padhy and Shashwat Sourav Swain and Sanjit Kumar Dash and Jibitesh Mishra", title = "Classification of Low-Resolution Satellite Images Using Fractal Augmented Descriptors", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "01", pages = "??--??", month = jan, year = "2022", DOI = "https://doi.org/10.1142/S0219467822500024", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Feb 9 07:11:50 MST 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500024", abstract = "Satellite imagery consists of highly complex spatial features that make it difficult for traditional image processing techniques to use them for classification tasks. In this paper, we propose a novel method to use these hidden fractal information that naturally exist in these satellite images. We have designed a fractal-based descriptor which generates a scale invariant fractal image for easier fractal-based pattern extraction and uses it as an added feature vector that is combined with the original image and fed into a VGG-16 deep learning architecture which successfully classifies even low-resolution satellite images with an f1-score of 0.78.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Suresha:2022:KDB, author = "M. Suresha and D. S. Raghukumar and S. Kuppa", title = "{Kumaraswamy} Distribution Based Bi-histogram Equalization for Enhancement of Microscopic Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "01", pages = "??--??", month = jan, year = "2022", DOI = "https://doi.org/10.1142/S0219467822500036", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Feb 9 07:11:50 MST 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500036", abstract = "Among all image enhancement techniques, histogram equalization is the most used technique. However, preserving brightness is the main issue, and it creates a weird look by destroying its originality. This paper proposes a new method that has command on the brightness issue of histogram equalization to enhance the quality of microscopic images. The method splits the histogram of each color channel into two sub-histograms based on their mean as the threshold and supplanting their cumulative distribution with Kumaraswamy distribution. The proposed method is tested with color microscopic images of cancer-affected lymph nodes gathered from Biological Image Repository IICBU, and objective and subjective assessments confirm that the proposed approach performs more efficiently compared to other state-of-the-art methods.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Vandana:2022:ARB, author = "Vandana and Navdeep Kaur", title = "Analytical Review of Biometric Technology Employing Vivid Modalities", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "01", pages = "??--??", month = jan, year = "2022", DOI = "https://doi.org/10.1142/S0219467822500048", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Feb 9 07:11:50 MST 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500048", abstract = "The digitalization has been challenged with the security and privacy aspects in each and every field. In addition to numerous authentication methods, biometrics has been popularized as it relies on one's individual behavioral and physical characters. In this context, numerous unimodal and multimodal biometrics have been proposed and tested in the last decade. In this paper, authors have presented a comprehensive survey of the existing biometric systems while highlighting their respective challenges, advantage and limitations. The paper also discusses the present biometric technology market value, its scope, and practical applications in vivid sectors. The goal of this review is to offer a compact outline of various advances in biometrics technology with potential applications using unimodal and multimodal bioinformatics are discussed that would prove to offer a base for any biometric-based future research.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Jameel:2022:GSI, author = "Samer Kais Jameel and Jafar Majidpour", title = "Generating Spectrum Images from Different Types --- Visible, Thermal, and Infrared Based on Autoencoder Architecture {(GVTI-AE)}", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "01", pages = "??--??", month = jan, year = "2022", DOI = "https://doi.org/10.1142/S021946782250005X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Feb 9 07:11:50 MST 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S021946782250005X", abstract = "Recently, numerous challenging problems have existed for transforming different image types (thermal infrared (TIR), visible spectrum, and near-infrared (NIR)). Other types of cameras may lack the ability and features of certain types of frequently-used cameras that produce different types of images. Based on camera features, different applications might emerge from observing a scenario under specific conditions (darkness, fog, night, day, and artificial light). We need to jump from one field to another to understand the scenario better. This paper proposes a fully automatic model (GVTI-AE) to manipulate the transformation into different types of vibrant, realistic images using the AutoEncoder method, which requires neither pre-nor post-processing or any user input. The experiments carried out using the GVTI-AE model showed that the perceptually realistic results produced in the widely available datasets (Tecnocampus Hand Image Database, Carl dataset, and IRIS Thermal/Visible Face Database).", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{BenSalah:2022:FES, author = "Marwa {Ben Salah} and Ameni Yengui and Mahmoud Neji", title = "Feature Extraction and Selection in Archaeological Images for Automatic Annotation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "01", pages = "??--??", month = jan, year = "2022", DOI = "https://doi.org/10.1142/S0219467822500061", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Feb 9 07:11:50 MST 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500061", abstract = "In this paper, we present two steps in the process of automatic annotation in archeological images. These steps are feature extraction and feature selection. We focus our research on archeological images which are very much studied in our days. It presents the most important steps in the process of automatic annotation in an image. Feature extraction techniques are applied to get the feature that will be used in classifying and recognizing the images. Also, the selection of characteristics reduces the number of unattractive characteristics. However, we reviewed various images of feature extraction techniques to analyze the archaeological images. Each feature represents one or more feature descriptors in the archeological images. We focus on the descriptor shape of the archaeological objects extraction in the images using contour method-based shape recognition of the monuments. So, the feature selection stage serves to acquire the most interesting characteristics to improve the accuracy of the classification. In the feature selection section, we present a comparative study between feature selection techniques. Then we give our proposal of application of methods of selection of the characteristics of the archaeological images. Finally, we calculate the performance of two steps already mentioned: the extraction of characteristics and the selection of characteristics.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Gogineni:2022:TSP, author = "Rajesh Gogineni and Dhara J. Sangani", title = "A Two-Stage {PAN}-Sharpening Algorithm Based on Sparse Representation for Spectral Distortion Reduction", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "01", pages = "??--??", month = jan, year = "2022", DOI = "https://doi.org/10.1142/S0219467822500073", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Feb 9 07:11:50 MST 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500073", abstract = "Inspite of technological advancement, inherent processing capability of current age sensors limits the desired details in the acquired image for variety of remote sensing applications. Pan-sharpening is a prominent scheme to integrate the essential spatial details inferred from panchromatic (PAN) image and the desired spectral information of multispectral (MS) image. This paper presents an effective two-stage pan-sharpening method to produce high resolution multispectral (HRMS) image. The proposed method is based on the premise that the HRMS image can be formulated as an amalgam of spectral and spatial components. The spectral components are estimated by processing the interpolated MS image with a filter approximated with modulation transfer function (MTF) of the sensor. Sparse representation theory is adapted to construct the spatial components. The high-frequency details extracted from the PAN image and its low resolution variant are utilized to construct dual dictionaries. The dictionaries are jointly learned by an efficient training algorithm to enhance the adaptability. The hypothesis of sparse coefficients invariance over scales is also incorporated to reckon the appropriate spatial information. Further, an iterative filtering mechanism is developed to enhance the quality of fused image. Four distinct datasets generated from QuickBird, IKONOS, Pl{\'e}iades and WorldView-2 sensors are used for experimentation. The comprehensive assessment at reduced-scale and full-scale persuade the effectiveness of proposed method in the retention of spectral information and intensification of the spatial details.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Dhar:2022:SRR, author = "Soumi Dhar and Shyamosree Pal", title = "Surface Reconstruction: Roles in the Field of Computer Vision and Computer Graphics", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "01", pages = "??--??", month = jan, year = "2022", DOI = "https://doi.org/10.1142/S0219467822500085", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Feb 9 07:11:50 MST 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500085", abstract = "Surface Reconstruction is the most potent aspect of 3D computer vision. It allows recapturing or imitating of the shape of real objects. It also provides sufficient knowledge regarding the mathematical foundation for rendering applications which are widely used for analyzing medical volume data, modeling, 3D interior designing, architectural designing. In our paper, we have mentioned various algorithms and approaches for surface reconstruction and their applications. Moreover, we have tried to emphasize the necessity of surface reconstruction for solving image analysis related problem.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kushwaha:2022:HAR, author = "Arati Kushwaha and Ashish Khare and Manish Khare", title = "Human Activity Recognition Algorithm in Video Sequences Based on Integration of Magnitude and Orientation Information of Optical Flow", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "01", pages = "??--??", month = jan, year = "2022", DOI = "https://doi.org/10.1142/S0219467822500097", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Feb 9 07:11:50 MST 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500097", abstract = "Human activity recognition from video sequences has emerged recently as pivotal research area due to its importance in a large number of applications such as real-time surveillance monitoring, healthcare, smart homes, security, behavior analysis, and many more. However, lots of challenges also exist such as intra-class variations, object occlusion, varying illumination condition, complex background, camera motion, etc. In this work, we introduce a novel feature descriptor based on the integration of magnitude and orientation information of optical flow and histogram of oriented gradients which gives an efficient and robust feature vector for the recognition of human activities for real-world environment. In the proposed approach first we computed magnitude and orientation of the optical flow separately then a local-oriented histogram of magnitude and orientation of motion flow vectors are computed using histogram of oriented gradients followed by linear combination feature fusion strategy. The resultant features are then processed by a multiclass Support Vector Machine (SVM) classifier for activity recognition. The experimental results are performed over different publically available benchmark video datasets such as UT interaction, CASIA, and HMDB51 datasets. The effectiveness of the proposed approach is evaluated in terms of six different performance parameters such as accuracy, precision, recall, specificity, F -measure, and Matthew's correlation coefficient (MCC). To show the significance of the proposed method, it is compared with the other state-of-the-art methods. The experimental result shows that the proposed method performs well in comparison to other state-of-the-art methods.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Ravikumar:2022:EMB, author = "M. Ravikumar and B. J. Shivaprasad and D. S. Guru", title = "Enhancement of {MRI} Brain Images Using Notch Filter Based on Discrete Wavelet Transform", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "01", pages = "??--??", month = jan, year = "2022", DOI = "https://doi.org/10.1142/S0219467822500103", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Feb 9 07:11:50 MST 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500103", abstract = "In this work, we have proposed Notch filter method to enhance MRI brain images. The proposed method performs better when compared with the existing methods from the literature. The performance is evaluated using quantitative measures like Michelon Contrast (MC), entropy, Peak Signal-to-Noise Ratio (PSNR), Structure Similarity Index Measurement (SSIM) and Absolute Mean Brightness Error (AMBE), as a parameter on publically available BRATS-2018 & 2019 dataset. Overall, the proposed method performs well in comparison to the other existing methods.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Bhardwaj:2022:IAU, author = "Anuj Bhardwaj and Vivek Singh Verma and Sandesh Gupta", title = "Image Authentication Using Block Truncation Coding in Lifting Wavelet Domain", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "01", pages = "??--??", month = jan, year = "2022", DOI = "https://doi.org/10.1142/S0219467822500115", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Feb 9 07:11:50 MST 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500115", abstract = "Image watermarking is one of the most accepted solutions protecting image authenticity. The method presented in this paper not only provides the desired outcome also efficient in terms of memory requirements and preserving image characteristics. This scheme effectively utilizes the concepts of block truncation coding (BTC) and lifting wavelet transform (LWT). The BTC method is applied to observe the binary watermark image corresponding to its gray-scale image. Whereas, the LWT is incorporated to transform the cover image from spatial coordinates to corresponding transform coordinates. In this, a quantization-based approach for watermark bit embedding is applied. And, the extraction of binary watermark data from the attacked watermarked image is based on adaptive thresholding. To show the effectiveness of the proposed scheme, the experiment over different benchmark images is performed. The experimental results and the comparison with state-of-the-art schemes depict not only the good imperceptibility but also high robustness against various attacks.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Ehsaeyan:2022:SDC, author = "Ehsan Ehsaeyan and Alireza Zolghadrasli", title = "A Study on {Darwinian} Crow Search Algorithm for Multilevel Thresholding", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "01", pages = "??--??", month = jan, year = "2022", DOI = "https://doi.org/10.1142/S0219467822500127", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Feb 9 07:11:50 MST 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500127", abstract = "Multilevel thresholding is a basic method in image segmentation. The conventional image multilevel thresholding algorithms are computationally expensive when the number of decomposed segments is high. In this paper, a novel and powerful technique is suggested for Crow Search Algorithm (CSA) devoted to segmentation applications. The main contribution of our work is to adapt Darwinian evolutionary theory with heuristic CSA. First, the population is divided into specified groups and each group tries to find better location in the search space. A policy of encouragement and punishment is set on searching agents to avoid being trapped in the local optimum and premature solutions. Moreover, to increase the convergence rate of the proposed method, a gray-scale map is applied to out-boundary agents. Ten test images are selected to measure the ability of our algorithm, compared with the famous procedure, energy curve method. Two popular entropies i.e. Otsu and Kapur are employed to evaluate the capability of the introduced algorithm. Eight different search algorithms are implemented and compared to the introduced method. The obtained results show that our method, compared with the original CSA, and other heuristic search methods, can extract multi-level thresholding more efficiently.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Boucetta:2022:BAU, author = "Aldjia Boucetta and Leila Boussaad", title = "Biometric Authentication Using Finger-Vein Patterns with Deep-Learning and Discriminant Correlation Analysis", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "01", pages = "??--??", month = jan, year = "2022", DOI = "https://doi.org/10.1142/S0219467822500139", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Feb 9 07:11:50 MST 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500139", abstract = "Finger-vein identification, a biometric technology that uses vein patterns in the human finger to identify people. In recent years, it has received increasing attention due to its tremendous advantages compared to fingerprint characteristics. Moreover, Deep-Convolutional Neural Networks (Deep-CNN) appeared to be highly successful for feature extraction in the finger-vein area, and most of the proposed works focus on new Convolutional Neural Network (CNN) models, which require huge databases for training, a solution that may be more practicable in real world applications, is to reuse pretrained Deep-CNN models. In this paper, a finger-vein identification system is proposed, which uses Squeezenet pretrained Deep-CNN model as feature extractor from the left and the right finger vein patterns. Then, combines this Deep-based features by using a feature-level Discriminant Correlation Analysis (DCA) to reduce feature dimensions and to give the most relevant features. Finally, these composite feature vectors are used as input data for a Support Vector Machine (SVM) classifier, in an identification stage. This method is tested on two widely available finger vein databases, namely SDUMLA-HMT and FV-USM. Experimental results show that the proposed finger vein identification system achieves significant high mean accuracy rates.", acknowledgement = ack-nhfb, fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Gajabe:2022:SKB, author = "Rajashree Gajabe and Syed Taqi Ali", title = "Secret Key-Based Image Steganography in Spatial Domain", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "02", pages = "??--??", month = apr, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467822500140", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri May 6 07:27:02 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/cryptography2020.bib; https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500140", abstract = "Day by day, the requirement for secure communication among users is rising in a digital world, to protect the message from the undesirable users. Steganography is a methodology that satisfies the user's necessity of secure communication by inserting a message into different formats. This paper proposes a secret key-based image steganography to secure the message by concealing the grayscale image inside a cover image. The proposed technique shares the 20 characters long secret key between two clients where the initial eight characters of a secret key are utilized for bit permutation of characters and pixels while the last 12 characters of secret key decide the encryption keys and position of pixels of a grayscale image into the cover. The grayscale image undergoes operation such as encryption and chaotic baker followed by its hiding in a cover to form a stego image. The execution of the proposed strategy is performed on Matlab 2018. It shows that the proposed approach manages to store the maximum message of size 16 KB into the cover of size 256{\texttimes}256. The image quality of stego images has been evaluated using PSNR, MSE. For a full payload of 16 KB, PSNR is around 51 dB to 53 dB which is greater than satisfactory PSNR.", acknowledgement = ack-nhfb, articleno = "2250014", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Gagaoua:2022:HMB, author = "Meriem Gagaoua and Hamza Ghilas and Abdelkamel Tari and Mohamed Cheriet", title = "Histogram of Marked Background {(HMB)} Feature Extraction Method for {Arabic} Handwriting Recognition", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "02", pages = "??--??", month = apr, year = "2022", DOI = "https://doi.org/10.1142/S0219467822500152", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri May 6 07:27:02 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500152", abstract = "Features extraction is one of the most important steps in handwriting recognition systems. In this paper, we propose a novel features extraction method, which is adapted to the complex nature of Arabic handwriting. The proposed feature called histogram of marked background (HMB) is not considering only ink pixels in a text image, but also uses the background of the image. Each background pixel in the text image was marked according to the repartition of ink pixels in its neighborhood. Feature vectors are extracted by computing histograms from the marked images. Hidden Markov models (HMMs) with Hidden Markov model toolkit (HTK) were used in the recognition process. The experiments were performed on two datasets: IBN SINA database of historical Arabic documents and Isolated Farsi Handwritten Character Database (IFHCDB). The proposed feature in this study produced efficient and promising results for Arabic handwriting recognition, for both isolated characters and for historical documents.", acknowledgement = ack-nhfb, articleno = "2250015", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Rashwan:2022:MFW, author = "Shaheera Rashwan and Walaa Sheta", title = "A Metaheuristics Framework for Weighted Multi-band Image Fusion", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "02", pages = "??--??", month = apr, year = "2022", DOI = "https://doi.org/10.1142/S0219467822500164", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri May 6 07:27:02 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500164", abstract = "The main objective of hyper/multispectral image fusion is producing a composite color image that allows for an appropriate visualization of the relevant spatial and spectral information. In this paper, we propose a general framework for spectral weighting-based image fusion. The proposed methodology relies on weight updates conducted using nature-inspired algorithms and a goodness-of-fit criterion defined as the average root mean square error. Simulations on four public data sets and a recent Landsat 8 image of Brullus Lake, Egypt, as an area of study prove the efficiency of the proposed framework. The purpose of the study is to present a framework of multi-band image fusion that produces a fused image of high quality to be further used in computer processing and the results show that the image produced by the presented framework has the highest quality compared with some of the state-of-the art algorithms. To prove the increase in the image quality, we used general quality metrics such as Universal Image Quality Index, Mutual Information, the Variance and Information Measure.", acknowledgement = ack-nhfb, articleno = "2250016", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Vaidya:2022:HEM, author = "Bhaumik Vaidya and Chirag Paunwala", title = "Hardware Efficient Modified {CNN} Architecture for Traffic Sign Detection and Recognition", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "02", pages = "??--??", month = apr, year = "2022", DOI = "https://doi.org/10.1142/S0219467822500176", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri May 6 07:27:02 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500176", abstract = "Traffic sign recognition is a vital part for any driver assistance system which can help in making complex driving decision based on the detected traffic signs. Traffic sign detection (TSD) is essential in adverse weather conditions or when the vehicle is being driven on the hilly roads. Traffic sign recognition is a complex computer vision problem as generally the signs occupy a very small portion of the entire image. A lot of research is going on to solve this issue accurately but still it has not been solved till the satisfactory performance. The goal of this paper is to propose a deep learning architecture which can be deployed on embedded platforms for driver assistant system with limited memory and computing resources without sacrificing on detection accuracy. The architecture uses various architectural modification to the well-known Convolutional Neural Network (CNN) architecture for object detection. It uses a trainable Color Transformer Network (CTN) with the existing CNN architecture for making the system invariant to illumination and light changes. The architecture uses feature fusion module for detecting small traffic signs accurately. In the proposed work, receptive field calculation is used for choosing the number of convolutional layer for prediction and the right scales for default bounding boxes. The architecture is deployed on Jetson Nano GPU Embedded development board for performance evaluation at the edge and it has been tested on well-known German Traffic Sign Detection Benchmark (GTSDB) and Tsinghua-Tencent 100k dataset. The architecture only requires 11 MB for storage which is almost ten times better than the previous architectures. The architecture has one sixth parameters than the best performing architecture and 50 times less floating point operations per second (FLOPs). The architecture achieves running time of 220 ms on desktop GPU and 578 ms on Jetson Nano which is also better compared to other similar implementation. It also achieves comparable accuracy in terms of mean average precision (mAP) for both the datasets.", acknowledgement = ack-nhfb, articleno = "2250017", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Bommisetty:2022:CBV, author = "Reddy Mounika Bommisetty and Ashish Khare and Manish Khare and P. Palanisamy", title = "Content-Based Video Retrieval Using Integration of Curvelet Transform and Simple Linear Iterative Clustering", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "02", pages = "??--??", month = apr, year = "2022", DOI = "https://doi.org/10.1142/S0219467822500188", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri May 6 07:27:02 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500188", abstract = "Video is a rich information source containing both audio and visual information along with motion information embedded in it. Applications such as e-learning, live TV, video on demand, traffic monitoring, etc. need an efficient video retrieval strategy. Content-based video retrieval and superpixel segmentation are two diverse application areas of computer vision. In this work, we are presenting an algorithm for content-based video retrieval with help of Integration of Curvelet transform and Simple Linear Iterative Clustering (ICTSLIC) algorithm. Proposed algorithm consists of two steps: off line processing and online processing. In offline processing, keyframes of the database videos are extracted by employing features: Pearson Correlation Coefficient (PCC) and color moments (CM) and on the extracted keyframes superpixel generation algorithm ICTSLIC is applied. The superpixels generated by applying ICTSLIC on keyframes are used to represent database videos. On other side, in online processing, ICTSLIC superpixel segmentation is applied on query frame and the superpixels generated by segmentation are used to represent query frame. Then videos similar to query frame are retrieved through matching done by calculation of Euclidean distance between superpixels of query frame and database keyframes. Results of the proposed method are irrespective of query frame features such as camera motion, object's pose, orientation and motion due to the incorporation of ICTSLIC superpixels as base feature for matching and retrieval purpose. The proposed method is tested on the dataset comprising of different categories of video clips such as animations, serials, personal interviews, news, movies and songs which is publicly available. For evaluation, the proposed method randomly picks frames from database videos, instead of selecting keyframes as query frames. Experiments were conducted on the developed dataset and the performance is assessed with different parameters Precision, Recall, Jaccard Index, Accuracy and Specificity. The experimental results shown that the proposed method is performing better than the other state-of-art methods.", acknowledgement = ack-nhfb, articleno = "2250018", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Khan:2022:MPS, author = "Rafflesia Khan and Rameswar Debnath", title = "Morphology Preserving Segmentation Method for Occluded Cell Nuclei from Medical Microscopy Image", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "02", pages = "??--??", month = apr, year = "2022", DOI = "https://doi.org/10.1142/S021946782250019X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri May 6 07:27:02 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S021946782250019X", abstract = "Nowadays, image segmentation techniques are being used in many medical applications such as tissue culture monitoring, cell counting, automatic measurement of organs, etc., for assisting doctors. However, high-level segmentation results cannot be obtained without manual annotation or prior knowledge for high variability, noise and other imaging artifacts in medical images. Furthermore, unstable and continuously changing characteristics of all human cells, tissues and organs manipulate training-based segmentation methods. Detecting appropriate contour of a region of interest and single cells from overlapping condition are extremely challenging. In this paper, we aim for a model that can detect biological structure (e.g. cell nuclei and lung contour) with their proper morphology even in overlapping or occluded condition without manual annotation or prior knowledge. We have introduced a new optimal approach for automatic medical image region segmentation. The method first clearly focuses the boundaries of all object regions in a microscopy image. Then it detects the areas by following their contours. Our model is capable of detecting and segmenting object regions from medial image using less computation effort. Our experimental results prove that our model provides better detection on several datasets of different types of medical data and ensures more than 98\% segmentation rate in the case of densely connected regions.", acknowledgement = ack-nhfb, articleno = "2250019", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Tanveer:2022:EIP, author = "Muhammad Tanveer and Tariq Shah and Asif Ali and Dawood Shah", title = "An Efficient Image Privacy-Preserving Scheme Based On Mixed Chaotic Map and Compression", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "02", pages = "??--??", month = apr, year = "2022", DOI = "https://doi.org/10.1142/S0219467822500206", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri May 6 07:27:02 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500206", abstract = "In the digital modern era, multimedia security has turned into a major concern by the rapid growth of network technologies and digital communications. Accordingly, from the last few decades, the application of nonlinear dynamics and chaotic phenomena for multimedia data security earn significant attention. In this paper, an efficient image-encryption technique based on a two-dimensional (2D) chaotic system combine with the finite field of the specific order is introduced. The proposed scheme consists of four modules which are the separation of bits, compression, 2D chaotic map, and small S-boxes. Initially, the suggested scheme separates the pixels of the image into the least significant bits (LSB) and the most significant bits (MSB). Subsequently, the compression algorithm on these separated bits is applied and instantly transformed the MSB of the image into LSB. The key objective of the first module is to minimize the range of the pixel value up to eight times less than the original image, which consequently reduces the time complexity of the scheme. In the end, a 2D chaotic map is used to reshuffle the bytes to interrupt the internal correlation amongst the pixels of the image. At the tail end, the small S-boxes have been used to substitute the permuted image. The significance of small S-boxes plays a vital role to maintain the optimum security level, prevent computational effort, and reduced time complexity. The result of the suggested encryption system is tailor-made for instantaneous communication.", acknowledgement = ack-nhfb, articleno = "2250020", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Chaudhari:2022:IFU, author = "Chaitrali Prasanna Chaudhari and Satish Devane", title = "Improved Framework using Rider Optimization Algorithm for Precise Image Caption Generation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "02", pages = "??--??", month = apr, year = "2022", DOI = "https://doi.org/10.1142/S0219467822500218", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri May 6 07:27:02 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500218", abstract = "``Image Captioning is the process of generating a textual description of an image''. It deploys both computer vision and natural language processing for caption generation. However, the majority of the image captioning systems offer unclear depictions regarding the objects like ``man'', ``woman'', ``group of people'', ``building'', etc. Hence, this paper intends to develop an intelligent-based image captioning model. The adopted model comprises of few steps like word generation, sentence formation, and caption generation. Initially, the input image is subjected to the Deep learning classifier called Convolutional Neural Network (CNN). Since the classifier is already trained in the relevant words that are related to all images, it can easily classify the associated words of the given image. Further, a set of sentences is formed with the generated words using Long-Short Term Memory (LSTM) model. The likelihood of the formed sentences is computed using the Maximum Likelihood (ML) function, and the sentences with higher probability are taken, which is further used for generating the visual representation of the scene in terms of image caption. As a major novelty, this paper aims to enhance the performance of CNN by optimally tuning its weight and activation function. This paper introduces a new enhanced optimization algorithm Rider with Randomized Bypass and Over-taker update (RR-BOU) for this optimal selection. In the proposed RR-BOU is the enhanced version of the Rider Optimization Algorithm (ROA). Finally, the performance of the proposed captioning model is compared over other conventional models with respect to statistical analysis.", acknowledgement = ack-nhfb, articleno = "2250021", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Hamroun:2022:MVI, author = "Mohamed Hamroun and Karim Tamine and Beno{\^\i}t Crespin", title = "Multimodal Video Indexing {(MVI)}: a New Method Based on Machine Learning and Semi-Automatic Annotation on Large Video Collections", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "02", pages = "??--??", month = apr, year = "2022", DOI = "https://doi.org/10.1142/S021946782250022X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri May 6 07:27:02 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S021946782250022X", abstract = "Indexing video by the concept is one of the most appropriate solutions for such problems. It is based on an association between a concept and its corresponding visual sound, or textual features. This kind of association is not a trivial task. It requires knowledge about the concept and its context. In this paper, we investigate a new concept detection approach to improve the performance of content-based multimedia documents retrieval systems. To achieve this goal, we are going to tackle the problem from different plans and make four contributions at various stages of the indexing process. We propose a new method for multimodal indexation based on (i) a new weakly supervised semi-automatic method based on the genetic algorithm (ii) the detection of concepts from the text in the videos (iii) the enrichment of the basic concepts thanks to the usage of our method DCM. Subsequently, the semantic and enriched concepts allow a better multimodal indexation and the construction of an ontology. Finally, the different contributions are tested and evaluated on a large dataset (TRECVID 2015).", acknowledgement = ack-nhfb, articleno = "2250022", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Shivaprasad:2022:ABT, author = "B. J. Shivaprasad and M. Ravikumar and D. S. Guru", title = "Analysis of Brain Tumor Using {MR} Images: a Brief Survey", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "02", pages = "??--??", month = apr, year = "2022", DOI = "https://doi.org/10.1142/S0219467822500231", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri May 6 07:27:02 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500231", abstract = "In this paper, we have discussed in detail about detection and extraction of brain tumor from MRI technique, where the importance of using MRI is also highlighted. Various features extraction methods and classifiers are explained in brain tumor segmentation. This paper mainly focuses on challenges involved in brain tumor analysis, which is helpful for researchers and those who are interested to carry out their research on this topic.", acknowledgement = ack-nhfb, articleno = "2250023", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Khadilkar:2022:CCD, author = "Samrat Pundalik Khadilkar", title = "Colon Cancer Detection Using Hybrid Features and Genetically Optimized Neural Network Classifier", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "02", pages = "??--??", month = apr, year = "2022", DOI = "https://doi.org/10.1142/S0219467822500243", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri May 6 07:27:02 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500243", abstract = "Computer-assisted colon cancer detection on the histopathological images has become a tedious task due to its shape characteristics and other biological properties. The images acquired through the histopathological microscope may vary in magnifications for better visibility. This may change the morphological properties and hence an automated magnification independent colon cancer detection system is essential. The manual diagnosis of colon biopsy images is subjective, sluggish, laborious leading to nonconformity between histopathologists due to visual evaluation at various microscopic magnifications. Automatic detection of colon across image magnifications is challenging due to many aspects like tailored segmentation and varying features. This demands techniques that take advantage of the textural, color, and geometric properties of colon tissue. This work exhibits a segmentation approach based on the morphological features derived from the segmented region. Gabor Wavelet, Harris Corner, and DWT-LBP coefficients are extracted as it should not be dependent on the spatial domain with respect to the magnification. These features are fed to the Genetically Optimized Neural Network classifier to classify them as normal and malignant ones. Here, the genetic algorithm is used to learn the best hyper-parameters for a neural network.", acknowledgement = ack-nhfb, articleno = "2250024", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Sridhar:2022:PAT, author = "S. Sridhar and A. Kalaivani", title = "Performance Analysis of Two-Stage Iterative Ensemble Method over Random Oversampling Methods on Multiclass Imbalanced Datasets", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "02", pages = "??--??", month = apr, year = "2022", DOI = "https://doi.org/10.1142/S0219467822500255", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri May 6 07:27:02 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500255", abstract = "Data imbalance occurring among multiclass datasets is very common in real-world applications. Existing studies reveal that various attempts were made in the past to overcome this multiclass imbalance problem, which is a severe issue related to the typical supervised machine learning methods such as classification and regression. But, still there exists a need to handle the imbalance problem efficiently as the datasets include both safe and unsafe minority samples. Most of the widely used oversampling techniques like SMOTE and its variants face challenges in replicating or generating the new data instances for balancing them across multiple classes, particularly when the imbalance is high and the number of rare samples count is too minimal thus leading the classifier to misclassify the data instances. To lessen this problem, we proposed a new data balancing method namely a two-stage iterative ensemble method to tackle the imbalance in multiclass environment. The proposed approach focuses on the rare minority sample's influence on learning from imbalanced datasets and the main idea of the proposed approach is to balance the data without any change in class distribution before it gets trained by the learner such that it improves the learner's learning process. Also, the proposed approach is compared against two widely used oversampling techniques and the results reveals that the proposed approach shows a much significant improvement in the learning process among the multiclass imbalanced data.", acknowledgement = ack-nhfb, articleno = "2250025", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Jayaraman:2022:MFP, author = "Kumaran @ Kumar Jayaraman and Koganti Srilakshmi and Sasikala Jayaraman", title = "Modified Flower Pollination-based Segmentation of Medical Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "02", pages = "??--??", month = apr, year = "2022", DOI = "https://doi.org/10.1142/S0219467822500267", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri May 6 07:27:02 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500267", abstract = "This paper presents a modified flower pollination-based method for performing multilevel segmentation of medical images. The flower pollination-based optimization (FPO) models the pollination process of flowers. Bees serve a major role in the pollination activity of flowers and they memorize and recognize the best flowers producing large pollens of nectar. Such memorizing ability of bees is adapted in the FPO for improving the exploration ability of the algorithm. Besides, the mechanism of avoiding predators by pollinators is also included in the modified FPO (MFPO) for getting away from sub-optimal traps. The medical image segmentation problem is transformed into an optimization problem and solved using the modified FPO (MFPO). The method explores for optimal thresholds in the problem space of the given medical image. The segmented images are presented for showing the superior performance of the proposed method.", acknowledgement = ack-nhfb, articleno = "2250026", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Patel:2022:AET, author = "Alpesh M. Patel and Anil Suthar", title = "{AdaBoosted} Extra Trees Classifier for Object-Based Multispectral Image Classification of Urban Fringe Area", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "03", pages = "??--??", month = may, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467821400064", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue May 31 06:44:45 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821400064", abstract = "In the past decade, it is proven that satellite image classification using an object-based technique is better than the standard pixel-based technique. With the increasing need for classifying multispectral satellite images for urban planning, the accuracy of the classification becomes a significant performance parameter. Object-based classification (OBC) is a technique in which group of pixels having similar spectral properties, called objects, are generated using image segmentation and then these objects are classified based on their attributes. In this paper, the combination of a multiclass AdaBoost algorithm with extra trees classifier (ETC) is proposed with higher prediction accuracy for the OBC of the urban fringe area. The performance of the AdaBoost algorithm is found to be better in terms of classification accuracy than benchmarked SVM and RF classifiers for OBC. These classification methods were applied to IRS-R2 LISS IV data. The AdaBoosted extra trees classifier (ABETC) has demonstrated the highest accuracy with overall accuracy (OA) of 88.47\% and a kappa coefficient of 0.85. The computational time of the ABETC is found to be much smaller than the RF algorithm. In detail, the sensitivity of the classifiers was investigated using stratified random sampling with various sample sizes.", acknowledgement = ack-nhfb, articleno = "2140006", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", remark = "Special Issue on Innovations in Image Processing using Machine Learning", } @Article{Hans:2022:HBB, author = "Rahul Hans and Harjot Kaur", title = "Hybrid Biogeography-Based Optimization and Genetic Algorithm for Feature Selection in Mammographic Breast Density Classification", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "03", pages = "??--??", month = may, year = "2022", DOI = "https://doi.org/10.1142/S0219467821400076", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue May 31 06:44:45 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821400076", abstract = "It can be acknowledged from the literature that the high density of breast tissue is a root cause for the escalation of breast cancer among the women, imparting its prime role in Cancer Death among women. Moreover, in this era where computer-aided diagnosis systems have become the right hand of the radiologists, the researchers still find room for improvement in the feature selection techniques. This research aspires to propose hybrid versions of Biogeography-Based Optimization and Genetic Algorithm for feature selection in Breast Density Classification, to get rid of redundant and irrelevant features from the dataset; along with it to achieve the superior classification accuracy or to uphold the same accuracy with lesser number of features. For experimentation, 322 mammogram images from mini-MIAS database are chosen, and then Region of Interests (ROI) of seven different sizes are extracted to extract a set of 45 texture features corresponding to each ROI. Subsequently, the proposed algorithms are used to extract an optimal subset of features from the hefty set of features corresponding to each ROI. The results indicate the outperformance of the proposed algorithms when results were compared with some of the other nature-inspired metaheuristic algorithms using various parameters.", acknowledgement = ack-nhfb, articleno = "2140007", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", remark = "Special Issue on Innovations in Image Processing using Machine Learning", } @Article{Sagayam:2022:RHG, author = "K. Martin Sagayam and A. Diana Andrushia and Ahona Ghosh and Omer Deperlioglu and Ahmed A. Elngar", title = "Recognition of Hand Gesture Image Using Deep Convolutional Neural Network", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "03", pages = "??--??", month = may, year = "2022", DOI = "https://doi.org/10.1142/S0219467821400088", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue May 31 06:44:45 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821400088", abstract = "In recent technology, there is tremendous growth in computer applications that highlight human--computer interaction (HCI), such as augmented reality (AR), and Internet of Things (IoT). As a consequence, hand gesture recognition was highlighted as a very up-to-date research area in computer vision. The body language is a vital method to communicate between people, as well as emphasis on voice messages, or as a complete message on its own. Thus, automatic hand gestures recognition systems can be used to increase human--computer interaction. Therefore, many approaches for hand gesture recognition systems have been designed. However, most of these methods include hybrid processes such as image pre-processing, segmentation, and classification. This paper describes how to create hand gesture model easily and quickly with a well-tuned deep convolutional neural network. Experiments were performed using the Cambridge Hand Gesture data set for illustration of success and efficiency of the convolutional neural network. The accuracy was achieved as 96.66\%, where sensitivity and specificity were found to be 85\% and 98.12\%, respectively, according to the average values obtained at the end of 20 times of operation. These results were compared with the existing works using the same dataset and it was found to have higher values than the hybrid methods.", acknowledgement = ack-nhfb, articleno = "2140008", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", remark = "Special Issue on Innovations in Image Processing using Machine Learning", } @Article{Singh:2022:EMD, author = "Swati Singh and Sheifali Gupta and Ankush Tanta and Rupesh Gupta", title = "Extraction of Multiple Diseases in Apple Leaf Using Machine Learning", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "03", pages = "??--??", month = may, year = "2022", DOI = "https://doi.org/10.1142/S021946782140009X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue May 31 06:44:45 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S021946782140009X", abstract = "This paper proposes a novel algorithm of segmentation of diseased part in apple leaf images. In agriculture-based image processing, leaf diseases segmentation is the main processing task for region of interest extraction. It is also extremely important to segment the plant leaf from the background in case on live images. Automated segmentation of plant leaves from the background is a common challenge in the processing of plant images. Although numerous methods have been proposed, still it is tough to segment the diseased part of the leaf from the live leaf images accurately by one particular method. In the proposed work, leaves of apple having different background have been segmented. Firstly, the leaves have been enhanced by using Brightness-Preserving Dynamic Fuzzy Histogram Equalization technique and then the extraction of diseased apple leaf part is done using a novel extraction algorithm. Real-time plant leaf database is used to validate the proposed approach. The results of the proposed novel methodology give better results when compared to existing segmentation algorithms. From the segmented apple leaves, color and texture features are extracted which are further classified as marsonina coronaria or apple scab using different machine learning classifiers. Best accuracy of 96.4\% is achieved using K nearest neighbor classifier.", acknowledgement = ack-nhfb, articleno = "2140009", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", remark = "Special Issue on Innovations in Image Processing using Machine Learning", } @Article{Gupta:2022:SSC, author = "Anuj Kumar Gupta and Manvinder Sharma and Ankit Sharma and Vikas Menon", title = "A Study on {SARS-CoV-2 (COVID-19)} and Machine Learning Based Approach to Detect {COVID-19} Through {X}-Ray Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "03", pages = "??--??", month = may, year = "2022", DOI = "https://doi.org/10.1142/S0219467821400106", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue May 31 06:44:45 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821400106", abstract = "From origin in Wuhan city of China, a highly communicable and deadly virus is spreading in the entire world and is known as COVID-19. COVID-19 is a new species of coronavirus which is affecting respiratory system of human. The virus is known as severe acute respiratory syndrome (SARS) coronavirus 2 abbreviated as SARS-CoV-2 and generally known as coronavirus disease COVID-19. This is growing day by day in countries. The symptoms include fever, cough and difficulty in breathing. As there is no vaccine made for this virus and COVID-19 tests are not readily available, this is causing panic. Various Artificial Intelligence-based algorithms and frameworks are being developed to detect this virus, but it has not been tested. People are taking advantages of others by providing duplicate COVID-19 test kits. A work is carried out with deep learning to detect presence of COVID 19. With the use of Convolutional Neural networks, the model is trained with dataset of COVID-19 positive and negative X-Rays. The accuracy of training model is 99\% and the confusion matrix shows 98\% values that are predicted truly. Hence, the model is able to detect the presence of COVID-19.", acknowledgement = ack-nhfb, articleno = "2140010", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", remark = "Special Issue on Innovations in Image Processing using Machine Learning", } @Article{Rani:2022:SIP, author = "Rajneesh Rani and Renu Dhir and Deepti Kakkar and Nonita Sharma", title = "Script Identification for Printed and Handwritten {Indian} Documents: an Empirical Study of Different Feature Classifier Combinations", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "03", pages = "??--??", month = may, year = "2022", DOI = "https://doi.org/10.1142/S0219467821400118", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue May 31 06:44:45 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821400118", abstract = "The identification of script in a document page image is the first step for an OCR system processing multi-script documents. In this multilingual/multiscript world, document processing systems relying on the OCR that need human involvement to select the appropriate OCR package is definitely undesirable and inefficient. The development of robust and efficient methods for automatic script identification of a document is a subject of major importance for automatic document processing in a multilingual/multiscript environment. Thus, the basic objective is to come up with some intuitive methods having straightforward implementation without compromising with efficiency. The aim of this work is to evaluate state-of-the-art feature extraction and classification techniques in the field of automatic script identification of printed and handwritten documents and to propose the best combination for the same.", acknowledgement = ack-nhfb, articleno = "2140011", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", remark = "Special Issue on Innovations in Image Processing using Machine Learning", } @Article{Kaur:2022:DAD, author = "Swapandeep Kaur and Sheifali Gupta and Swati Singh and Isha Gupta", title = "Detection of {Alzheimer}'s Disease Using Deep Convolutional Neural Network", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "03", pages = "??--??", month = may, year = "2022", DOI = "https://doi.org/10.1142/S021946782140012X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue May 31 06:44:45 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S021946782140012X", abstract = "Alzheimer's disease (AD) is a disease that gradually develops and causes degeneration of the cells of the brain. The leading cause of AD is dementia that results in a person's inability to work independently. In the early stages of AD, a person forgets recent conversations or the occurrence of an event. In the later stages, there could be severe loss of memory such that the person is not able to even perform everyday tasks. The medicines currently available for AD may improve its symptoms on a temporary basis in the early stage of the disease. Since no treatment is available for curing AD, its detection becomes extremely important. As the clinical treatments are very expensive, the need for automated diagnosis of AD is of critical importance. In this paper, a deep learning model based on a convolutional neural network has been used and applied to four classes of images of AD that is very mild demented, mild demented, average demented, and non-demented. It was found that the moderate demented class had the highest accuracy of 98.9\%, a classification error rate of 0.01, and a specificity of 0.992. Also, the lowest false positive rate of 0.007 was obtained.", acknowledgement = ack-nhfb, articleno = "2140012", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", remark = "Special Issue on Innovations in Image Processing using Machine Learning", } @Article{Gore:2022:IBR, author = "Sonal Gore and Jayant Jagtap", title = "{IDH}-Based Radiogenomic Characterization of Glioma Using Local Ternary Pattern Descriptor Integrated with Radiographic Features and Random Forest Classifier", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "03", pages = "??--??", month = may, year = "2022", DOI = "https://doi.org/10.1142/S0219467821400131", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue May 31 06:44:45 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821400131", abstract = "Mutations in family of Isocitrate Dehydrogenase (IDH) gene occur early in oncogenesis, especially with glioma brain tumor. Molecular diagnostic of glioma using machine learning has grabbed attention to some extent from last couple of years. The development of molecular-level predictive approach carries great potential in radiogenomic field. But more focused efforts need to be put to develop such approaches. This study aims to develop an integrative genomic diagnostic method to assess the significant utility of textures combined with other radiographic and clinical features for IDH classification of glioma into IDH mutant and IDH wild type. Random forest classifier is used for classification of combined set of clinical features and radiographic features extracted from axial T2-weighted Magnetic Resonance Imaging (MRI) images of low- and high-grade glioma. Such radiogenomic analysis is performed on The Cancer Genome Atlas (TCGA) data of 74 patients of IDH mutant and 104 patients of IDH wild type. Texture features are extracted using uniform, rotation invariant Local Ternary Pattern (LTP) method. Other features such as shape, first-order statistics, image contrast-based, clinical data like age, histologic grade are combined with LTP features for IDH discrimination. Proposed random forest-assisted model achieved an accuracy of 85.89\% with multivariate analysis of integrated set of feature descriptors using Glioblastoma and Low-Grade Glioma dataset available with The Cancer Imaging Archive (TCIA). Such an integrated feature analysis using LTP textures and other descriptors can effectively predict molecular class of glioma as IDH mutant and wild type.", acknowledgement = ack-nhfb, articleno = "2140013", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", remark = "Special Issue on Innovations in Image Processing using Machine Learning", } @Article{Singh:2022:OAI, author = "Rishipal Singh and Rajneesh Rani and Aman Kamboj", title = "An Optimized Approach for Intra-Class Fruit Classification Using Deep Convolutional Neural Network", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "03", pages = "??--??", month = may, year = "2022", DOI = "https://doi.org/10.1142/S0219467821400143", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue May 31 06:44:45 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467821400143", abstract = "Fruits classification is one of the influential applications of computer vision. Traditional classification models are trained by considering various features such as color, shape, texture, etc. These features are common for different varieties of the same fruit. Therefore, a new set of features is required to classify the fruits belonging to the same class. In this paper, we have proposed an optimized method to classify intra-class fruits using deep convolutional layers. The proposed architecture is capable of solving the challenges of a commercial tray-based system in the supermarket. As the research in intra-class classification is still in its infancy, there are challenges that have not been tackled. So, the proposed method is specifically designed to overcome the challenges related to intra-class fruits classification. The proposed method showcases an impressive performance for intra-class classification, which is achieved using a few parameters than the existing methods. The proposed model consists of Inception block, Residual connections and various other layers in very precise order. To validate its performance, the proposed method is compared with state-of-the-art models and performs best in terms of accuracy, loss, parameters, and depth.", acknowledgement = ack-nhfb, articleno = "2140014", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", remark = "Special Issue on Innovations in Image Processing using Machine Learning", } @Article{Zhang:2022:MDM, author = "Qi Zhang", title = "Medical Data and Mathematically Modeled Implicit Surface Real-Rime Visualization in {Web} Browsers", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "04", pages = "??--??", month = jul, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467822500279", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Aug 11 08:52:44 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500279", abstract = "Raycasting can display volumetric medical data in fine details and reveal crucial inner imaging information, while implicit surface is able to effectively model complex objects with high flexibility, combining these two rendering modalities together will provide comprehensive information of the scene and has wide applications in surgical simulation, image-guided intervention, and medical training. However, medical data rendering is based on texture depth at every sampling point, while mathematically modeled implicit surfaces do not have geometric information in texture space. It is a challenging task to visualize both physical scalar data and virtual implicit surfaces simultaneously. To address this issue, in this paper, we present a new dual-casting ray-based double modality data rendering algorithm and web-based software platform to visualize volumetric medical data and implicit surface in the same browser. The algorithm runs on graphics processing unit and casts two virtual rays from camera to each pixel on the display panel, where one ray travels through the mathematically defined scene for implicit surface rendering and the other one passes the 3D texture space for volumetric data visualization. The proposed algorithm can detect voxel depth information and algebraic surface models along each casting ray and dynamically enhance the visualized dual-modality data with the improved lighting model and transparency adjustment function. Moreover, auxiliary innovative techniques are also presented to enhance the shading and rendering features of interest. Our software platform can seamlessly visualize volumetric medical data and implicit surfaces in the same web browser over Internet.", acknowledgement = ack-nhfb, articleno = "2250027", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Shah:2022:NRI, author = "Said Khalid Shah", title = "Non-Rigid Image Registration based on Parameterized Surfaces: Application to {$3$D} Cardiac Motion Image Analysis", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "04", pages = "??--??", month = jul, year = "2022", DOI = "https://doi.org/10.1142/S0219467822500280", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Aug 11 08:52:44 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500280", abstract = "This paper describes the Fast Radial Basis Function (RBF) method for cardiac motion tracking in 3D CT using non-rigid medical image registration based on parameterized (regular) surfaces. The technique is a point-based registration evaluation algorithm which does register 3D MR or CT images in real time. We first extract the surface of the whole heart 3D CT and its contrast enhanced part (left ventricle (LV) blood cavity) of each dataset with a semiautomatic contouring and a fully-automatic triangulation method followed by a global surface parameterization and optimization algorithm. In second step, a set of registration experiments are run to calculate the deformation field at various phases of cardiac motion or cycle from CT images, which results into significant deformation during each phase of a cycle. The surface points of the whole heart and LV are used to register the source systole image to various diastole target images taken at different phases during a heart beat. Our registration accuracy improves with the increase in number of salient feature points (i.e. optimized parameterized surfaces) and it has no effect on the speed of the algorithm (i.e. still less than a second). The results show that the target registration error is less than 3 mm (2.53) and the performance of the Fast RBF algorithm is less than a second using a whole heart CT dataset of a single patient taken over the course of the entire cardiac cycle. At the end, the results for recovery (or analysis) of bigger deformation in heart CT images using the Fast RBF algorithm is compared to the state-of-the-art Free Form Deformation (FFD) registration technique. It is proved that the Fast RBF method is performing better in speed and slightly less accurate than the FFD (when measured in terms of NMI) due to iterative nature of the latter.", acknowledgement = ack-nhfb, articleno = "2250028", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Zhang:2022:FBC, author = "Geng Zhang and Qi Zhu and Jing Yang and Ruting Xu and Zhiqiang Zhang and Daoqiang Zhang", title = "Functional Brain Connectivity Hyper-Network Embedded with Structural Information for Epilepsy Diagnosis", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "04", pages = "??--??", month = jul, year = "2022", DOI = "https://doi.org/10.1142/S0219467822500292", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Aug 11 08:52:44 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500292", abstract = "Automatic diagnosis of brain diseases based on brain connectivity network (BCN) classification is one of the hot research fields in medical image analysis. The functional brain network reflects the brain functional activities and structural brain network reflects the neural connections of the main brain regions. It is of great significance to explore and explain the inner mechanism of the brain and to understand and treat brain diseases. In this paper, based on the graph structure characteristics of brain network, the fusion model of functional brain network and structural brain network is designed to classify the diagnosis of brain mental diseases. Specifically, the main work of this paper is to use the Laplacian graph embed the information of diffusion tensor imaging, which contains the characteristics of structural brain networks, into the functional brain network with hyper-order functional connectivity information built based on functional magnetic resonance data using the sparse representation method, to obtain brain network with both functional and structural characteristics. Projection of the brain network and the two original modes data to the kernel space respectively and then classified by the multi-task learning method. Experiments on the epilepsy dataset show that our method has better performance than several state-of-the-art methods. In addition, brain regions and connections that are highly correlated with disease revealed by our method are discussed.", acknowledgement = ack-nhfb, articleno = "2250029", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Nnolim:2022:DSE, author = "Uche A. Nnolim", title = "Dynamic Selective Edge-Based {Integer/Fractional-Order} Partial Differential Equation for Degraded Document Image Binarization", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "04", pages = "??--??", month = jul, year = "2022", DOI = "https://doi.org/10.1142/S0219467822500309", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Aug 11 08:52:44 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500309", abstract = "Conventional thresholding algorithms have had limited success with degraded document images. Recently, partial differential equations (PDEs) have been applied with good results. However, these are usually tailored to handle relatively few specific distortions. In this study, we combine an edge detection term with a linear binarization source term in a PDE formulation. Additionally, a new proposed diffusivity function further amplifies desired edges. It also suppresses undesired edges that comprise bleed-through effects. Furthermore, we develop the fractional variant of the proposed scheme, which further improves results and provides more flexibility. Moreover, nonlinear color spaces are utilized to improve binarization results for images with color distortion. The proposed scheme removes document image degradation such as bleed-through, stains, smudges, etc., and also restores faded text in the images. Experimental subjective and objective results show consistently superior performance of the proposed approach compared to the state-of-the-art PDE-based models.", acknowledgement = ack-nhfb, articleno = "2250030", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Moradi:2022:IDM, author = "Hamid Moradi and Amir Hossein Foruzan", title = "Integration of Dynamic Multi-Atlas and Deep Learning Techniques to Improve Segmentation of the Prostate in {MR} Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "04", pages = "??--??", month = jul, year = "2022", DOI = "https://doi.org/10.1142/S0219467822500310", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Aug 11 08:52:44 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500310", abstract = "Accurate delineation of the prostate in MR images is an essential step for treatment planning and volume estimation of the organ. Prostate segmentation is a challenging task due to its variable size and shape. Moreover, neighboring tissues have a low-contrast with the prostate. We propose a robust and precise automatic algorithm to define the prostate's boundaries in MR images in this paper. First, we find the prostate's ROI by a deep neural network and decrease the input image's size. Next, a dynamic multi-atlas-based approach obtains the initial segmentation of the prostate. A watershed algorithm improves the initial segmentation at the next stage. Finally, an SSM algorithm keeps the result in the domain of allowable prostate shapes. The quantitative evaluation of 74 prostate volumes demonstrated that the proposed method yields a mean Dice coefficient of 0.83{\textpm}0.05. In comparison with recent researches, our algorithm is robust against shape and size variations.", acknowledgement = ack-nhfb, articleno = "2250031", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Rajalakshmi:2022:EVP, author = "M. Rajalakshmi and K. Annapurani", title = "Enhancement of Vascular Patterns in Palm Images Using Various Image Enhancement Techniques for Person Identification", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "04", pages = "??--??", month = jul, year = "2022", DOI = "https://doi.org/10.1142/S0219467822500322", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Aug 11 08:52:44 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500322", abstract = "Image classification is a complicated process of classifying an image based on its visual representation. This paper portrays the need for adapting and applying a suitable image enhancement and denoising technique in order to arrive at a successful classification of data captured remotely. Biometric properties that are widely explored today are very important for authentication purposes. Noise may be the result of incorrect vein detection in the accepted image, thus explaining the need for a better development technique. This work provides subjective and objective analysis of the performance of various image enhancement filters in the spatial domain. After performing these pre-processing steps, the vein map and the corresponding vein graph can be easily obtained with minimal extraction steps, in which the appropriate Graph Matching method can be used to evaluate hand vein graphs thus performing the person authentication. The analysis result shows that the image enhancement filter performs better as an image enhancement filter compared to all other filters. Image quality measures (IQMs) are also tabulated for the evaluation of image quality.", acknowledgement = ack-nhfb, articleno = "2250032", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Abedini:2022:IDU, author = "Maryam Abedini and Horriyeh Haddad and Marzieh Faridi Masouleh and Asadollah Shahbahrami", title = "Image Denoising Using Sparse Representation and Principal Component Analysis", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "04", pages = "??--??", month = jul, year = "2022", DOI = "https://doi.org/10.1142/S0219467822500334", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Aug 11 08:52:44 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500334", abstract = "This study proposes an image denoising algorithm based on sparse representation and Principal Component Analysis (PCA). The proposed algorithm includes the following steps. First, the noisy image is divided into overlapped 8{\texttimes}8 blocks. Second, the discrete cosine transform is applied as a dictionary for the sparse representation of the vectors created by the overlapped blocks. To calculate the sparse vector, the orthogonal matching pursuit algorithm is used. Then, the dictionary is updated by means of the PCA algorithm to achieve the sparsest representation of vectors. Since the signal energy, unlike the noise energy, is concentrated on a small dataset by transforming into the PCA domain, the signal and noise can be well distinguished. The proposed algorithm was implemented in a MATLAB environment and its performance was evaluated on some standard grayscale images under different levels of standard deviations of white Gaussian noise by means of peak signal-to-noise ratio, structural similarity indexes, and visual effects. The experimental results demonstrate that the proposed denoising algorithm achieves significant improvement compared to dual-tree complex discrete wavelet transform and K-singular value decomposition image denoising methods. It also obtains competitive results with the block-matching and 3D filtering method, which is the current state-of-the-art for image denoising.", acknowledgement = ack-nhfb, articleno = "2250033", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Wagdy:2022:DCM, author = "Marian Wagdy and Khaild Amin and Mina Ibrahim", title = "Detection and Correction of Multi-Warping Document Image", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "04", pages = "??--??", month = jul, year = "2022", DOI = "https://doi.org/10.1142/S0219467822500346", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Aug 11 08:52:44 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500346", abstract = "In this work, we aim to solve the multi-warping document image problems. We can overcome the limitations of the previous dewarping algorithms to recover the shape of the document. The proposed method is based on a well-defined pattern to simulate the distorted and undistorted connected component of document images. Some pairs of control points are selected for each connected component and its ground truth pattern to define the mapping function between them. The dewarping process transforms the warping connected component according to the geometric transformation defined by the calculated mapping function. Results on document dewarping dataset CBDAR demonstrate the effectiveness of our method. OCR error metrics are also used to evaluate the performance of the proposed approach.", acknowledgement = ack-nhfb, articleno = "2250034", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Ghilas:2022:SDI, author = "Hamza Ghilas and Meriem Gagaoua and Abdelkamel Tari and Mohamed Cheriet", title = "{Spatial Distribution of Ink at Keypoints (SDIK)}: a Novel Feature for Word Spotting in {Arabic} Documents", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "04", pages = "??--??", month = jul, year = "2022", DOI = "https://doi.org/10.1142/S0219467822500358", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Aug 11 08:52:44 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500358", abstract = "This paper addresses the challenging task of word spotting in Arabic handwritten documents. We proposed a novel feature that we called Spatial Distribution of Ink at Keypoints (SDIK). The proposed feature captures the characteristics of Arabic handwriting concentrated at endpoints and branch points. SDIK feature quantizes the spatial repartition of ink pixels in the neighborhoods of keypoints. The resulting SDIK features are very fast to match, we take this advantage to match a query word with lines images rather than words images. By this matching mechanism, we overcome the hard task of segmenting an Arabic document into words. The method proposed in this study is tested on historical Arabic document with IBN SINA dataset and on modern handwriting with IFN/ENIT database. The obtained results are great of interest for retrieving query words in an Arabic document.", acknowledgement = ack-nhfb, articleno = "2250035", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Salehi:2022:ISB, author = "Hadi Salehi", title = "Image De-Speckling Based on the Coefficient of Variation, Improved Guided Filter, and Fast Bilateral Filter", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "04", pages = "??--??", month = jul, year = "2022", DOI = "https://doi.org/10.1142/S021946782250036X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Aug 11 08:52:44 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S021946782250036X", abstract = "Images are widely used in engineering. Unfortunately, medical ultrasound images and synthetic aperture radar (SAR) images are mainly degraded by an intrinsic noise called speckle. Therefore, de-speckling is a main pre-processing stage for degraded images. In this paper, first, an optimized adaptive Wiener filter (OAWF) is proposed. OAWF can be applied to the input image without the need for logarithmic transform. In addition its performance is improved. Next, the coefficient of variation (CV) is computed from the input image. With the help of CV, the guided filter converts to an improved guided filter (IGF). Next, the improved guided filter is applied on the image. Subsequently, the fast bilateral filter is applied on the image. The proposed filter has a better image detail preservation compared to some other standard methods. The experimental outcomes show that the proposed denoising algorithm is able to preserve image details and edges compared with other de-speckling methods.", acknowledgement = ack-nhfb, articleno = "2250036", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Hassan:2022:NSI, author = "Gaber Hassan and Khalid M. Hosny and R. M. Farouk and Ahmed M. Alzohairy", title = "New Set of Invariant Quaternion {Krawtchouk} Moments for Color Image Representation and Recognition", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "04", pages = "??--??", month = jul, year = "2022", DOI = "https://doi.org/10.1142/S0219467822500371", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Aug 11 08:52:44 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500371", abstract = "One of the most often used techniques to represent color images is quaternion algebra. This study introduces the quaternion Krawtchouk moments, QKrMs, as a new set of moments to represent color images. Krawtchouk moments (KrMs) represent one type of discrete moments. QKrMs use traditional Krawtchouk moments of each color channel to describe color images. This new set of moments is defined by using orthogonal polynomials called the Krawtchouk polynomials. The stability against the translation, rotation, and scaling transformations for QKrMs is discussed. The performance of the proposed QKrMs is evaluated against other discrete quaternion moments for image reconstruction capability, toughness against various types of noise, invariance to similarity transformations, color face image recognition, and CPU elapsed times.", acknowledgement = ack-nhfb, articleno = "2250037", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Islam:2022:CSP, author = "Rafiqul Islam and Md Shafiqul Islam and Muhammad Shahin Uddin", title = "Compressed Sensing in Parallel {MRI}: a Review", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "04", pages = "??--??", month = jul, year = "2022", DOI = "https://doi.org/10.1142/S0219467822500383", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Aug 11 08:52:44 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500383", abstract = "Magnetic resonance imaging (MRI) is a dynamic and safe imaging technique in medical imaging. Recently, parallel MRI (pMRI) is widely used for accelerating conventional MRI. Both frequency and image domain-based reconstructions are the most attractive methods for generating the image from multi-channel k-space data. Compressed sensing (CS) is a recently used procedure to reduce the acquisition time of conventional MRI. This reduction is achieved by taking fewer measurements from the fully sampled k-space data. Therefore, applying the CS technique in pMRI is the most emerging way for further improving the acquisition time that is a tremendous research interest. However, as the phase encoding plane may be perpendicular or parallel to the coil elements plane, finding the exact domain for CS in pMRI reconstruction is a major challenging issue. In this work, the application of the CS technique in pMRI in both domains is investigated. Later some widely used methodologies are presented as the nonlinear reconstruction algorithm of CS in pMRI. Finally, a discussion is performed based on CS in pMRI to perceive the reality of different reconstruction algorithms at a glance for finding preferred methodologies.", acknowledgement = ack-nhfb, articleno = "2250038", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Arora:2022:CBP, author = "Tanvi Arora", title = "{CNN}-based Prediction of {COVID-19} using Chest {CT} Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "04", pages = "??--??", month = jul, year = "2022", DOI = "https://doi.org/10.1142/S0219467822500395", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Aug 11 08:52:44 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500395", abstract = "The coronavirus disease (COVID-19) pandemic that is caused by the SARS-CoV2 has spread all over the world. It is an infectious disease that can spread from person to person. The severity of the disease can be categorized into five categories namely asymptomatic, mild, moderate, severe, and critical. From the reported cases thus, it has been seen that 80\% of the cases that test positive with COVID-19 infection have less than moderate complications, whereas 20\% of the positive cases develop severe and critical complications. The virus infects the lungs of an individual, therefore, it has been observed that the X-ray and computed tomography (CT) scan images of the infected people can be used by the machine learning-based application programs to predict the presence of the infection. Therefore, in the proposed work, a Convolutional Neural Network model based upon the DenseNet architecture is being used to predict the presence of COVID-19 infection using the CT scan images of the chest. The proposed work has been carried out using the dataset of the CT images from the COVID CT Dataset. It has 349 images marked as COVID-19 positive and 397 images have been marked as COVID-19 negative. The proposed system can categorize the test set images with an accuracy of 91.4\%. The proposed method is capable of detecting the presence of COVID-19 infection with good accuracy using the chest CT scan images of the humans.", acknowledgement = ack-nhfb, articleno = "2250039", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Varghese:2022:DLB, author = "Prathibha Varghese and G. Arockia Selva Saroja", title = "Deep Learning-Based Hexrep Neural Network for Convergence Free with Operator's Efficacy in Hexagonal Image Processing", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "04", pages = "??--??", month = jul, year = "2022", DOI = "https://doi.org/10.1142/S0219467823500328", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Aug 11 08:52:44 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500328", abstract = "The field of hexagonal image processing is concerned with the creation of image processing systems that combine the advantages of biological model-based evolutionary motivated frameworks. The structure and functionality of artificial neural networks were inspired by biological processes. The fundamental framework of recording and output devices limits their present state of the art. Prior neural networks have used square or hexagonal style input to completely connected layers, which resulted in a high coherence problem between two adjacent hexagonal kernel layers due to pooling. Previous research does not design the self-data structure to support convolution to increase computational efficiency, so it violates the convolution and pooling operator, which greatly degrades the image process performance. This paper introduces a novel paradigm Proficient Deep Learning-based Hexrep Neural Network that overcomes major significant problems in image operations structure constraint, coherence problem, and violation of convolution and pooling operator and achieves hexagonal image processing.", acknowledgement = ack-nhfb, articleno = "2350032", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kaur:2022:RND, author = "Swapandeep Kaur and Sheifali Gupta and Swati Singh and Tanvi Arora", title = "A Review on Natural Disaster Detection in Social Media and Satellite Imagery Using Machine Learning and Deep Learning", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "05", pages = "??--??", month = oct, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467822500401", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Nov 8 11:46:54 MST 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500401", abstract = "A disaster is a devastating incident that causes a serious disruption of the functions of a community. It leads to loss of human life and environmental and financial losses. Natural disasters cause damage and privation that could last for months and even years. Immediate steps need to be taken and social media platforms like Twitter help to provide relief to the affected public. However, it is difficult to analyze high-volume data obtained from social media posts. Therefore, the efficiency and accuracy of useful data extracted from the enormous posts related to disaster are low. Satellite imagery is gaining popularity because of its ability to cover large temporal and spatial areas. But, both the social media and satellite imagery require the use of automated methods to avoid the errors caused by humans. Deep learning and machine learning have become extremely popular for text and image classification tasks. In this paper, a review has been done on natural disaster detection through information obtained from social media and satellite images using deep learning and machine learning.", acknowledgement = ack-nhfb, articleno = "2250040", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kumar:2022:DLB, author = "V. Akash Kumar and Vijaya Mishra and Monika Arora", title = "Deep Learning-Based Classification of Malignant and Benign Cells in Dermatoscopic Images via Transfer Learning Approach", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "05", pages = "??--??", month = oct, year = "2022", DOI = "https://doi.org/10.1142/S0219467822500413", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Nov 8 11:46:54 MST 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500413", abstract = "The inhibition of healthy cells creating improper controlling process of the human body system indicates the occurrence of growth of cancerous cells. The cluster of such cells leads to the development of tumor. The observation of this type of abnormal skin pigmentation is done using an effective tool called Dermoscopy. However, these dermatoscopic images possess a great challenge for diagnosis. Considering the characteristics of dermatoscopic images, transfer learning is an appropriate approach of automatically classifying the images based on the respective categories. An automatic identification of skin cancer not only saves human life but also helps in detecting its growth at an earlier stage which saves medical practitioner's effort and time. A newly predicted model has been proposed for classifying the skin cancer as benign or malignant by DCNN with transfer learning and its pre-trained models such as VGG 16, VGG 19, ResNet 50, ResNet 101, and Inception V3. The proposed methodology aims at examining the efficiency of pre-trained models and transfer learning approach for the classification tasks and opens new dimensions of research in the field of medicines using imaging technique which can be implementable in real-time applications.", acknowledgement = ack-nhfb, articleno = "2250041", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Sasikumar:2022:CAD, author = "K. Sasikumar and B. Vijayakumar", title = "Comparative Analysis of Different Data Replication Strategies in Cloud Environment", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "05", pages = "??--??", month = oct, year = "2022", DOI = "https://doi.org/10.1142/S0219467822500425", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Nov 8 11:46:54 MST 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib; https://www.math.utah.edu/pub/tex/bib/java2020.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500425", abstract = "In this paper, we performed a comparative study of the different data replication strategies such as Adaptive Data Replication Strategy (ADRS), Dynamic Cost Aware Re-Replication and Rebalancing Strategy (DCR2S) and Efficient Placement Algorithm (EPA) in the cloud environment. The implementation of these three techniques is done in JAVA and the performance analysis is conducted to study the performance of those replication techniques by various parameters. The parameters used for the performance analysis of these three techniques are Load Variance, Response Time, Probability of File Availability, System Byte Effective Rate (SBER), Latency, and Fault Ratio. From the analysis, it is evaluated that by varying the number of file replicas, it shows deviations in the outcomes of these parameters. The comparative results were also analyzed.", acknowledgement = ack-nhfb, articleno = "2250042", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Padalkar:2022:FBS, author = "Ganesh R. Padalkar and Madhuri B. Khambete", title = "Fusion-Based Semantic Segmentation Using Deep Learning Architecture in Case of Very Small Training Dataset", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "05", pages = "??--??", month = oct, year = "2022", DOI = "https://doi.org/10.1142/S0219467822500437", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Nov 8 11:46:54 MST 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500437", abstract = "Semantic segmentation is a pre-processing step in computer vision-based applications. It is the task of assigning a predefined class label to every pixel of an image. Several supervised and unsupervised algorithms are available to classify pixels of an image into predefined object classes. The algorithms, such as random forest and SVM are used to obtain the semantic segmentation. Recently, convolutional neural network (CNN)-based architectures have become popular for the tasks of object detection, object recognition, and segmentation. These deep architectures perform semantic segmentation with far better accuracy than the algorithms that were used earlier. CNN-based deep learning architectures require a large dataset for training. In real life, some of the applications may not have sufficient good quality samples for training of deep learning architectures e.g. medical applications. Such a requirement initiated a need to have a technique of effective training of deep learning architecture in case of a very small dataset. Class imbalance is another challenge in the process of training deep learning architecture. Due to class imbalance, the classifier overclassifies classes with large samples. In this paper, the challenge of training a deep learning architecture with a small dataset and class imbalance is addressed by novel fusion-based semantic segmentation technique which improves segmentation of minor and major classes.", acknowledgement = ack-nhfb, articleno = "2250043", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Elyounsi:2022:FAO, author = "Asma Elyounsi and Hatem Tlijani and Mohamed Salim Bouhlel", title = "Firefly Algorithm Optimized Functional Link Artificial Neural Network for {ISA}-Radar Image Recognition", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "05", pages = "??--??", month = oct, year = "2022", DOI = "https://doi.org/10.1142/S0219467822500449", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Nov 8 11:46:54 MST 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500449", abstract = "Traditional neural networks are very diverse and have been used during the last decades in the fields of data classification. These networks like MLP, back propagation neural networks (BPNN) and feed forward network have shown inability to scale with problem size and with the slow convergence rate. So in order to overcome these numbers of drawbacks, the use of higher order neural networks (HONNs) becomes the solution by adding input units along with a stronger functioning of other neural units in the network and transforms easily these input units to hidden layers. In this paper, a new metaheuristic method, Firefly (FFA), is applied to calculate the optimal weights of the Functional Link Artificial Neural Network (FLANN) by using the flashing behavior of fireflies in order to classify ISA-Radar target. The average classification result of FLANN-FFA which reached 96\% shows the efficiency of the process compared to other tested methods.", acknowledgement = ack-nhfb, articleno = "2250044", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Ravikumar:2022:MLT, author = "S. Ravikumar and E. Kannan", title = "Machine Learning Techniques for Identifying Fetal Risk During Pregnancy", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "05", pages = "??--??", month = oct, year = "2022", DOI = "https://doi.org/10.1142/S0219467822500450", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Nov 8 11:46:54 MST 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500450", abstract = "Cardiotocography (CTG) is a biophysical method for assessing fetal condition that primarily relies on the recording and automated analysis of fetal heart activity. The quantitative description of the CTG signals is provided by computerized fetal monitoring systems. Even though effective conclusion generation methods for decision process support are still required to find out the fetal risk such as premature embryo, this proposed method and outcome data can confirm the assessment of the fetal state after birth. Low birth weight is quite possibly the main attribute that significantly depicts an unusual fetal result. These expectations are assessed in a constant experimental decision support system, providing valuable information that can be used to obtain additional information about the fetal state using machine learning techniques. The advancements in modern obstetric practice enabled the use of numerous reliable and robust machine learning approaches in classifying fetal heart rate signals. The Na{\"\i}ve Bayes (NB) classifier, support vector machine (SVM), decision trees (DT), and random forest (RF) are used in the proposed method. To assess these outcomes in the proposed method, some of the metrics such as precision, accuracy, F1 score, recall, sensitivity, logarithmic loss and mean absolute error have been taken. The above mentioned metrics will be helpful to predict the fetal risk.", acknowledgement = ack-nhfb, articleno = "2250045", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Bhasha:2022:AIS, author = "A. Valli Bhasha and B. D. Venkatramana Reddy", title = "Automated Image Super Resolution with the Aid of Activation Function Optimized Deep {CNN} and Adaptive Wavelet Lifting Approach", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "05", pages = "??--??", month = oct, year = "2022", DOI = "https://doi.org/10.1142/S0219467822500462", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Nov 8 11:46:54 MST 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500462", abstract = "Diverse image super-resolution (SR) techniques have been implemented to reconstruct the high-resolution (HR) images from input images through lower spatial resolutions. However, the evaluation of the perceptual quality of SR images remains an important and complex research problem. This paper proposes a new image SR model with the intention of attaining maximum Peak Signal-to-Noise Ratio (PSNR). The conversion of low-resolution (LR) images from the HR images is performed by bicubic interpolation-based downsampling and upsampling. Then, the four sub-bands of LR and HR images are generated by the novel Adaptive Wavelet Lifting approach, in which the filter modes are optimized using the proposed SA-CBO. From this technique, LR wavelet sub-bands (LRSB) for LR images and HR wavelet sub-bands (HRSB) for HR images are formed. With the help of the LRSB and HRSB images, the residual images are formed by the adoption of the optimized Activation function and optimized hidden neurons in a deep convolutional neural network (CNN). The improvement in both the adaptive wavelet lifting approach and deep CNN is made by the self-adaptive-colliding bodies optimization (SA-CBO). Finally, the inverse adaptive wavelet lifting approach is used to produce the final SR image. Experimental results on publicly available SR image quality databases confirm the effectiveness and generalization ability of the proposed method compared with the traditional image quality assessment algorithms.", acknowledgement = ack-nhfb, articleno = "2250046", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kumar:2022:ICB, author = "Gangavarapu Venkata Satya Kumar and P. G. Krishna Mohan", title = "Improved Content Based Image Retrieval Process Based on Deep Convolutional Neural Network and Salp Swarm Algorithm", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "05", pages = "??--??", month = oct, year = "2022", DOI = "https://doi.org/10.1142/S0219467822500474", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Nov 8 11:46:54 MST 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500474", abstract = "Digital image and medical image retrieval from several repositories are improving gradually, so the capacity of repositories increases rapidly. The semantic space is the main issue on content-based image retrieval (CBIR), which exists among the semantic level as well as increases the data recognized through human and low level visible data obtained through the image. The CBIR system utilizes the deep convolutional neural network (DCNN), which is trained to medical image characterization and the digital image by salp swarm optimization algorithm (SSA). The average classification accuracy for medical image is 86.805\%, a mean average precision is 79\%, Average Recall Rate (ARR) is 91.7\% and F -measure is 84.9\%, are achieved during retrieval task. For image retrieval, the Average Precision Rate (APR) improved from 39\%, 40\%, 36\% and 42.5\% to 86.8\% and the ARR enhanced from 39.5\%, 40.5\%, 35.5\% and 42.5\% to 86.8\%. The F -measure is improved from 39.5\%, 40.5\%, 35.5\% and 42.5\% to 86.8\% as different with Local tetra patterns (LTrP), LOOP, local derivative pattern (LDP) and local mean differential excitation pattern (LMDeP) separately on Corel-1K dataset. The presented method is most suitable for multimodal digital images and medical image retrieval for various parts of the body.", acknowledgement = ack-nhfb, articleno = "2250047", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Shahrokhi:2022:ICM, author = "Marziye Shahrokhi and Alireza Akoushideh and Asadollah Shahbahrami", title = "Image Copy--Move Forgery Detection Using Combination of Scale-Invariant Feature Transform and Local Binary Pattern Features", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "05", pages = "??--??", month = oct, year = "2022", DOI = "https://doi.org/10.1142/S0219467822500486", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Nov 8 11:46:54 MST 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500486", abstract = "Today, manipulating, storing, and sending digital images are simple and easy because of the development of digital imaging devices from hardware and software points of view. Digital images are used in different contexts of people's lives such as news, forensics, and so on. Therefore, the reliability of received images is a question that often occupies the viewer's mind and the authenticity of digital images is increasingly important. Detecting a forged image as a genuine one as well as detecting a genuine image as a forged one can sometimes have irreparable consequences. For example, an image that is available from the scene of a crime can lead to a wrong decision if it is detected incorrectly. In this paper, we propose a combination method to improve the accuracy of copy--move forgery detection (CMFD) reducing the false positive rate (FPR) based on texture attributes. The proposed method uses a combination of the scale-invariant feature transform (SIFT) and local binary pattern (LBP). Consideration of texture features around the keypoints detected by the SIFT algorithm can be effective to reduce the incorrect matches and improve the accuracy of CMFD. In addition, to find more and better keypoints some pre-processing methods have been proposed. This study was evaluated on the COVERAGE, GRIP, and MICC-F220 databases. Experimental results show that the proposed method without clustering or segmentation and only with simple matching operations, has been able to earn the true positive rates of 98.75\%, 95.45\%, and 87\% on the GRIP, MICC-F220, and COVERAGE datasets, respectively. Also, the proposed method, with FPRs from 17.75\% to 3.75\% on the GRIP dataset, has been able to achieve the best results.", acknowledgement = ack-nhfb, articleno = "2250048", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Saha:2022:TBF, author = "Priya Saha and Debotosh Bhattacharjee and Barin Kumar De and Mita Nasipuri", title = "A Thermal Blended Facial Expression Analysis and Recognition System Using Deformed Thermal Facial Areas", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "05", pages = "??--??", month = oct, year = "2022", DOI = "https://doi.org/10.1142/S0219467822500498", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Nov 8 11:46:54 MST 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500498", abstract = "There are many research works in visible as well as thermal facial expression analysis and recognition. Several facial expression databases have been designed in both modalities. However, little attention has been given for analyzing blended facial expressions in the thermal infrared spectrum. In this paper, we have introduced a Visual-Thermal Blended Facial Expression Database (VTBE) that contains visual and thermal face images with both basic and blended facial expressions. The database contains 12 posed blended facial expressions and spontaneous six basic facial expressions in both modalities. In this paper, we have proposed Deformed Thermal Facial Area (DTFA) in thermal expressive face image and make an analysis to differentiate between basic and blended expressions using DTFA. Here, the fusion of DTFA and Deformed Visual Facial Area (DVFA) has been proposed combining the features of both modalities and experiments and has been conducted on this new database. However, to show the effectiveness of our proposed approach, we have compared our method with state-of-the-art methods using USTC-NVIE database. Experiment results reveal that our approach is superior to state-of-the-art methods.", acknowledgement = ack-nhfb, articleno = "2250049", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Jadav:2022:DSD, author = "Kalpesh R. Jadav and Arvind R. Yadav", title = "Dynamic Shadow Detection and Removal for Vehicle Tracking System", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "05", pages = "??--??", month = oct, year = "2022", DOI = "https://doi.org/10.1142/S0219467822500504", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Nov 8 11:46:54 MST 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500504", abstract = "Shadow leads to failure of moving target positioning, segmentation, tracking, and classification in the video surveillance system thus shadow detection and removal is essential for further computer vision process. The existing state-of-the-art methods for dynamic shadow detection have produced a high discrimination rate but a poor detection rate (foreground pixels are classified as shadow pixels). This paper proposes an effective method for dynamic shadow detection and removal based on intensity ratio along with frame difference, gamma correction, and morphology operations. The performance of the proposed method has been tested on two outdoor ATON datasets, namely, highway-I and highway-III for vehicle tracking systems. The proposed method has produced a discrimination rate of 89.07\% and a detection rate of 80.79\% for highway-I video sequences. Similarly, for a highway-III video sequence, the discrimination rate of 85.60\% and detection rate of 84.05\% have been obtained. Investigational outcomes show that the proposed method is the simple, steadiest, and robust for dynamic shadow detection on the dataset used in this work.", acknowledgement = ack-nhfb, articleno = "2250050", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Indhumathi:2022:HAR, author = "C. Indhumathi and V. Murugan and G. Muthulakshmii", title = "Human Action Recognition Using Spatio-Temporal Multiplier Network and Attentive Correlated Temporal Feature", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "05", pages = "??--??", month = oct, year = "2022", DOI = "https://doi.org/10.1142/S0219467822500516", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Nov 8 11:46:54 MST 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500516", abstract = "Nowadays, action recognition has gained more attention from the computer vision community. Normally for recognizing human actions, spatial and temporal features are extracted. Two-stream convolutional neural network is used commonly for human action recognition in videos. In this paper, Adaptive motion Attentive Correlated Temporal Feature (ACTF) is used for temporal feature extractor. The temporal average pooling in inter-frame is used for extracting the inter-frame regional correlation feature and mean feature. This proposed method has better accuracy of 96.9\% for UCF101 and 74.6\% for HMDB51 datasets, respectively, which are higher than the other state-of-the-art methods.", acknowledgement = ack-nhfb, articleno = "2250051", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Jatain:2022:EFR, author = "Rashmi Jatain and Manisha Jailia", title = "Enhanced Face Recognition Using Adaptive Local Tri {Weber} Pattern with Improved Deep Learning Architecture", journal = j-INT-J-IMAGE-GRAPHICS, volume = "22", number = "05", pages = "??--??", month = oct, year = "2022", DOI = "https://doi.org/10.1142/S0219467822500528", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Nov 8 11:46:54 MST 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500528", abstract = "Effective face recognition is accomplished using the extraction of features and classification. Though there are multiple techniques for face image recognition, full face recognition in real-time is quite difficult. One of the emerging and promising methods to address this challenge in face recognition is deep learning networks. The inevitable network tool associated with the face recognition method with deep learning systems is convolutional neural networks (CNNs). This research intends to develop a new method for face recognition using adaptive intelligent methods. The main phases of the proposed method are (a) data collection, (b) image pre-processing, (c) normalization, (d) pattern extraction, and (e) recognition. Initially, the images for face recognition are gathered from CPFW, Yale datasets, and the MIT-CBCL dataset. The image pre-processing is performed by the Gaussian filtering method. Further, the normalization of the image will be done, which is a process that alters the range of pixel intensities and can handle the poor contrast due to glare. Then a new descriptor called adaptive local tri Weber pattern (ALTrWP) acts as a pattern extractor. In the recognition phase, the VGG16 architecture with new chick updated-chicken swarm optimization (NSU-CSO) is used. As the modification, VGG16 architecture will be enhanced by this optimization technique. The performance of the developed method is analyzed on two standards face database. Experimental results are compared with different machine learning approaches concerned with noteworthy measures, which demonstrate the efficiency of the considered classifier.", acknowledgement = ack-nhfb, articleno = "2250052", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Raj:2023:DRV, author = "R. Jisha Raj and Smitha Dharan and T. T. Sunil", title = "Dimensionality Reduction and Visualization of {{\em Bharatanatyam Mudras}}", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "01", pages = "??--??", month = jan, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467823500018", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Mar 25 07:40:33 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500018", abstract = "Cultural dances are practiced all over the world. The study of various gestures of the performer using computer vision techniques can help in better understanding of these dance forms and for annotation purposes. {\em Bharatanatyam\/} is a classical dance that originated in South India. {\em Bharatanatyam\/} performer uses hand gestures ( {\em mudras\/} ), facial expressions and body movements to communicate to the audience the intended meaning. According to {\em Natyashastra}, a classical text on Indian dance, there are 28 {\em Asamyukta Hastas\/} (single-hand gestures) and 23 {\em Samyukta Hastas\/} (Double-hand gestures) in {\em Bharatanatyam}. Open datasets on {\em Bharatanatyam\/} dance gestures are not presently available. An exhaustive open dataset comprising of various {\em mudras\/} in {\em Bharatanatyam\/} was created. The dataset consists of 15\,396 distinct single-hand {\em mudra\/} images and 13\,035 distinct double-hand {\em mudra\/} images. In this paper, we explore the dataset using various multidimensional visualization techniques. PCA, Kernel PCA, Local Linear Embedding, Multidimensional Scaling, Isomap, t-SNE and PCA--t-SNE combination are being investigated. The best visualization for exploration of the dataset is obtained using PCA--t-SNE combination.", acknowledgement = ack-nhfb, articleno = "2350001", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Adhikari:2023:PEC, author = "Ramesh Adhikari and Suresh Pokharel", title = "Performance Evaluation of Convolutional Neural Network Using Synthetic Medical Data Augmentation Generated by {GAN}", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "01", pages = "??--??", month = jan, year = "2023", DOI = "https://doi.org/10.1142/S021946782350002X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Mar 25 07:40:33 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S021946782350002X", abstract = "Data augmentation is widely used in image processing and pattern recognition problems in order to increase the richness in diversity of available data. It is commonly used to improve the classification accuracy of images when the available datasets are limited. Deep learning approaches have demonstrated an immense breakthrough in medical diagnostics over the last decade. A significant amount of datasets are needed for the effective training of deep neural networks. The appropriate use of data augmentation techniques prevents the model from over-fitting and thus increases the generalization capability of the network while testing afterward on unseen data. However, it remains a huge challenge to obtain such a large dataset from rare diseases in the medical field. This study presents the synthetic data augmentation technique using Generative Adversarial Networks to evaluate the generalization capability of neural networks using existing data more effectively. In this research, the convolutional neural network (CNN) model is used to classify the X-ray images of the human chest in both normal and pneumonia conditions; then, the synthetic images of the X-ray from the available dataset are generated by using the deep convolutional generative adversarial network (DCGAN) model. Finally, the CNN model is trained again with the original dataset and augmented data generated using the DCGAN model. The classification performance of the CNN model is improved by 3.2\% when the augmented data were used along with the originally available dataset.", acknowledgement = ack-nhfb, articleno = "2350002", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kumawat:2023:IDH, author = "Anchal Kumawat and Sucheta Panda", title = "An Integrated Double Hybrid Fusion Approach for Image Smoothing", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "01", pages = "??--??", month = jan, year = "2023", DOI = "https://doi.org/10.1142/S0219467823500031", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Mar 25 07:40:33 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500031", abstract = "Often in practice, during the process of image acquisition, the acquired image gets degraded due to various factors like noise, motion blur, mis-focus of a camera, atmospheric turbulence, etc. resulting in the image unsuitable for further analysis or processing. To improve the quality of these degraded images, a double hybrid restoration filter is proposed on the two same sets of input images and the output images are fused to get a unified filter in combination with the concept of image fusion. First image set is processed by applying deconvolution using Wiener Filter (DWF) twice and decomposing the output image using Discrete Wavelet Transform (DWT). Similarly, second image set is also processed simultaneously by applying Deconvolution using Lucy--Richardson Filter (DLR) twice followed by the above procedure. The proposed filter gives a better performance as compared to DWF and DLR filters in case of both blurry as well as noisy images. The proposed filter is compared with some standard deconvolution algorithms and also some state-of-the-art restoration filters with the help of seven image quality assessment parameters. Simulation results prove the success of the proposed algorithm and at the same time, visual and quantitative results are very impressive.", acknowledgement = ack-nhfb, articleno = "2350003", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Antony:2023:TFE, author = "Joycy K. Antony and K. Kanagalakshmi", title = "{T2FRF} Filter: an Effective Algorithm for the Restoration of Fingerprint Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "01", pages = "??--??", month = jan, year = "2023", DOI = "https://doi.org/10.1142/S0219467823500043", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Mar 25 07:40:33 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500043", abstract = "Images captured in dim light are hardly satisfactory and increasing the International Organization for Standardization (ISO) for a short duration of exposure makes them noisy. The image restoration methods have a wide range of applications in the field of medical imaging, computer vision, remote sensing, and graphic design. Although the use of flash improves the lighting, it changed the image tone besides developing unnecessary highlight and shadow. Thus, these drawbacks are overcome using the image restoration methods that recovered the image with high quality from the degraded observation. The main challenge in the image restoration approach is recovering the degraded image contaminated with the noise. In this research, an effective algorithm, named T2FRF filter, is developed for the restoration of the image. The noisy pixel is identified from the input fingerprint image using Deep Convolutional Neural Network (Deep CNN), which is trained using the neighboring pixels. The Rider Optimization Algorithm (ROA) is used for the removal of the noisy pixel in the image. The enhancement of the pixel is performed using the type II fuzzy system. The developed T2FRF filter is measured using the metrics, such as correlation coefficient and Peak Signal to Noise Ratio (PSNR) for evaluating the performance. When compared with the existing image restoration method, the developed method obtained a maximum correlation coefficient of 0.7504 and a maximum PSNR of 28.2467dB, respectively.", acknowledgement = ack-nhfb, articleno = "2350004", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Sharma:2023:PAS, author = "Sandhya Sharma and Sheifali Gupta and Neeraj Kumar and Tanvi Arora", title = "Postal Automation System in {Gurmukhi} Script using Deep Learning", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "01", pages = "??--??", month = jan, year = "2023", DOI = "https://doi.org/10.1142/S0219467823500055", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Mar 25 07:40:33 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500055", abstract = "Nowadays in the era of automation, the postal automation system is one of the major research areas. Developing a postal automation system for a nation like India is much troublesome than other nations because of India's multi-script and multi-lingual behavior. This proposed work will be helpful in the postal automation of district names of Punjab (state) written in Gurmukhi script, which is the official language of the state in North India. For this, a holistic approach i.e. a segmentation-free technique has been used with the help of Convolutional Neural Network (CNN) and Deep learning (DL). For the purpose of recognition, a database of 22 000 images (samples) which are handwritten in Gurmukhi script for all the 22 districts of Punjab is prepared. Each sample is written two times by 500 different writers generating 1000 samples for each district name. Two CNN models are proposed which are named as ConvNetGuru and ConvNetGuruMod for the purpose of recognition. Maximum validation accuracy achieved by ConvNetGuru is 90\% and ConvNetGuruMod is 98\%.", acknowledgement = ack-nhfb, articleno = "2350005", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Erwin:2023:RBV, author = "Erwin and Hadrians Kesuma Putra and Bambang Suprihatin and Fathoni", title = "Retinal Blood Vessel Extraction Using a New Enhancement Technique of Modified Convolution Filters and {Sauvola} Thresholding", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "01", pages = "??--??", month = jan, year = "2023", DOI = "https://doi.org/10.1142/S0219467823500067", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Mar 25 07:40:33 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500067", abstract = "The retinal blood vessels in humans are major components with different shapes and sizes. The extraction of the blood vessels from the retina is an important step to identify the type or nature of the pattern of the diseases in the retina. Furthermore, the retinal blood vessel was also used for diagnosis, detection, and classification. The most recent solution in this topic is to enable retinal image improvement or enhancement by a convolution filter and Sauvola threshold. In image enhancement, gamma correction is applied before filtering the retinal fundus. After that, the image should be transformed to a gray channel to enhance pictorial clarity using contrast-limited histogram equalization. For filter, this paper combines two convolution filters, namely sharpen and smooth filters. The Sauvola threshold, the morphology, and the medium filter are applied to extract blood vessels from the retinal image. This paper uses DRIVE and STARE datasets. The accuracies of the proposed method are 95.37\% for DRIVE with a runtime of 1.77s and 95.17\% for STARE with 2.05s runtime. Based on the result, it concludes that the proposed method is good enough to achieve average calculation parameters of a low time quality, quick, and significant.", acknowledgement = ack-nhfb, articleno = "2350006", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Tudavekar:2023:STI, author = "Gajanan Tudavekar and Santosh S. Saraf and Sanjay R. Patil", title = "Spatio-Temporal Inference Transformer Network for Video Inpainting", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "01", pages = "??--??", month = jan, year = "2023", DOI = "https://doi.org/10.1142/S0219467823500079", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Mar 25 07:40:33 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500079", abstract = "Video inpainting aims to complete in a visually pleasing way the missing regions in video frames. Video inpainting is an exciting task due to the variety of motions across different frames. The existing methods usually use attention models to inpaint videos by seeking the damaged content from other frames. Nevertheless, these methods suffer due to irregular attention weight from spatio-temporal dimensions, thus giving rise to artifacts in the inpainted video. To overcome the above problem, Spatio-Temporal Inference Transformer Network (STITN) has been proposed. The STITN aligns the frames to be inpainted and concurrently inpaints all the frames, and a spatio-temporal adversarial loss function improves the STITN. Our method performs considerably better than the existing deep learning approaches in quantitative and qualitative evaluation.", acknowledgement = ack-nhfb, articleno = "2350007", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Devi:2023:DSF, author = "Bhagyashri Devi and M. Mary Synthuja Jain Preetha", title = "A Descriptive Survey on Face Emotion Recognition Techniques", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "01", pages = "??--??", month = jan, year = "2023", DOI = "https://doi.org/10.1142/S0219467823500080", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Mar 25 07:40:33 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500080", abstract = "Recognition of natural emotion from human faces has applications in Human--Computer Interaction, image and video retrieval, automated tutoring systems, smart environment as well as driver warning systems. It is also a significant indication of nonverbal communication among the individuals. The assignment of Face Emotion Recognition (FER) is predominantly complex for two reasons. The first reason is the nonexistence of a large database of training images, and the second one is about classifying the emotions, which can be complex based on the static input image. In addition, robust unbiased FER in real time remains the foremost challenge for various supervised learning-based techniques. This survey analyzes diverse techniques regarding the FER systems. It reviews a bunch of research papers and performs a significant analysis. Initially, the analysis depicts various techniques that are contributed in different research papers. In addition, this paper offers a comprehensive study regarding the chronological review and performance achievements in each contribution. The analytical review is also concerned about the measures for which the maximum performance was achieved in several contributions. Finally, the survey is extended with various research issues and gaps that can be useful for the researchers to promote improved future works on the FER models.", acknowledgement = ack-nhfb, articleno = "2350008", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Canejo:2023:EDN, author = "Marcos Jos{\'e} Can{\^e}jo and Carlos Alexandre {Barros De Mello}", title = "Edge Detection in Natural Scenes Inspired by the Speed Drawing Challenge", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "01", pages = "??--??", month = jan, year = "2023", DOI = "https://doi.org/10.1142/S0219467823500092", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Mar 25 07:40:33 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500092", abstract = "Edge detection is a major step in several computer vision applications. Edges define the shape of objects to be used in a recognition system, for example. In this work, we introduce an approach to edge detection inspired by a challenge for artists: the Speed Drawing Challenge. In this challenge, a person is asked to draw the same figure in different times (as 10 min, 1 min and 10 s); at each time, different levels of details are drawn by the artist. In a short time stamp, just the major elements remain. This work proposes a new approach for producing images with different amounts of edges representing different levels of relevance. Our method uses superpixel to suppress image details, followed by Globalized Probability of Boundary (gPb) and Canny edge detection algorithms to create an image containing different number of edges. After that, an edge analysis step detects whose edges are the most relevant for the scene. The results are presented for the BSDS500 dataset and they are compared to other edge and contour detection algorithms by quantitative and qualitative means with very satisfactory results.", acknowledgement = ack-nhfb, articleno = "2350009", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Gaddour:2023:NMA, author = "Houda Gaddour and Slim Kanoun and Nicole Vincent", title = "A New Method for {Arabic} Text Detection in Natural Scene Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "01", pages = "??--??", month = jan, year = "2023", DOI = "https://doi.org/10.1142/S0219467823500109", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Mar 25 07:40:33 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500109", abstract = "Text in scene images can provide useful and vital information for content-based image analysis. Therefore, text detection and script identification in images are an important task. In this paper, we propose a new method for text detection in natural scene images, particularly for Arabic text, based on a bottom-up approach where four principal steps can be highlighted. The detection of extremely stable and homogeneous regions of interest (ROIs) is based on the Color Stability and Homogeneity Regions (CSHR) proposed technique. These regions are then labeled as textual or non-textual ROI. This identification is based on a structural approach. The textual ROIs are grouped to constitute zones according to spatial relations between them. Finally, the textual or non-textual nature of the constituted zones is refined. This last identification is based on handcrafted features and on features built from a Convolutional Neural Network (CNN) after learning. The proposed method was evaluated on the databases used for text detection in natural scene images: the competitions organized in 2017 edition of the International Conference on Document Analysis and Recognition (ICDAR2017), the Urdu-text database and our Natural Scene Image Database for Arabic Text detection (NSIDAT) database. The obtained experimental results seem to be interesting.", acknowledgement = ack-nhfb, articleno = "2350010", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Lepcha:2023:IMC, author = "Dawa Chyophel Lepcha and Bhawna Goyal and Ayush Dogra", title = "Image Matting: a Comprehensive Survey on Techniques, Comparative Analysis, Applications and Future Scope", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "01", pages = "??--??", month = jan, year = "2023", DOI = "https://doi.org/10.1142/S0219467823500110", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Mar 25 07:40:33 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500110", abstract = "In the era of rapid growth of technologies, image matting plays a key role in image and video editing along with image composition. In many significant real-world applications such as film production, it has been widely used for visual effects, virtual zoom, image translation, image editing and video editing. With recent advancements in digital cameras, both professionals and consumers have become increasingly involved in matting techniques to facilitate image editing activities. Image matting plays an important role to estimate {\em alpha matte\/} in the {\em unknown\/} region to distinguish {\em foreground\/} from the {\em background\/} region of an image using an input image and the corresponding trimap of an image which represents a {\em foreground\/} and {\em unknown\/} region. Numerous image matting techniques have been proposed recently to extract high-quality {\em matte\/} from image and video sequences. This paper illustrates a systematic overview of the current image and video matting techniques mostly emphasis on the current and advanced algorithms proposed recently. In general, image matting techniques have been categorized according to their underlying approaches, namely, sampling-based, propagation-based, combination of sampling and propagation-based and deep learning-based algorithms. The traditional image matting algorithms depend primarily on color information to predict {\em alpha matte\/} such as sampling-based, propagation-based or combination of sampling and propagation-based algorithms. However, these techniques mostly use low-level features and suffer from high-level {\em background\/} which tends to produce unwanted artifacts when color is same or semi-transparent in the {\em foreground\/} object. Image matting techniques based on deep learning have recently introduced to address the shortcomings of traditional algorithms. Rather than simply depending on the color information, it uses deep learning mechanism to estimate the {\em alpha matte\/} using an input image and the trimap of an image. A comprehensive survey on recent image matting algorithms and in-depth comparative analysis of these algorithms has been thoroughly discussed in this paper.", acknowledgement = ack-nhfb, articleno = "2350011", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Elmoufidi:2023:CMI, author = "Abdelali Elmoufidi and Ayoub Skouta and Said Jai-andaloussi and Ouail Ouchetto", title = "{CNN} with Multiple Inputs for Automatic Glaucoma Assessment Using Fundus Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "01", pages = "??--??", month = jan, year = "2023", DOI = "https://doi.org/10.1142/S0219467823500122", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Mar 25 07:40:33 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500122", abstract = "In the area of ophthalmology, glaucoma affects an increasing number of people. It is a major cause of blindness. Early detection avoids severe ocular complications such as glaucoma, cystoid macular edema, or diabetic proliferative retinopathy. Intelligent artificial intelligence has been confirmed beneficial for glaucoma assessment. In this paper, we describe an approach to automate glaucoma diagnosis using funds images. The setup of the proposed framework is in order: The Bi-dimensional Empirical Mode Decomposition (BEMD) algorithm is applied to decompose the Regions of Interest (ROI) to components (BIMFs+residue). CNN architecture VGG19 is implemented to extract features from decomposed BEMD components. Then, we fuse the features of the same ROI in a bag of features. These last very long; therefore, Principal Component Analysis (PCA) are used to reduce features dimensions. The bags of features obtained are the input parameters of the implemented classifier based on the Support Vector Machine (SVM). To train the built models, we have used two public datasets, which are ACRIMA and REFUGE. For testing our models, we have used a part of ACRIMA and REFUGE plus four other public datasets, which are RIM-ONE, ORIGA-light, Drishti-GS1, and sjchoi86-HRF. The overall precision of 98.31\%, 98.61\%, 96.43\%, 96.67\%, 95.24\%, and 98.60\% is obtained on ACRIMA, REFUGE, RIM-ONE, ORIGA-light, Drishti-GS1, and sjchoi86-HRF datasets, respectively, by using the model trained on REFUGE. Again an accuracy of 98.92\%, 99.06\%, 98.27\%, 97.10\%, 96.97\%, and 96.36\% is obtained in the ACRIMA, REFUGE, RIM-ONE, ORIGA-light, Drishti-GS1, and sjchoi86-HRF datasets, respectively, using the model training on ACRIMA. The experimental results obtained from different datasets demonstrate the efficiency and robustness of the proposed approach. A comparison with some recent previous work in the literature has shown a significant advancement in our proposal.", acknowledgement = ack-nhfb, articleno = "2350012", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Sangani:2023:PSS, author = "Dhara J. Sangani and Rajesh A. Thakker and S. D. Panchal and Rajesh Gogineni", title = "{Pan}-Sharpening for Spectral Details Preservation Via Convolutional Sparse Coding in Non-Subsampled Shearlet Space", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "02", pages = "??--??", month = mar, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467823500134", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Mar 25 07:40:34 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500134", abstract = "The optical satellite sensors encounter certain constraints on producing high-resolution multispectral (HRMS) images. Pan-sharpening (PS) is a remote sensing image fusion technique, which is an effective mechanism to overcome the limitations of available imaging products. The prevalent issue in PS algorithms is the imbalance between spatial quality and spectral details preservation, thereby producing intensity variations in the fused image. In this paper, a PS method is proposed based on convolutional sparse coding (CSC) implemented in the non-subsampled shearlet transform (NSST) domain. The source images, panchromatic (PAN) and multispectral (MS) images, are decomposed using NSST. The resultant high-frequency bands are fused using adaptive weights determined from chaotic grey wolf optimization (CGWO) algorithm. The CSC-based model is employed to fuse the low-frequency bands. Further, an iterative filtering mechanism is developed to enhance the quality of fused image. Four datasets with different geographical content like urban area, vegetation, etc. and eight existing algorithms are used for evaluation of the proposed PS method. The comprehensive visual and quantitative results approve that the proposed method accomplishes considerable improvement in spatial and spectral details equivalence in the pan-sharpened image.", acknowledgement = ack-nhfb, articleno = "2350013", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Fathima:2023:MIC, author = "M. Dhilsath Fathima and R. Hariharan and S. P. Raja", title = "Multiple Imputation by Chained Equations --- {$K$}-Nearest Neighbors and Deep Neural Network Architecture for Kidney Disease Prediction", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "02", pages = "??--??", month = mar, year = "2023", DOI = "https://doi.org/10.1142/S0219467823500146", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Mar 25 07:40:34 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500146", abstract = "Chronic kidney disease (CKD) is a health concern that affects people all over the world. Kidney dysfunction or impaired kidney functions are the causes of CKD. The machine learning-based prediction models are used to determine the risk level of CKD and assist healthcare practitioners in delaying and preventing the disease's progression. The researchers proposed many prediction models for determining the CKD risk level. Although these models performed well, their precision is limited since they do not handle missing values in the clinical dataset adequately. The missing values of a clinical dataset can degrade the training outcomes that leads to false predictions. Thus, imputing missing values increases the prediction model performance. This proposed work developed a novel imputation technique by combining Multiple Imputation by Chained Equations and K -Nearest Neighbors (MICE--KNN) for imputing the missing values. The experimental results show that MICE--KNN accurately predicts the missing values, and the Deep Neural Network (DNN) improves the prediction performance of the CKD model. Various metrics like mean absolute error, accuracy, specificity, Matthews correlation coefficient, the area under the curve, $ F_1$-score, sensitivity, and precision have been used to evaluate the proposed CKD model performance. The performance analysis exhibits that MICE--KNN with deep learning outperforms other classifiers. According to our experimental study, the MICE--KNN imputation algorithm with DNN is more appropriate for predicting the kidney disease.", acknowledgement = ack-nhfb, articleno = "2350014", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Jahnavi:2023:NES, author = "Yeturu Jahnavi and Poongothai Elango and S. P. Raja and P. Nagendra Kumar", title = "A Novel Ensemble Stacking Classification of Genetic Variations Using Machine Learning Algorithms", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "02", pages = "??--??", month = mar, year = "2023", DOI = "https://doi.org/10.1142/S0219467823500158", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Mar 25 07:40:34 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500158", abstract = "Genetics is the clinical review of congenital mutation, where the principal advantage of analyzing genetic mutation of humans is the exploration, analysis, interpretation and description of the genetic transmitted and inherited effect of several diseases such as cancer, diabetes and heart diseases. Cancer is the most troublesome and disordered affliction as the proportion of cancer sufferers is growing massively. Identification and discrimination of the mutations that impart to the enlargement of tumor from the unbiased mutations is difficult, as majority tumors of cancer are able to exercise genetic mutations. The genetic mutations are systematized and categorized to sort the cancer by way of medical observations and considering clinical studies. At the present time, genetic mutations are being annotated and these interpretations are being accomplished either manually or using the existing primary algorithms. Evaluation and classification of each and every individual genetic mutation was basically predicated on evidence from documented content built on medical literature. Consequently, as a means to build genetic mutations, basically, depending on the clinical evidences persists a challenging task. There exist various algorithms such as one hot encoding technique is used to derive features from genes and their variations, TF-IDF is used to extract features from the clinical text data. In order to increase the accuracy of the classification, machine learning algorithms such as support vector machine, logistic regression, Naive Bayes, etc., are experimented. A stacking model classifier has been developed to increase the accuracy. The proposed stacking model classifier has obtained the log loss 0.8436 and 0.8572 for cross-validation data set and test data set, respectively. By the experimentation, it has been proved that the proposed stacking model classifier outperforms the existing algorithms in terms of log loss. Basically, minimum log loss refers to the efficient model. Here the log loss has been reduced to less than 1 by using the proposed stacking model classifier. The performance of these algorithms can be gauged on the basis of the various measures like multi-class log loss.", acknowledgement = ack-nhfb, articleno = "2350015", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Shashikala:2023:SSM, author = "T. D. Shashikala and B. L. Sunitha and S. Basavarajappa and J. P. Davim", title = "Some Studies on Measurement of Worn Surface by Digital Image Processing", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "02", pages = "??--??", month = mar, year = "2023", DOI = "https://doi.org/10.1142/S021946782350016X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Mar 25 07:40:34 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S021946782350016X", abstract = "Digital image processing (DIP) becomes a common tool for analyzing engineering problems by fast, frequent and noncontact method of identification and measurement. An attempt has been made in the present investigation to use this method for automatically detecting the worn regions on the material surface and also its measurement. Brass material has been used for experimentation as it is used generally as a bearing material. A pin on disc dry sliding wear testing machine has been used for conducting the experiments by applying loads from 10 N to 50 N and by keeping sliding distance and sliding speed constant. After testing, images are acquired by using 1/2 inch interline transfer CCD image sensor with 795(H) {\^a} 896(V) spatial resolution of 8.6 {\textmu} m (H) {\^a} 8.3 {\textmu} m (V) unit cell. Denoising has been done to remove any possible noise followed by contrast stretching to enhance image for wear region extraction. Segmentation tool was used to divide the worn and unworn regions by identifying white regions greater than a threshold value with an objective of quantifying the worn surface for tested specimen. Canny edge detection and granulometry techniques have been used to quantify the wear region. The results revel that the specific wear rate increases with increase in applied load, at constant sliding speed and sliding distance. Similarly, the area of worn region as identified by DIP also increased from 42.7\% to 69.97\%. This is because of formation of deeper groves in the worn material.", acknowledgement = ack-nhfb, articleno = "2350016", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Spoorthi:2023:FCS, author = "B. Spoorthi and Shanthi Mahesh", title = "Firefly Competitive Swarm Optimization Based Hierarchical Attention Network for Lung Cancer Detection", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "02", pages = "??--??", month = mar, year = "2023", DOI = "https://doi.org/10.1142/S0219467823500171", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Mar 25 07:40:34 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500171", abstract = "Lung cancer is a severe disease, which causes high deaths in the world. Earlier discovery of lung cancer is useful to enhance the rate of survival in patients. Computed Tomography (CT) is utilized for determining the tumor and identifying the cancer level in the body. However, the issues of CT images cause less tumor visibility areas and unconstructive rates in tumor regions. This paper devises an optimization-driven technique for classifying lung cancer. The CT image is utilized for determining the position of the tumor. Here, the CT image undergoes segmentation, which is performed using the DeepJoint model. Furthermore, the feature extraction is carried out, wherein features such as local ternary pattern-based features, Histogram of Gradients (HoG) features, and statistical features, like variance, mean, kurtosis, energy, entropy, and skewness. The categorization of lung cancer is performed using Hierarchical Attention Network (HAN). The training of HAN is carried out using proposed Firefly Competitive Swarm Optimization (FCSO), which is devised by combining firefly algorithm (FA), and Competitive Swarm Optimization (CSO). The proposed FCSO-based HAN provided effective performance with high accuracy of 91.3\%, sensitivity of 88\%, and specificity of 89.1\%.", acknowledgement = ack-nhfb, articleno = "2350017", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Mondal:2023:DCW, author = "Saorabh Kumar Mondal and Arpitam Chatterjee and Bipan Tudu", title = "{DCT} Coefficients Weighting ({DCTCW})-Based {Gray Wolf Optimization (GWO)} for Brightness Preserving Image Contrast Enhancement", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "02", pages = "??--??", month = mar, year = "2023", DOI = "https://doi.org/10.1142/S0219467823500183", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Mar 25 07:40:34 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500183", abstract = "Image contrast enhancement (CE) is a frequent image enhancement requirement in diverse applications. Histogram equalization (HE), in its conventional and different further improved ways, is a popular technique to enhance the image contrast. The conventional as well as many of the later versions of HE algorithms often cause loss of original image characteristics particularly brightness distribution of original image that results artificial appearance and feature loss in the enhanced image. Discrete Cosine Transform (DCT) coefficient mapping is one of the recent methods to minimize such problems while enhancing the image contrast. Tuning of DCT parameters plays a crucial role towards avoiding the saturations of pixel values. Optimization can be a possible solution to address this problem and generate contrast enhanced image preserving the desired original image characteristics. Biological behavior-inspired optimization techniques have shown remarkable betterment over conventional optimization techniques in different complex engineering problems. Gray wolf optimization (GWO) is a comparatively new algorithm in this domain that has shown promising potential. The objective function has been formulated using different parameters to retain original image characteristics. The objective evaluation against CEF, PCQI, FSIM, BRISQUE and NIQE with test images from three standard databases, namely, SIPI, TID and CSIQ shows that the presented method can result in values up to 1.4, 1.4, 0.94, 19 and 4.18, respectively, for the stated metrics which are competitive to the reported conventional and improved techniques. This paper can be considered a first-time application of GWO towards DCT-based image CE.", acknowledgement = ack-nhfb, articleno = "2350018", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Lohith:2023:MBP, author = "M. S. Lohith and Yoga Suhas Kuruba Manjunath and M. N. Eshwarappa", title = "Multimodal Biometric Person Authentication Using Face, Ear and Periocular Region Based on Convolution Neural Networks", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "02", pages = "??--??", month = mar, year = "2023", DOI = "https://doi.org/10.1142/S0219467823500195", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Mar 25 07:40:34 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500195", abstract = "Biometrics is an active area of research because of the increase in need for accurate person identification in numerous applications ranging from entertainment to security. Unimodal and multimodal are the well-known biometric methods. Unimodal biometrics uses one biometric modality of a person for person identification. The performance of an unimodal biometric system is degraded due to certain limitations such as: intra-class variations and nonuniversality. The person identification using more than one biometric modality of a person is multimodal biometrics. This method of identification has gained more interest due to resistance on spoof attacks and more recognition rate. Conventional methods of feature extraction have difficulty in engineering features that are liable to more variations such as illumination, pose and age variations. Feature extraction using convolution neural network (CNN) can overcome these difficulties because large dataset with robust variations can be used for training, where CNN can learn these variations. In this paper, we propose multimodal biometrics at feature level horizontal fusion using face, ear and periocular region biometric modalities and apply deep learning CNN for feature representation and also we propose face, ear and periocular region dataset that are robust to intra-class variations. The evaluation of the system is made by using proposed database. Accuracy, Precision, Recall and F1 score are calculated to evaluate the performance of the system and had shown remarkable improvement over existing biometric system.", acknowledgement = ack-nhfb, articleno = "2350019", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Sudha:2023:CES, author = "K. Antony Sudha and V. Cibi Castro and G. Muthulakshmi and T. Ilam Parithi and S. P. Raja", title = "A Chaotic Encryption System Based on {DNA} Coding Using a Deep Neural Network", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "02", pages = "??--??", month = mar, year = "2023", DOI = "https://doi.org/10.1142/S0219467823500201", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Mar 25 07:40:34 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500201", abstract = "Critical to computer vision applications, deep learning demands a massive volume of training data for great performance. However, encrypting the sensitive information in a photograph is fraught with difficulty, despite rapid technological advancements. The Advanced Encryption System (AES) is the bedrock of classical encryption technologies. The Data Encryption Standard (DES) has low sensitivity, with weak anti-hacking capabilities. In a chaotic encryption system, a chaotic logistic map is employed to generate a key double logistic sequence, and deoxyribonucleic acid (DNA) matrices are created by DNA coding. The XOR operation is carried out between the DNA sequence matrix and the key matrix. Finally, the DNA matrix is decoded to obtain an encrypted image. Given that encrypted images are susceptible to attacks, a rapid and efficient Convolutional Neural Network (CNN) denoiser is used that enhances the robustness of the algorithm by maximizing the resolution of rebuilt images. The use of a key mixing percentage factor gives the proposed system vast key space and great key sensitivity. Its implementation is examined using statistical techniques such as histogram analysis, information entropy, key space analysis and key sensitivity. Experiments have shown that the suggested system is secure and robust to statistical and noise attacks.", acknowledgement = ack-nhfb, articleno = "2350020", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Das:2023:NSE, author = "Gyanesh Das and Rutuparna Panda and Leena Samantaray and Sanjay Agrawal", title = "A Novel Segmentation Error Minimization-Based Method for Multilevel Optimal Threshold Selection Using Opposition Equilibrium Optimizer", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "02", pages = "??--??", month = mar, year = "2023", DOI = "https://doi.org/10.1142/S0219467823500213", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Mar 25 07:40:34 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500213", abstract = "Image segmentation is imperative for image processing applications. Thresholding technique is the easiest way of partitioning an image into different regions. Mostly, entropy-based threshold selection methods are used for multilevel thresholding. However, these methods suffer from their dependencies on spatial distribution of gray values. To solve this issue, a novel segmentation error minimization (SEM)-based method for multilevel optimal threshold selection using opposition equilibrium optimizer (OEO) is suggested. In this contribution, a new segmentation score (SS) (objective function) is derived while minimizing the segmentation error function. Our proposal is explicitly free from gray level spatial distribution of an image. Optimal threshold values are achieved by maximizing the SS (fitness value) using OEO. The key to success is the maximization of score among classes, ensuring the sharpening of the shred boundary between classes, leading to an improved threshold selection method. It is empirically demonstrated how the optimal threshold selection is made. Experimental results are presented using standard test images. Standard measures like PSNR, SSIM and FSIM are used for validation The results are compared with state-of-the-art entropy-based technique. Our method performs well both qualitatively and quantitatively. The suggested technique would be useful for biomedical image segmentation.", acknowledgement = ack-nhfb, articleno = "2350021", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Pemula:2023:GRF, author = "Rambabu Pemula and Sagenela Vijaya Kumar and C. Nagaraju", title = "Generation of Random Fields for Image Segmentation Techniques: a Review", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "02", pages = "??--??", month = mar, year = "2023", DOI = "https://doi.org/10.1142/S0219467823500225", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Mar 25 07:40:34 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500225", abstract = "Generation of random fields (GRF) for image segmentation represents partitioning an image into different regions that are homogeneous or have similar facets of the image. It is one of the most challenging tasks in image processing and a very important pre-processing step in the fields of computer vision, image analysis, medical image processing, pattern recognition, remote sensing, and geographical information system. Many researchers have presented numerous image segmentation approaches, but still, there are challenges like segmentation of low contrast images, removal of shadow in the images, reduction of high dimensional images, and computational complexity of segmentation techniques. In this review paper, the authors address these issues. The experiments are conducted and tested on the Berkely dataset (BSD500), Semantic dataset, and our own dataset, and the results are shown in the form of tables and graphs.", acknowledgement = ack-nhfb, articleno = "2350022", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Raju:2023:PAC, author = "Ayalapogu Ratna Raju and Suresh Pabboju and Rajeswara Rao Ramisetty", title = "Performance Analysis and Critical Review on Segmentation Techniques for Brain Tumor Classification", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "02", pages = "??--??", month = mar, year = "2023", DOI = "https://doi.org/10.1142/S0219467823500237", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Mar 25 07:40:34 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500237", abstract = "An irregular growth in brain cells causes brain tumors. In recent years, a considerable rate of increment in medical cases regarding brain tumors has been observed, affecting adults and children. However, it is highly curable in recent times only if detected in the early time of tumor growth. Moreover, there are many sophisticated approaches devised by researchers for predicting the tumor regions and their stages. In addition, Magnetic Resonance Imaging (MRI) is utilized commonly by radiologists to evaluate tumors. In this paper, the input image is from a database, and brain tumor segmentation is performed using various segmentation techniques. Here, the comparative analysis is performed by comparing the performance of segmentation approaches, like Hybrid Active Contour (HAC) model, Bayesian Fuzzy Clustering (BFC), Active Contour (AC), Fuzzy C-Means (FCM) clustering technique, Sparse (Sparse FCM), and Black Hole Entropy Fuzzy Clustering (BHEFC) model. Moreover, segmentation technique performance is evaluated with the Dice coefficient, Jaccard coefficient, and segmentation accuracy. The proposed method shows high Dice and Jaccard coefficients of 0.7809 and 0.6456 by varying iteration with the REMBRANDT dataset and a better segmentation accuracy of 0.9789 by changing image size in the Brats-2015 database.", acknowledgement = ack-nhfb, articleno = "2350023", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Chamundeshwari:2023:HPE, author = "Chamundeshwari and Nagashetteppa Biradar and Udaykumar", title = "Hybrid Pattern Extraction with Deep Learning-Based Heart Disease Diagnosis Using Echocardiogram Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "02", pages = "??--??", month = mar, year = "2023", DOI = "https://doi.org/10.1142/S0219467823500249", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Mar 25 07:40:34 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500249", abstract = "Echocardiography represents a noninvasive diagnostic approach that offers information concerning hemodynamics and cardiac function. It is a familiar cardiovascular diagnostic test apart from chest X-ray and echocardiography. The medical knowledge is enhanced by the Artificial Intelligence (AI) approaches like deep learning and machine learning because of the increase in the complexity as well as the volume of the data that in turn unlocks the clinically significant information. Similarly, the usage of developing information as well as communication technologies is becoming important for generating a persistent healthcare service via which the chronic disease and elderly patients get their medical facility at their home that in turn enhances the life quality and avoids hospitalizations. The main intention of this paper is to design and develop a novel heart disease diagnosis using speckle-noise reduction and deep learning-based feature learning and classification. The datasets gathered from the hospital are composed of both the images and the video frames. Since echocardiogram images suffer from speckle noise, the initial process is the speckle-noise reduction technique. Then, the pattern extraction is performed by combining the Local Binary Pattern (LBP), and Weber Local Descriptor (WLD) referred to as the hybrid pattern extraction. The deep feature learning is conducted by the optimized Convolutional Neural Network (CNN), in which the features are extracted from the max-pooling layer, and the fully connected layer is replaced by the optimized Recurrent Neural Network (RNN) for handling the diagnosis of heart disease, thus proposed model is termed as CRNN. The novel Adaptive Electric Fish Optimization (A-EFO) is used for performing feature learning and classification. In the final step, the best accuracy is achieved with the introduced model, while a comparative analysis is accomplished over the traditional models. From the experimental analysis, FDR of A-EFO-CRNN at 75\% learning percentage is 21.05\%, 15\%, 48.89\%, and 71.95\% progressed than CRNN, CNN, RNN, and NN, respectively. Thus, the performance of the A-EFO-CRNN is enriched than the existing heuristic-oriented and classifiers in terms of the image dataset.", acknowledgement = ack-nhfb, articleno = "2350024", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Vasantharaj:2023:ABI, author = "A. Vasantharaj and Pacha Shoba Rani and Sirajul Huque and K. S. Raghuram and R. Ganeshkumar and Sebahadin Nasir Shafi", title = "Automated Brain Imaging Diagnosis and Classification Model using Rat Swarm Optimization with Deep Learning based Capsule Network", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "03", pages = "??--??", month = may, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467822400010", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Jun 2 06:51:21 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822400010", abstract = "Earlier identification of brain tumor (BT) is essential to increase the survival rate of the patients. The commonly used imaging technique for BT diagnosis is magnetic resonance imaging (MRI). Automated BT classification model is required for assisting the radiologists to save time and enhance efficiency. The classification of BT is difficult owing to the non-uniform shapes of tumors and location of tumors in the brain. Therefore, deep learning (DL) models can be employed for the effective identification, prediction, and diagnosis of diseases. In this view, this paper presents an automated BT diagnosis using rat swarm optimization (RSO) with deep learning based capsule network (DLCN) model, named RSO-DLCN model. The presented RSO-DLCN model involves bilateral filtering (BF) based preprocessing to enhance the quality of the MRI. Besides, non-iterative grabcut based segmentation (NIGCS) technique is applied to detect the affected tumor regions. In addition, DLCN model based feature extractor with RSO algorithm based parameter optimization processes takes place. Finally, extreme learning machine with stacked autoencoder (ELM-SA) based classifier is employed for the effective classification of BT. For validating the BT diagnostic performance of the presented RSO-DLCN model, an extensive set of simulations were carried out and the results are inspected under diverse dimensions. The simulation outcome demonstrated the promising results of the RSO-DLCN model on BT diagnosis with the sensitivity of 98.4\%, specificity of 99\%, and accuracy of 98.7\%.", acknowledgement = ack-nhfb, articleno = "2240001", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", remark = "Special Issue on Advances in Deep Learning Algorithms for Brain Imaging Guest Editor: Dr. Bala Anand Muthu", } @Article{Kiran:2023:MLD, author = "S. Vishwa Kiran and Inderjeet Kaur and K. Thangaraj and V. Saveetha and R. Kingsy Grace and N. Arulkumar", title = "Machine Learning with Data Science-Enabled Lung Cancer Diagnosis and Classification Using Computed Tomography Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "03", pages = "??--??", month = may, year = "2023", DOI = "https://doi.org/10.1142/S0219467822400022", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Jun 2 06:51:21 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822400022", abstract = "In recent times, the healthcare industry has been generating a significant amount of data in distinct formats, such as electronic health records (EHR), clinical trials, genetic data, payments, scientific articles, wearables, and care management databases. Data science is useful for analysis (pattern recognition, hypothesis testing, risk valuation) and prediction. The major, primary usage of data science in the healthcare domain is in medical imaging. At the same time, lung cancer diagnosis has become a hot research topic, as automated disease detection poses numerous benefits. Although numerous approaches have existed in the literature for lung cancer diagnosis, the design of a novel model to automatically identify lung cancer is a challenging task. In this view, this paper designs an automated machine learning (ML) with data science-enabled lung cancer diagnosis and classification (MLDS-LCDC) using computed tomography (CT) images. The presented model initially employs Gaussian filtering (GF)-based pre-processing technique on the CT images collected from the lung cancer database. Besides, they are fed into the normalized cuts (Ncuts) technique where the nodule in the pre-processed image can be determined. Moreover, the oriented FAST and rotated BRIEF (ORB) technique is applied as a feature extractor. At last, sunflower optimization-based wavelet neural network (SFO-WNN) model is employed for the classification of lung cancer. In order to examine the diagnostic outcome of the MLDS-LCDC model, a set of experiments were carried out and the results are investigated in terms of different aspects. The resultant values demonstrated the effectiveness of the MLDS-LCDC model over the other state-of-the-art methods with the maximum sensitivity of 97.01\%, specificity of 98.64\%, and accuracy of 98.11\%.", acknowledgement = ack-nhfb, articleno = "2240002", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", remark = "Special Issue on Advances in Deep Learning Algorithms for Brain Imaging Guest Editor: Dr. Bala Anand Muthu", } @Article{Sammeta:2023:DOS, author = "Naresh Sammeta and Latha Parthiban", title = "Data Ownership and Secure Medical Data Transmission using Optimal Multiple Key-Based Homomorphic Encryption with Hyperledger Blockchain", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "03", pages = "??--??", month = may, year = "2023", DOI = "https://doi.org/10.1142/S0219467822400034", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Jun 2 06:51:21 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/bitcoin.bib; https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822400034", abstract = "Recent healthcare systems are defined as highly complex and expensive. But it can be decreased with enhanced electronic health records (EHR) management, using blockchain technology. The healthcare sector in today's world needs to address two major issues, namely data ownership and data security. Therefore, blockchain technology is employed to access and distribute the EHRs. With this motivation, this paper presents novel data ownership and secure medical data transmission model using optimal multiple key-based homomorphic encryption (MHE) with Hyperledger blockchain (OMHE-HBC). The presented OMHE-HBC model enables the patients to access their own data, provide permission to hospital authorities, revoke permission from hospital authorities, and permit emergency contacts. The proposed model involves the MHE technique to securely transmit the data to the cloud and prevent unauthorized access to it. Besides, the optimal key generation process in the MHE technique takes place using a hosted cuckoo optimization (HCO) algorithm. In addition, the proposed model enables sharing of EHRs by the use of multi-channel HBC, which makes use of one blockchain to save patient visits and another one for the medical institutions in recoding links that point to EHRs stored in external systems. A complete set of experiments were carried out in order to validate the performance of the suggested model, and the results were analyzed under many aspects. A comprehensive comparison of results analysis reveals that the suggested model outperforms the other techniques.", acknowledgement = ack-nhfb, articleno = "2240003", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", remark = "Special Issue on Advances in Deep Learning Algorithms for Brain Imaging Guest Editor: Dr. Bala Anand Muthu", } @Article{Madhusudhan:2023:FVR, author = "M. V. Madhusudhan and V. Udaya Rani and Chetana Hegde", title = "Finger Vein Recognition Model for Biometric Authentication Using Intelligent Deep Learning", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "03", pages = "??--??", month = may, year = "2023", DOI = "https://doi.org/10.1142/S0219467822400046", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Jun 2 06:51:21 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822400046", abstract = "In recent years, biometric authentication systems have remained a hot research topic, as they can recognize or authenticate a person by comparing their data to other biometric data stored in a database. Fingerprints, palm prints, hand vein, finger vein, palm vein, and other anatomic or behavioral features have all been used to develop a variety of biometric approaches. Finger vein recognition (FVR) is a common method of examining the patterns of the finger veins for proper authentication among the various biometrics. Finger vein acquisition, preprocessing, feature extraction, and authentication are all part of the proposed intelligent deep learning-based FVR (IDL-FVR) model. Infrared imaging devices have primarily captured the use of finger veins. Furthermore, a region of interest extraction process is carried out in order to save the finger part. The shark smell optimization algorithm is used to tune the hyperparameters of the bidirectional long--short-term memory model properly. Finally, an authentication process based on Euclidean distance is performed, which compares the features of the current finger vein image to those in the database. The IDL-FVR model surpassed the earlier methods by accomplishing a maximum accuracy of 99.93\%. Authentication is successful when the Euclidean distance is small and vice versa.", acknowledgement = ack-nhfb, articleno = "2240004", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", remark = "Special Issue on Advances in Deep Learning Algorithms for Brain Imaging Guest Editor: Dr. Bala Anand Muthu", } @Article{Banerjee:2023:SVD, author = "Rudranath Banerjee and Sourav De and Shouvik Dey", title = "A Survey on Various Deep Learning Algorithms for an Efficient Facial Expression Recognition System", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "03", pages = "??--??", month = may, year = "2023", DOI = "https://doi.org/10.1142/S0219467822400058", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Jun 2 06:51:21 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822400058", abstract = "Facial Expression (FE) encompasses information concerning the emotional together with the physical state of a human. In the last few years, FE Recognition (FER) has turned out to be a propitious research field. FER is the chief processing technique for non-verbal intentions, and also it is a significant and propitious computer vision together with the artificial intelligence field. As a novel machine learning theory, Deep Learning (DL) not only highlights the depth of the learning model but also emphasizes the significance of Feature Learning (FL) for the network model, and it has made several research achievements in FER. Here, the present research states are examined typically from the latest FE extraction algorithm as well as the FER centered on DL. The research on classifiers gathered from recent papers discloses a more powerful as well as reliable comprehending of the peculiar traits of classifiers for research fellows. At the ending of the survey, few problems in addition to chances that are required to be tackled in the upcoming future are presented.", acknowledgement = ack-nhfb, articleno = "2240005", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", remark = "Special Issue on Advances in Deep Learning Algorithms for Brain Imaging Guest Editor: Dr. Bala Anand Muthu", } @Article{Thushara:2023:GTB, author = "A. Thushara and C. Ushadevi Amma and Ansamma John", title = "Graph Theory-Based Brain Network Connectivity Analysis and Classification of {Alzheimer}'s Disease", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "03", pages = "??--??", month = may, year = "2023", DOI = "https://doi.org/10.1142/S021946782240006X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Jun 2 06:51:21 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S021946782240006X", abstract = "Alzheimer's Disease (AD) is basically a progressive neurodegenerative disorder associated with abnormal brain networks that affect millions of elderly people and degrades their quality of life. The abnormalities in brain networks are due to the disruption of White Matter (WM) fiber tracts that connect the brain regions. Diffusion-Weighted Imaging (DWI) captures the brain's WM integrity. Here, the correlation betwixt the WM degeneration and also AD is investigated by utilizing graph theory as well as Machine Learning (ML) algorithms. By using the DW image obtained from Alzheimer's Disease Neuroimaging Initiative (ADNI) database, the brain graph of each subject is constructed. The features extracted from the brain graph form the basis to differentiate between Mild Cognitive Impairment (MCI), Control Normal (CN) and AD subjects. Performance evaluation is done using binary and multiclass classification algorithms and obtained an accuracy that outperforms the current top-notch DWI-based studies.", acknowledgement = ack-nhfb, articleno = "2240006", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", remark = "Special Issue on Advances in Deep Learning Algorithms for Brain Imaging Guest Editor: Dr. Bala Anand Muthu", } @Article{Malathi:2023:RRC, author = "V. Malathi and M. P. Gopinath", title = "A Review on Rice Crop Disease Classification Using Computational Approach", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "03", pages = "??--??", month = may, year = "2023", DOI = "https://doi.org/10.1142/S0219467822400071", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Jun 2 06:51:21 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822400071", abstract = "Rice is a significant cereal crop across the world. In rice cultivation, different types of sowing methods are followed, and thus bring in issues regarding sampling collection. Climate, soil, water level, and a diversified variety of crop seeds (hybrid and traditional varieties) and the period of growth are some of the challenges. This survey mainly focuses on rice crop diseases which affect the parts namely leaves, stems, roots, and spikelet; it mainly focuses on leaf-based diseases. Existing methods for diagnosing leaf disease include statistical approaches, data mining, image processing, machine learning, and deep learning techniques. This review mainly addresses diseases of the rice crop, a framework to diagnose rice crop diseases, and computational approaches in Image Processing, Machine Learning, Deep Learning, and Convolutional Neural Networks. Based on performance indicators, interpretations were made for the following algorithms namely support vector machine (SVM), convolutional neural network (CNN), backpropagational neural network (BPNN), and feedforward neural network (FFNN).", acknowledgement = ack-nhfb, articleno = "2240007", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", remark = "Special Issue on Advances in Deep Learning Algorithms for Brain Imaging Guest Editor: Dr. Bala Anand Muthu", } @Article{Kumar:2023:HBV, author = "T. Satish Kumar and S. Jothilakshmi and Batholomew C. James and M. Prakash and N. Arulkumar and C. Rekha", title = "{HHO}-Based Vector Quantization Technique for Biomedical Image Compression in Cloud Computing", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "03", pages = "??--??", month = may, year = "2023", DOI = "https://doi.org/10.1142/S0219467822400083", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Jun 2 06:51:21 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/datacompression.bib; https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822400083", abstract = "In the present digital era, the exploitation of medical technologies and massive generation of medical data using different imaging modalities, adequate storage, management, and transmission of biomedical images necessitate image compression techniques. Vector quantization (VQ) is an effective image compression approach, and the widely employed VQ technique is Linde--Buzo--Gray (LBG), which generates local optimum codebooks for image compression. The codebook construction is treated as an optimization issue solved with utilization of metaheuristic optimization techniques. In this view, this paper designs an effective biomedical image compression technique in the cloud computing (CC) environment using Harris Hawks Optimization (HHO)-based LBG techniques. The HHO-LBG algorithm achieves a smooth transition among exploration as well as exploitation. To investigate the better performance of the HHO-LBG technique, an extensive set of simulations was carried out on benchmark biomedical images. The proposed HHO-LBG technique has accomplished promising results in terms of compression performance and reconstructed image quality.", acknowledgement = ack-nhfb, articleno = "2240008", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", remark = "Special Issue on Advances in Deep Learning Algorithms for Brain Imaging Guest Editor: Dr. Bala Anand Muthu", } @Article{Kumar:2023:WTO, author = "K. Praveen Kumar and C. Venkata Narasimhulu and K. Satya Prasad", title = "{$2$D} Wavelet Tree Ordering Based Localized Total Variation Model for Efficient Image Restoration", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "03", pages = "??--??", month = may, year = "2023", DOI = "https://doi.org/10.1142/S0219467822400095", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Jun 2 06:51:21 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822400095", abstract = "The degraded image during the process of image analysis needs more number of iterations to restore it. These iterations take long waiting time and slow scanning, resulting in inefficient image restoration. A few numbers of measurements are enough to recuperate an image with good condition. Due to tree sparsity, a 2D wavelet tree reduces the number of coefficients and iterations to restore the degraded image. All the wavelet coefficients are extracted with overlaps as low and high sub-band space and ordered them such that they are decomposed in the tree ordering structured path. Some articles have addressed the problems with tree sparsity and total variation (TV), but few authors endorsed the benefits of tree sparsity. In this paper, a spatial variation regularization algorithm based on tree order is implemented to change the window size and variation estimators to reduce the loss of image information and to solve the problem of image smoothing operation. The acceptance rate of the tree-structured path relies on local variation estimators to regularize the performance parameters and update them to restore the image. For this, the Localized Total Variation (LTV) method is proposed and implemented on a 2D wavelet tree ordering structured path based on the proposed image smooth adjustment scheme. In the end, a reliable reordering algorithm proposed to reorder the set of pixels and to increase the reliability of the restored image. Simulation results clearly show that the proposed method improved the performance compared to existing methods of image restoration.", acknowledgement = ack-nhfb, articleno = "2240009", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", remark = "Special Issue on Advances in Deep Learning Algorithms for Brain Imaging Guest Editor: Dr. Bala Anand Muthu", } @Article{Reddy:2023:MMM, author = "Mummadi Gowthami Reddy and Palagiri Veera Narayana Reddy and Patil Ramana Reddy", title = "Multi-Modal Medical Image Fusion Using 3-Stage Multiscale Decomposition and {PCNN} with Adaptive Arguments", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "03", pages = "??--??", month = may, year = "2023", DOI = "https://doi.org/10.1142/S0219467822400101", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Jun 2 06:51:21 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822400101", abstract = "In the current era of technological development, medical imaging plays an important role in many applications of medical diagnosis and therapy. In this regard, medical image fusion could be a powerful tool to combine multi-modal images by using image processing techniques. But, conventional approaches failed to provide the effective image quality assessments and robustness of fused image. To overcome these drawbacks, in this work three-stage multiscale decomposition (TSMSD) using pulse-coupled neural networks with adaptive arguments (PCNN-AA) approach is proposed for multi-modal medical image fusion. Initially, nonsubsampled shearlet transform (NSST) is applied onto the source images to decompose them into low frequency and high frequency bands. Then, low frequency bands of both the source images are fused using nonlinear anisotropic filtering with discrete Karhunen--Loeve transform (NLAF-DKLT) methodology. Next, high frequency bands obtained from NSST are fused using PCNN-AA approach. Now, fused low frequency and high frequency bands are reconstructed using NSST reconstruction. Finally, band fusion rule algorithm with pyramid reconstruction is applied to get final fused medical image. Extensive simulation outcome discloses the superiority of proposed TSMSD using PCNN-AA approach as compared to state-of-the-art medical image fusion methods in terms of fusion quality metrics such as entropy (E), mutual information (MI), mean (M), standard deviation (STD), correlation coefficient (CC) and computational complexity.", acknowledgement = ack-nhfb, articleno = "2240010", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", remark = "Special Issue on Advances in Deep Learning Algorithms for Brain Imaging Guest Editor: Dr. Bala Anand Muthu", } @Article{Naveen:2023:FDA, author = "J. Naveen and Sheba Selvam and Blessy Selvam", title = "{FO-DPSO} Algorithm for Segmentation and Detection of Diabetic Mellitus for Ulcers", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "03", pages = "??--??", month = may, year = "2023", DOI = "https://doi.org/10.1142/S0219467822400113", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Jun 2 06:51:21 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822400113", abstract = "In recent days, the major concern for diabetic patients is foot ulcers. According to the survey, among 15 people among 100 are suffering from this foot ulcer. The wound or ulcer found which is found in diabetic patients consumes more time to heal, also required more conscious treatment. Foot ulcers may lead to deleterious danger condition and also may be the cause for loss of limb. By understanding this grim condition, this paper proposes Fractional-Order Darwinian Particle Swarm Optimization (FO-DPSO) technique for analyzing foot ulcer 2D color images. This paper deals with standard image processing, i.e. efficient segmentation using FO-DPSO algorithm and extracting textural features using Gray Level Co-occurrence Matrix (GLCM) technique. The whole effort projected results as accuracy of 91.2\%, sensitivity of 100\% and specificity as 96.7\% for Na{\"\i}ve Bayes classifier and accuracy of 91.2\%, sensitivity of 100\% and sensitivity of 79.6\% for Hoeffding tree classifier.", acknowledgement = ack-nhfb, articleno = "2240011", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", remark = "Special Issue on Advances in Deep Learning Algorithms for Brain Imaging Guest Editor: Dr. Bala Anand Muthu", } @Article{Gayatri:2023:CID, author = "Erapaneni Gayatri and S. L. Aarthy", title = "Challenges and Imperatives of Deep Learning Approaches for Detection of Melanoma: a Review", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "03", pages = "??--??", month = may, year = "2023", DOI = "https://doi.org/10.1142/S0219467822400125", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Jun 2 06:51:21 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822400125", abstract = "Recently, melanoma became one of the deadliest forms of skin cancer due to ultraviolet rays. The diagnosis of melanoma is very crucial if it is not identified in the early stages and later on, in the advanced stages, it affects the other organs of the body, too. Earlier identification of melanoma plays a major role in the survival chances of a human. The manual detection of tumor thickness is a very difficult task so dermoscopy is used to measure the thickness of the tumor which is a non-invasive method. Computer-aided diagnosis is one of the greatest evolutions in the medical sector, this system helps the doctors for the automated diagnosis of the disease because it improves accurate disease detection. In the world of digital images, some phases are required to remove the artifacts for achieving the best accurate diagnosis results such as the acquisition of an image, pre-processing, segmentation, feature selection, extraction and finally classification phase. This paper mainly focuses on the various deep learning techniques like convolutional neural networks, recurrent neural networks, You Only Look Once for the purpose of classification and prediction of the melanoma and is also focuses on the other variant of melanomas, i.e. ocular melanoma and mucosal melanoma because it is not a matter where the melanoma starts in the body.", acknowledgement = ack-nhfb, articleno = "2240012", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", remark = "Special Issue on Advances in Deep Learning Algorithms for Brain Imaging Guest Editor: Dr. Bala Anand Muthu", } @Article{Rao:2023:DLB, author = "E. Srinivasa Rao and Ch. Raghava Prasad", title = "Deep Learning-Based Medical Image Fusion Using Integrated Joint Slope Analysis with Probabilistic Parametric Steered Image Filter", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "03", pages = "??--??", month = may, year = "2023", DOI = "https://doi.org/10.1142/S0219467822400137", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Jun 2 06:51:21 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467822400137", abstract = "Medical image fusion plays a significant role in medical diagnosis applications. Although the conventional approaches have produced moderate visual analysis, still there is a scope to improve the performance parameters and reduce the computational complexity. Thus, this article implemented the hybrid fusion method by using the novel implementation of joint slope analysis (JSA), probabilistic parametric steered image filtration (PPSIF), and deep learning convolutional neural networks (DLCNNs)-based SR Fusion Net. Here, JSA decomposes the images to estimate edge-based slopes and develops the edge-preserving approximate layers from the multi-modal medical images. Further, PPSIF is used to generate the feature fusion with base layer-based weight maps. Then, the SR Fusion Net is used to generate the spatial and texture feature-based weight maps. Finally, optimal fusion rule is applied on the detail layers generated from the base layer and approximate layer, which resulted in the fused outcome. The proposed method is capable of performing the fusion operation between various modalities of images, such as MRI-CT, MRI-PET, and MRI-SPECT combinations by using two different architectures. The simulation results show that the proposed method resulted in better subjective and objective performance as compared to state of art approaches.", acknowledgement = ack-nhfb, articleno = "2240013", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", remark = "Special Issue on Advances in Deep Learning Algorithms for Brain Imaging Guest Editor: Dr. Bala Anand Muthu", } @Article{Suresh:2023:DLC, author = "Gulivindala Suresh and Chanamallu Srinivasa Rao", title = "Detection and Localization of Copy--Move Forgery in Digital Images: Review and Challenges", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "04", pages = "??--??", month = jul, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467823500250", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Aug 5 16:18:20 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500250", abstract = "Copy move forgery in digital images became a common problem due to the wide accessibility of image processing algorithms and open-source editing software. The human visual system cannot identify the traces of forgery in the tampered image. The proliferation of such digital images through the internet and social media is possible with a finger touch. These tampered images have been used in news reports, judicial forensics, medical records, and financial statements. In this paper, a detailed review has been carried on various copy-move forgery detection (CMFD) and localization techniques. Further, challenges in the research are identified along with possible solutions.", acknowledgement = ack-nhfb, articleno = "2350025", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Islam:2023:CSD, author = "Md. Shafiqul Islam and Rafiqul Islam", title = "A Critical Survey on Developed Reconstruction Algorithms for Computed Tomography Imaging from a Limited Number of Projections", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "04", pages = "??--??", month = jul, year = "2023", DOI = "https://doi.org/10.1142/S0219467823500262", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Aug 5 16:18:20 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500262", abstract = "Rapid system and hardware development of X-ray computed tomography (CT) technologies has been accompanied by equally exciting advances in image reconstruction algorithms. Of the two reconstruction algorithms, analytical and iterative, iterative reconstruction (IR) algorithms have become a clinically viable option in CT imaging. The first CT scanners in the early 1970s used IR algorithms, but lack of computation power prevented their clinical use. In 2009, the first IR algorithms became commercially available and replaced conventionally established analytical algorithms as filtered back projection. Since then, IR has played a vital role in the field of radiology. Although all available IR algorithms share the common mechanism of artifact reduction and/or potential for radiation dose reduction, the magnitude of these effects depends upon specific IR algorithms. IR reconstructs images by iteratively optimizing an objective function. The objective function typically consists of a data integrity term and a regularization term. Therefore, different regularization priors are used in IR algorithms. This paper will briefly look at the overall evolution of CT image reconstruction and the regularization priors used in IR algorithms. Finally, a discussion is presented based on the reality of various reconstruction methodologies at a glance to find the preferred one. Consequently, we will present anticipation towards future advancements in this domain.", acknowledgement = ack-nhfb, articleno = "2350026", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Zhiyu:2023:IIA, author = "Wang Zhiyu and Ding Weili and Wang Mingkui", title = "Illumination Invariance Adaptive Sidewalk Detection Based on Unsupervised Feature Learning", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "04", pages = "??--??", month = jul, year = "2023", DOI = "https://doi.org/10.1142/S0219467823500274", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Aug 5 16:18:20 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500274", abstract = "To solve the problem of road recognition when the robot is driving on the sidewalk, a novel sidewalk detection algorithm from the first-person perspective is proposed, which is crucial for robot navigation. The algorithm starts from the illumination invariance graph of the sidewalk image, and the sidewalk ``seeds'' are selected dynamically to get the sidewalk features for unsupervised feature learning. The final sidewalk region will be extracted by multi-threshold adaptive segmentation and connectivity processing. The key innovations of the proposed algorithm are the method of illumination invariance based on PCA and the unsupervised feature learning for sidewalk detection. With the PCA-based illumination invariance, it can calculate the lighting invariance angle dynamically to remove the impact of illumination and different brick colors' influence on sidewalk detection. Then the sidewalk features are selected dynamically using the parallel geometric structure of the sidewalk, and the confidence region of the sidewalk is obtained through unsupervised feature learning. The proposed method can effectively suppress the effects of shadows and different colored bricks in the sidewalk area. The experimental result proves that the F-measure of the proposed algorithm can reach 93.11\% and is at least 7.7\% higher than the existing algorithm.", acknowledgement = ack-nhfb, articleno = "2350027", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Chole:2023:LMO, author = "Vikrant Chole and Vijay Gadicha", title = "Locust Mayfly Optimization-Tuned Neural Network for {AI}-Based Pruning in Chess Game", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "04", pages = "??--??", month = jul, year = "2023", DOI = "https://doi.org/10.1142/S0219467823500286", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Aug 5 16:18:20 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500286", abstract = "The art of mimicking a human's responses and behavior in a programming machine is called Artificial intelligence (AI). AI has been incorporated in games in such a way to make them interesting, especially in chess games. This paper proposes a hybrid optimization tuned neural network (NN) to establish a winning strategy in the chess game by generating the possible next moves in the game. Initially, the images from Portable Game Notation (PGN) file are used to train the NN classifier. The proposed Locust Mayfly algorithm is utilized to optimally tune the weights of the NN classifier. The proposed Locust Mayfly algorithm inherits the characteristic features of hybrid survival and social interacting search agents. The NN classifier involves in finding all the possible moves in the board, among which the best move is obtained using the mini-max algorithm. At last, the performance of the proposed Locust mayfly-based NN method is evaluated with help of the performance metrics, such as specificity, accuracy, and sensitivity. The proposed Locust mayfly-based NN method attained a specificity of 98\%, accuracy of 98\%, and a sensitivity of 98\%, which demonstrates the productiveness of the proposed mayfly-based NN method in pruning.", acknowledgement = ack-nhfb, articleno = "2350028", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Pankaj:2023:NDO, author = "Pankaj and P. K. Bharti and Brajesh Kumar", title = "A New Design of Occlusion-Invariant Face Recognition Using Optimal Pattern Extraction and {CNN} with {GRU}-Based Architecture", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "04", pages = "??--??", month = jul, year = "2023", DOI = "https://doi.org/10.1142/S0219467823500298", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Aug 5 16:18:20 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500298", abstract = "Face detection is a computer technology being used in a variety of applications that identify human faces in digital images. In many face recognition challenges, Convolutional Neural Networks (CNNs) are regarded as a problem solver. Occlusion is determined as the most common challenge of face recognition in realistic applications. Several studies are undergoing to obtain face recognition without any challenges. However, the occurrence of noise and occlusion in the image reduces the achievement of face recognition. Hence, various researches and studies are carried out to solve the challenges involved with the occurrence of occlusion and noise in the image, and more clarification is needed to acquire high accuracy. Hence, a deep learning model is intended to be developed in this paper using the meta-heuristic approach. The proposed model covers four main steps: (a) data acquisition, (b) pre-processing, (c) pattern extraction and (d) classification. The benchmark datasets regarding the face image with occlusion are gathered from a public source. Further, the pre-processing of the images is performed by contrast enhancement and Gabor filtering. With these pre-processed images, pattern extraction is done by the optimal local mesh ternary pattern. Here, the hybrid Whale--Galactic Swarm Optimization (WGSO) algorithm is used for developing the optimal local mesh ternary pattern extraction. By inputting the pattern-extracted image, the new deep learning model namely ``CNN with Gated Recurrent Unit (GRU)'' network performs the recognition process to maximize the accuracy, and also it is used to enhance the face recognition model. From the results, in terms of accuracy, the proposed WGSO- CNN+GRU model is better by 4.02\%, 3.76\% and 2.17\% than the CNN, SVM and SRC, respectively. The experimental results are presented by performing their comparative analysis on a standard dataset, and they assure the efficiency of the proposed model. However, many challenging problems related to face recognition still exist, which offer excellent opportunities to face recognition researchers in the future.", acknowledgement = ack-nhfb, articleno = "2350029", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Rahul:2023:ESP, author = "Vaddadi Sai Rahul and M. Tejas and N. Narayanan Prasanth and S. P. Raja", title = "Early Success Prediction of {Indian} Movies Using Subtitles: a Document Vector Approach", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "04", pages = "??--??", month = jul, year = "2023", DOI = "https://doi.org/10.1142/S0219467823500304", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Aug 5 16:18:20 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500304", abstract = "Scientific studies of the elements that influence the box office performance of Indian films have generally concentrated on post-production elements, such as those discovered after a film has been completed or released, and notably for Bollywood films. Only fewer studies have looked at regional film industries and pre-production factors, which are elements that are known before a decision to greenlight a film is made. This study looked at Indian films using natural language processing and machine learning approaches to see if they would be profitable in the pre-production stage. We extract movie data and English subtitles (as an approximation to the screenplay) for the top five Indian regional film industries: Bollywood, Kollywood, Tollywood, Mollywood, and Sandalwood, as they make up a major portion of the Indian film industry's revenue. Subtitle Vector (Sub2Vec), a Paragraph Vector model trained on English subtitles, was used to embed subtitle text into 50 and 100 dimensions. The proposed approach followed a two-stage pipeline. In the first stage, Return on Investment (ROI) was calculated using aggregated subtitle embeddings and associated movie data. Classification models used the ROI calculated in the first step to predicting a film's verdict in the second step. The optimal regressor--classifier pair was determined by evaluating classification models using $ F_1$-score and Cohen's Kappa scores on various hyperparameters. When compared to benchmark methods, our proposed methodology forecasts box office success more accurately.", acknowledgement = ack-nhfb, articleno = "2350030", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Honnutagi:2023:UVE, author = "Pooja Honnutagi and Y. S. Laitha and V. D. Mytri", title = "Underwater Video Enhancement Using Manta Ray Foraging Lion Optimization-Based Fusion Convolutional Neural Network", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "04", pages = "??--??", month = jul, year = "2023", DOI = "https://doi.org/10.1142/S0219467823500316", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Aug 5 16:18:20 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500316", abstract = "Due to the significance of aquatic robotics and marine engineering, the underwater video enhancement has gained huge attention. Thus, a video enhancement method, namely Manta Ray Foraging Lion Optimization-based fusion Convolutional Neural Network (MRFLO-based fusion CNN) algorithm is developed in this research for enhancing the quality of the underwater videos. The MRFLO is developed by merging the Lion Optimization Algorithm (LOA) and Manta Ray Foraging Optimization (MRFO). The blur in the input video frame is detected and estimated through the Laplacian's variance method. The fusion CNN classifier is used for deblurring the frame by combining both the input frame and blur matrix. The fusion CNN classifier is tuned by the developed MRFLO algorithm. The pixel of the deblurred frame is enhanced using the Type II Fuzzy system and Cuckoo Search optimization algorithm filter (T2FCS filter). The developed MRFLO-based fusion CNN algorithm uses the metrics, Underwater Image Quality Measure (UIQM), Underwater Color Image Quality Evaluation (UCIQE), Structural Similarity Index Measure (SSIM), Mean Square Error (MSE), and Peak Signal-to-Noise Ratio (PSNR) for the evaluation by varying the blur intensity. The proposed MRFLO-based fusion CNN algorithm acquired a PSNR of 38.9118, SSIM of 0.9593, MSE of 3.2214, UIQM of 3.0041 and UCIQE of 0.7881.", acknowledgement = ack-nhfb, articleno = "2350031", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Sasikaladevi:2023:CBD, author = "N. Sasikaladevi and A. Revathi", title = "Certainty-Based Deep Fused Neural Network Using Transfer Learning and Adaptive Movement Estimation for the Diagnosis of Cardiomegaly", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "04", pages = "??--??", month = jul, year = "2023", DOI = "https://doi.org/10.1142/S021946782350033X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Aug 5 16:18:20 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S021946782350033X", abstract = "Cardiomegaly is a radiographic abnormality, and it has significant prognosis importance in the population. Chest X-ray images can identify it. Early detection of cardiomegaly reduces the risk of congestive heart failure and systolic dysfunction. Due to the lack of radiologists, there is a demand for the artificial intelligence tool for the early detection of cardiomegaly. The cardiomegaly X-ray dataset is extracted from the cheXpert database. Totally, 46195 X-ray records with a different view such as AP view, PA views, and lateral views are used to train and validate the proposed model. The artificial intelligence app named CardioXpert is constructed based on deep neural network. The transfer learning approach is adopted to increase the prediction metrics, and an optimized training method called adaptive movement estimation is used. Three different transfer learning-based deep neural networks named APNET, PANET, and LateralNET are constructed for each view of X-ray images. Finally, certainty-based fusion is performed to enrich the prediction accuracy, and it is named CardioXpert. As the proposed method is based on the largest cardiomegaly dataset, hold-out validation is performed to verify the prediction accuracy of the proposed model. An unseen dataset validates the model. These deep neural networks, APNET, PANET, and LateralNET, are individually validated, and then the fused network CardioXpert is validated. The proposed model CardioXpert provides an accuracy of 93.6\%, which is the highest at this time for this dataset. It also yields the highest sensitivity of 94.7\% and a precision of 97.7\%. These prediction metrics prove that the proposed model outperforms all the state-of-the-art deep transfer learning methods for diagnosing cardiomegaly thoracic disorder. The proposed deep learning neural network model is deployed as the web app. The cardiologist can use this prognostic app to predict cardiomegaly disease faster and more robust in the early state by using low-cost and chest X-ray images.", acknowledgement = ack-nhfb, articleno = "2350033", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Ainapure:2023:DEM, author = "Bharati S. Ainapure and Mythili Boopathi and Chandra Sekhar Kolli and C. Jackulin", title = "Deep Ensemble Model for Spam Classification in {Twitter} via Sentiment Extraction: Bio-Inspiration-Based Classification Model", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "04", pages = "??--??", month = jul, year = "2023", DOI = "https://doi.org/10.1142/S0219467823500341", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Aug 5 16:18:20 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500341", abstract = "Twitter Spam has turned out to be a significant predicament of these days. Current works concern on exploiting the machine learning models to detect the spams in Twitter by determining the statistic features of the tweets. Even though these models result in better success, it is hard to sustain the performances attained by the supervised approaches. This paper intends to introduce a deep learning-assisted spam classification model on twitter. This classification is based on sentiments and topics modeled in it. The initial step is data collection. Subsequently, the collected data are preprocessed with ``stop word removal, stemming and tokenization''. The next step is feature extraction, wherein, the post tagging, headwords, rule-based lexicon, word length, and weighted holoentropy features are extracted. Then, the proposed sentiment score extraction is carried out to analyze their variations in nonspam and spam information. At last, the diffusions of spam data on Twitter are classified into spam and nonspams. For this, an Optimized Deep Ensemble technique is introduced that encloses ``neural network (NN), support vector machine (SVM), random forest (RF) and convolutional neural network (DNN)''. Particularly, the weights of DNN are optimally tuned by an arithmetic crossover-based cat swarm optimization (AC-CS) model. At last, the supremacy of the developed approach is examined via evaluation over extant techniques. Accordingly, the proposed AC-CS + ensemble model attained better accuracy value when the learning percentage is 80, which is 18.1\%, 14.89\%, 11.7\%, 12.77\%, 10.64\%, 6.38\%, 6.38\%, and 6.38\% higher than SVM, DNN, RNN, DBN, MFO + ensemble model, WOA + ensemble model, EHO + ensemble model and CSO + ensemble model models.", acknowledgement = ack-nhfb, articleno = "2350034", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Lata:2023:DDL, author = "Navdeep Lata and Raman Kumar", title = "{DSIT}: a Dynamic Lightweight Cryptography Algorithm for Securing Image in {IoT} Communication", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "04", pages = "??--??", month = jul, year = "2023", DOI = "https://doi.org/10.1142/S0219467823500353", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Aug 5 16:18:20 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500353", abstract = "One of the most significant challenges appears to be securing the Internet of Things (IoT) communication network. As a corollary, information security has become the basis for establishing trustworthiness in IoT network communication. Cryptography is one of the ways for securing information in this case. However, the majority of current approaches are static, making them subject to security threats. As a consequence, a new concept, dynamic encryption, is growing rapidly in IoT communication. In this paper, a dynamic encryption algorithm (DSIT) has been proposed to secure IoT communication. This algorithm is based on Feistel and Substitution--Permutation Network. DSIT is a block cipher that takes the 64-bit block of plaintext, 64-bit secret key, and a secret dynamic box (D-box) as input. It produces a 64-bit ciphertext by performing eight rounds of the DSIT algorithm. For each round, the key and D-box are updated. This dynamic effect provides high security to a dynamic IoT network. The proposed algorithm has been executed in IoT environment using Raspberry Pi 3 Model B + and 50\% average Avalanche effect has been achieved. The proposed algorithm efficiently encrypts the image data to secure the communication and high resistant to cryptanalysis attacks.", acknowledgement = ack-nhfb, articleno = "2350035", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Dahiya:2023:RDL, author = "Neelam Dahiya and Sartajvir Singh and Sheifali Gupta", title = "A Review on Deep Learning Classifier for Hyperspectral Imaging", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "04", pages = "??--??", month = jul, year = "2023", DOI = "https://doi.org/10.1142/S0219467823500365", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Aug 5 16:18:20 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500365", abstract = "Nowadays, hyperspectral imaging (HSI) attracts the interest of many researchers in solving the remote sensing problems especially in various specific domains such as agriculture, snow/ice, object detection and environmental monitoring. In the previous literature, various attempts have been made to extract the critical information through hyperspectral imaging which is not possible through multispectral imaging (MSI). The classification in image processing is one of the important steps to categorize and label the pixels based on some specific rules. There are various supervised and unsupervised approaches which can be used for classification. Since the past decades, various classifiers have been developed and improved to meet the requirement of remote sensing researchers. However, each method has its own merits and demerits and is not applicable in all scenarios. Past literature also concluded that deep learning classifiers are more preferable as compared to machine learning classifiers due to various advantages such as lesser training time for model generation, handle complex data and lesser user intervention requirements. This paper aims to perform the review on various machine learning and deep learning-based classifiers for HSI classification along with challenges and remedial solution of deep learning with hyperspectral imaging. This work also highlights the various limitations of the classifiers which can be resolved with developments and incorporation of well-defined techniques.", acknowledgement = ack-nhfb, articleno = "2350036", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Birajdar:2023:SSP, author = "Gajanan K. Birajdar and Mukesh D. Patil", title = "A Systematic Survey on Photorealistic Computer Graphic and Photographic Image Discrimination", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "04", pages = "??--??", month = jul, year = "2023", DOI = "https://doi.org/10.1142/S0219467823500377", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Aug 5 16:18:20 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500377", abstract = "The advent in graphic rendering software and technological progress in hardware can generate or modify photorealistic computer graphic (CG) images that are difficult to identify by human observers. Computer-generated images are used in magazines, film and advertisement industry, medical and insurance agencies, social media, and law agencies as an information carrier. The forged computer-generated image created by the malicious user may distort social stability and impacts on public opinion. Hence, the precise identification of computer graphic and photographic image (PG) is a significant and challenging task. In the last two decades, several researchers have proposed different algorithms with impressive accuracy rate, including a recent addition of deep learning methods. This comprehensive survey presents techniques dealing with CG and PG image classification using machine learning and deep learning. In the beginning, broad classification of all the methods in to five categories is discussed in addition to generalized framework of CG detection. Subsequently, all the significant works are surveyed and are grouped into five types: image statistics methods, acquisition device properties-based techniques, color, texture, and geometry-based methods, hybrid methods, and deep learning methods. The advantages and limitations of CG detection methods are also presented. Finally, major challenges and future trends in the CG and PG image identification field are discussed.", acknowledgement = ack-nhfb, articleno = "2350037", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Ravikumar:2023:AMS, author = "S. Ravikumar and E. Kannan", title = "Analysis on Mental Stress of Professionals and Pregnant Women Using Machine Learning Techniques", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "05", pages = "??--??", month = sep, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467823500389", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Oct 13 07:20:29 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500389", abstract = "Stress is the way that everyone can respond actually, intellectually and sincerely to different conditions, changes and requests in our lives. Stress problems are a typical issue among working experts in the business today. With changing way of life and work societies, there is an expansion in the stress among the representatives. However, numerous ventures and corporate give emotional wellness-related plans and attempt to facilitate the work environment climate, the issue is a long way from control. When it comes to Pregnant Women, the uterus climate assumes a fundamental part in future development and improvement of hatchling. Stress during pregnancy will influence the sensitive climate of the hatchling. These can remember impacts for your unborn child's development and the length of incubation period. They can likewise expand the danger of issues in your child's future physical and mental turn of events, just as social issues in youth. By using various machine learning techniques, the proposed model can analyze the stress in a working professional and also in a pregnant woman. We can predict the best way of yoga to reduce their stress and get good work results from working employees and a good growth in fetus of a pregnant women. Yoga can positively affect the parasympathetic sensory system and helps in bringing down heartbeat and circulatory strain. This decreases the interest of the body for oxygen and furthermore increment lung limit. Compelling utilization of yoga can likewise decrease the odds of stress, nervousness and despondency.", acknowledgement = ack-nhfb, articleno = "2350038", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Sadaghiani:2023:IIB, author = "Abdol Vahab Khalili Sadaghiani and Samad Sheikhaei and Behjat Forouzandeh", title = "Image Interpolation Based on {$2$D-DWT} with Novel Regularity-Preserving Algorithm Using {RLS} Adaptive Filters", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "05", pages = "??--??", month = sep, year = "2023", DOI = "https://doi.org/10.1142/S0219467823500390", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Oct 13 07:20:29 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500390", abstract = "This paper proposes a novel method for the image interpolation problem based on two-dimensional discrete wavelet transform (DWT) with the edge preserving approach. The purpose of this method is to consider two contrasting issues of over-smoothing and creation of spurious edges at the same time, and offer a novel solution based on statistical dependencies of image sub-bands, and noise behavior. The offered method has a multi-faceted approach for the problem; by sub-band coding, it handles each 2D-DWT image sub-band with a different solution. For LH and HL sub-bands, two algorithms work together in order to preserve regularity. Area\_Check algorithm is a four-phase edge-preserving algorithm that aims to recognize and interpolate separating lines of environments and edgy regions in the best possible way. On the other hand, RLS\_AVG algorithm interpolates smooth surfaces of the image by keeping the regularity of the image without over-smoothing. In this regard, the offered algorithm has a great power to counter jaggies and annoying artifacts. In the end, in order to demonstrate the capability, and performance of the proposed method, the final results in various metrics are compared with the results of the most famous and the newest image interpolation methods.", acknowledgement = ack-nhfb, articleno = "2350039", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Gill:2023:NIG, author = "Jasmeen Gill and Ravinder Pal Singh", title = "Non-Invasive Grading and Sorting of Mango (\bioname{Mangiferad indica} {L.}) Using Antlion Optimizer-Based Artificial Neural Networks", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "05", pages = "??--??", month = sep, year = "2023", DOI = "https://doi.org/10.1142/S0219467823500407", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Oct 13 07:20:29 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500407", abstract = "Mango is an imperative commercial fruit in terms of market value and volume of production. In addition, it is grown in more than ninety nations around the globe. Consequently, the demand for effective grading and sorting has increased, ever since. This communication describes a non-invasive mango fruit grading and sorting model that utilizes hybrid soft computing approach. Artificial neural networks (ANN), optimized with Antlion optimizer (ALO), are used as a classification tool. The quality of mangoes is evaluated according to four grading parameters: size (volume and morphology), maturity (ripe/unripe), defect (defective/healthy) and variety (cultivar). Besides, a comparison of proposed grading system with state-of-the-art models is performed. The system showed an overall classification rate of 95.8\% and outperformed the other models. Results demonstrate the effectiveness of proposed model in fruit grading and sorting applications.", acknowledgement = ack-nhfb, articleno = "2350040", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Padmavathi:2023:WCE, author = "P. Padmavathi and J. Harikiran", title = "Wireless Capsule Endoscopy Infected Images Detection and Classification Using {MobileNetV2-BiLSTM} Model", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "05", pages = "??--??", month = sep, year = "2023", DOI = "https://doi.org/10.1142/S0219467823500419", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Oct 13 07:20:29 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500419", abstract = "An efficient tool to execute painless imaging and examine gastrointestinal tract illnesses of the intestine is also known as wireless capsule endoscopy (WCE). Performance, safety, tolerance, and efficacy are the several concerns that make adaptation challenging and wide applicability. In addition, to detect abnormalities, the great importance is the automatic analysis of the WCE dataset. These issues are resolved by numerous vision-based and computer-aided solutions. But, they want further enhancements and do not give the accuracy at the desired level. In order to solve these issues, this paper presents the detection and classification of WCE infected images by a deep neural network and utilizes a bleed image recognizer (BIR) that associates the MobileNetV2 design to classify the images of WCE infected. For the opening-level evaluation, the BIR uses the MobileNetV2 model for its minimum computation power necessity, and then the outcome is sent to the CNN for more processing. Then, Bi-LSTM with an attention mechanism is used to improve the performance level of the model. Hybrid attention Bi-LSTM design yields more accurate classification outcomes. The proposed scheme is implemented in the Python platform and the performance is evaluated by Cohen's kappa, F1-score, recall, accuracy, and precision. The implementation outcomes show that the introduced scheme achieved maximum accuracy of 0.996 with data augmentation with the dataset of WCE images which provided higher outcomes than the others.", acknowledgement = ack-nhfb, articleno = "2350041", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Rajkumar:2023:DLF, author = "Rajeev Rajkumar", title = "Deep Learning Feature Extraction Using Attention-Based {DenseNet 121} for Copy Move Forgery Detection", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "05", pages = "??--??", month = sep, year = "2023", DOI = "https://doi.org/10.1142/S0219467823500420", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Oct 13 07:20:29 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500420", abstract = "In modern society, digital images can be far-reaching, and the images are manipulated by various software and hardware technologies. The image forgery activities are undertaken by the attackers mainly for damaging the reputation of people or receiving fiscal gain, etc. Taking this into consideration, many techniques are developed to detect the forged images. In this paper, a new deep learning-based approach is introduced for copy-move forgery detection. The input images are segmented into non-overlapping patches using superpixel-based modified dense peak clustering and the features are extracted from the segmented patches by applying deep learning structure of attention-based DenseNet 121 model. Besides, to compare every block, the depth of each pixel is reconstructed, and eventually matching process is carried out using the adaptive chimp patch matching approach, which detects the suspicious forged regions in an image. Finally, the matched keypoints are merged with the segmented patches using the merged keypoint matching algorithm. As a result, the new deep learning approach has detected the forged regions efficiently from the tampered image with less time even the image is compressed, rotated, or scaled. The performance is evaluated in terms of recall, precision, accuracy, F1-score, computational time, and False Positive Rate (FPR). Moreover, the performance is compared with the other existing approaches, and the outcomes showed that the proposed method has achieved higher accuracy of 97\%, recall of 99\%, precision of 97.84\%, F1-score of 98.81\%, FPR of 0.022 and less computational time of 2.5 s.", acknowledgement = ack-nhfb, articleno = "2350042", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kaur:2023:FIB, author = "Rajdeep Kaur and Rakesh Kumar and Meenu Gupta", title = "Food Image-based Nutritional Management System to Overcome Polycystic Ovary Syndrome using {DeepLearning}: A Systematic Review", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "05", pages = "??--??", month = sep, year = "2023", DOI = "https://doi.org/10.1142/S0219467823500432", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Oct 13 07:20:29 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500432", abstract = "Polycystic Ovary Syndrome (PCOS) is one of the growing non-communicable diseases in those women who do not take proper nutrients in their meals. Medically, it is not proven that an unhealthy diet is the only cause of PCOS, but it is one of the major causes behind this disease. PCOS is an endocrine disorder that influences 8--10\% of women at their reproductive age and may cause infertility or other health problems. Deep Learning (DL) is a popular technique to classify the food images for identifying the nutrients in the food. This work considers food image datasets (FOOD-101, UEC-256, UEC-100, etc.) to analyze the food image using pre-trained Convolutional Neural Network (CNN) and a nutritional information dataset for identifying the nutrients in food. The proposed study aims to find the solution to overcome the PCOS problem in women by tracking nutrient intake using food images and recommending the diet. Further, this study will also provide comprehensive review of image classification and recommendation techniques that may help the dieticians to track the nutrient intake using food images provided by PCOS patients to overcome the disease.", acknowledgement = ack-nhfb, articleno = "2350043", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Nnolim:2023:FOP, author = "Uche A. Nnolim", title = "Fourth-Order Partial Differential Equation Framelet Fusion-Based Colour Correction and Contrast Enhancement for Underwater Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "05", pages = "??--??", month = sep, year = "2023", DOI = "https://doi.org/10.1142/S0219467823500444", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Oct 13 07:20:29 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500444", abstract = "A framelet augmented fourth-order forward-reverse partial differential equation (PDE)-fusion-based algorithm is proposed for underwater image enhancement. The algorithm combines framelet domain transform-based fusion of modified base, detail and amplified detail layers in a PDE-based formulation. The extracted layers via framelet decomposition with adaptive threshold computation comprise the detail and approximation components of the images, which are amplified, attenuated and aggregated. Additions include a modified global contrast enhancement/color correction function and a suitable color space transformation to enhance difficult underwater images with flat non-overlapping color channel histograms. Also, gradient domain fusion of several color corrected image layers and fuzzy rule-based enhancement is combined in the proposed PDE-based fusion framework. Furthermore, variational illumination correction was also employed for better enhancement of dark underwater images. Experimental comparisons indicate that the proposed approaches yield better overall visual and numerical results in most cases when compared with state-of-the-art methods.", acknowledgement = ack-nhfb, articleno = "2350044", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Gavade:2023:HFD, author = "Priyanka A. Gavade and Vandana S. Bhat and and Jagadeesh Pujari", title = "Hybrid Features and Deep Learning Model for Facial Expression Recognition From Videos", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "05", pages = "??--??", month = sep, year = "2023", DOI = "https://doi.org/10.1142/S0219467823500456", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Oct 13 07:20:29 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500456", abstract = "Facial expression recognition plays a crucial function in the advancement of technologies that can be used in detecting mental illness, sensors, and a wide variety of applications. Facial expression recognition is an interesting as well as strenuous task in digital field due to the complexity of the varying individuals. The intention of this work is to establish a face recognition model relying upon the modified GWO-based ensemble deep convolutional neural network (DCNN), which effectively recognizes the expressions. The substance of the research anticipates on the proposed modified GWO optimization which helps in maintaining the storage capacity with simple structures and provides high convergence. Enabling the optimization in the ensemble DCNN helps in tuning the internal parameters present in the classifier as well as helps in attaining best solution. The accomplishment of the proposed expression recognition model is evaluated utilizing the parameter metrics accuracy, precision, and recall that attained the values of 94.114\%, 92.003\%, and 95.734\% which is more efficient.", acknowledgement = ack-nhfb, articleno = "2350045", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Vishnuvardhan:2023:MIF, author = "Veruva Vishnuvardhan and T. Jaya", title = "Medical Image Fusion using {ECNN}- and {OMBO}-based Adaptive Weighted Fusion Rule", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "05", pages = "??--??", month = sep, year = "2023", DOI = "https://doi.org/10.1142/S0219467823500468", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Oct 13 07:20:29 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500468", abstract = "Medical imaging and information processing technologies are constantly evolving, resulting in a wide range of multimodality therapeutic pictures for clinical illness investigation. Physicians often require medical images produced using various modalities such as computed tomography (CT), magnetic resonance (MR), and positron emission computed tomography (PET) for clinical diagnosis. Many deep learning-based fusion methods have recently been proposed. In Convolutional Neural Network (CNN)-based fusion methods, only the last layer results are used as the image features, which result in the loss of useful information at middle layers. The fusion rule, based on the weighted averaging, causes noises in the source images and suppresses salient features of the image. In order to solve these issues, this paper proposes medical image fusion using Enhanced CNN (ECNN)- and Opposition-based Monarch Butterfly Optimization (OMBO)-based adaptive weighted fusion rule (AWFR). The ECNN contains feature extraction and reconstruction components. Both these components are trained in order to minimize the pixel loss and structural similarity loss. A pair of multimodal medical image is passed as input to the ECNN model to extract the low level and high level features. For the extracted features from ECNN, weighted fusion rule is applied in which OMBO algorithm is applied to adaptively optimize the weights of the fusion rule.", acknowledgement = ack-nhfb, articleno = "2350046", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Jameel:2023:BAI, author = "Samer Kais Jameel and Jafar Majidpour", title = "{BCS-AE}: Integrated Image Compression-Encryption Model Based on {AE} and {Block-CS}", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "05", pages = "??--??", month = sep, year = "2023", DOI = "https://doi.org/10.1142/S021946782350047X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Oct 13 07:20:29 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S021946782350047X", abstract = "For Compressive Sensing problems, a number of techniques have been introduced, including traditional compressed-sensing (CS) image reconstruction and Deep Neural Network (DNN) models. Unfortunately, due to low sampling rates, the quality of image reconstruction is still poor. This paper proposes a lossy image compression model (i.e. BCS-AE), which combines two different types to produce a model that uses more high-quality low-bitrate CS reconstruction. Initially, block-based compressed sensing (BCS) was utilized, and it was done one block at a time by the same operator. It can correctly extract images with complex geometric configurations. Second, we create an AutoEncoder architecture to replace traditional transforms, and we train it with a rate-distortion loss function. The proposed model is trained and then tested on the CelebA and Kodak databases. According to the results, advanced deep learning-based and iterative optimization-based algorithms perform better in terms of compression ratio and reconstruction quality.", acknowledgement = ack-nhfb, articleno = "2350047", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Preetham:2023:SMR, author = "Anusha Preetham and Vishnu Vardhan Battu", title = "Soil Moisture Retrieval Using Sail Squirrel Search Optimization-based Deep Convolutional Neural Network with {Sentinel-1} Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "05", pages = "??--??", month = sep, year = "2023", DOI = "https://doi.org/10.1142/S0219467823500481", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Oct 13 07:20:29 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500481", abstract = "Soil Moisture (SM) is an environmental descriptor, which acts as the affiliation between the atmosphere and the earth's surface. Various SM retrieval methods are developed to abolish the influence of vegetation cover attenuation, surface roughness, and scattering to find an association among SM and backscatter coefficient. To understand the relationship between various vegetation parameters and backscatter coefficient poses a great challenge in SM retrieval. Hence, an efficacious SM retrieval method is afforded using the proposed Sail Squirrel Search Optimization-based Deep Convolutional Neural Network (SSSO-based Deep CNN). Here, the proposed SSSO is derived by concatenating the Sail Fish Optimization (SFO) with Squirrel Search Algorithm (SSA). The Deep CNN performs the process of SM retrieval using vegetation indices. The fitness measure of the proposed optimization enables to find the best solution to update the weights of the classifier for increasing the efficiency of the retrieval mechanism. By training Deep CNN with the proposed optimization, the soil moisture of an area is effectively retrieved. However, the proposed SSSO-based Deep CNN obtained minimal estimation error and minimal RMSE of 0.550 and 0.726 using sentinel-1 data, respectively.", acknowledgement = ack-nhfb, articleno = "2350048", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Gantenapalli:2023:SMF, author = "Srinivasa Rao Gantenapalli and Praveen Babu Choppala and James Stephen Meka", title = "Selective Mean Filtering for Reducing Impulse Noise in Digital Color Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "05", pages = "??--??", month = sep, year = "2023", DOI = "https://doi.org/10.1142/S0219467823500493", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Oct 13 07:20:29 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500493", abstract = "The interest of this paper is in reduction of impulse noise in digital color images. The two main methods used for noise reduction in images are the mean and median filters. These techniques operate by replacing the test pixel in a chosen window by a new filtered pixel value. The window is made to iteratively slide across the entire image to reconstruct a new noise reduced image. The mean filters suffer from the effect of smoothing out color contrast and edges due to leveraging the unrepresentative pixels in the filtering process. The vector median filter and its variants overcome this problem by considering only the most representative pixel in the chosen window. The most representative pixel, i.e. the pixel that is of highest conformity to take the place of the test pixel, is determined by minimizing the aggregate distance from one pixel to every other pixel in the window. The problem in these median filtering approaches is that only one pixel is treated as representative of all the pixels in the chosen window. This conjecture could lead to information loss due to marginalizing other pixels that also are representative of the center pixel. In this paper, we propose a selective mean filtering process to overcome the said problem. The key idea here is to determine the most representative pixels in the window using the method of aggregate distances and then compute the mean of these pixels. This approach will perform better than the vector median filters as now a set of representative pixels are leveraged into the filtering process. Simulation results show that the proposed method performs better than the conventional vector median filtering methods in terms of noise reduction and structural similarity and thus validates the proposed approach. Moreover, the method is tested on real MRI scan images in successfully reducing impulse noise for improved medical diagnosis.", acknowledgement = ack-nhfb, articleno = "2350049", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Wang:2023:TIN, author = "Xin Wang and Xiaogang Dong", title = "Time Image De-Noising Method Based on Sparse Regularization", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "05", pages = "??--??", month = sep, year = "2023", DOI = "https://doi.org/10.1142/S0219467825500093", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Fri Oct 13 07:20:29 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500093", abstract = "The blurring of texture edges often occurs during image data transmission and acquisition. To ensure the detailed clarity of the drag-time images, we propose a time image de-noising method based on sparse regularization. First, the image pixel sparsity index is set, and then an image de-noising model is established based on sparse regularization processing to obtain the neighborhood weights of similar image blocks. Second, a time image de-noising algorithm is designed to determine whether the coding coefficient reaches the standard value, and a new image de-noising method is obtained. Finally, the images of electronic clocks and mechanical clocks are used as two kinds of time images to compare different image de-noising methods, respectively. The results show that the sparsity regularization method has the highest peak signal-to-noise ratio among the six compared methods for different noise standard deviations and two time images. The image structure similarity is always above which shows that the proposed method is better than the other five image de-noising methods.", acknowledgement = ack-nhfb, articleno = "2550009", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Salman:2023:FCI, author = "Khalid A. Salman and Khalid Shaker and Sufyan Al-janabi", title = "Fake Colorized Image Detection Approaches: a Review", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "06", pages = "??--??", month = nov, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1142/S021946782350050X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu May 23 07:14:55 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S021946782350050X", abstract = "Colorization is a process used in image editing in which grayscale images are colored with realistic colors. Modern techniques of colorization could produce artfully colored images in such a way that it is difficult for human eyes to differentiate between actual and fake colorized images. As a result, identifying fraudulent colored pictures has captured the scientific community's attention in digital forensics. This paper provides an overview of the strategies used for detecting fake colorized images. Mainly, two approaches were used to design fake colorized image detection systems. The first one uses traditional machine learning (ML) techniques that rely on hand-crafted features derived from images and used to differentiate actual and fake images. The second approach uses deep learning (DL) techniques as ``end to end'' systems that don't have to be supplied with such hand-crafted features, as they can learn the features from the image directly. This paper focuses on the various methods and techniques used in fake-colorized image detection. It may aid researchers in better understanding the benefits and drawbacks of existing technologies to develop more efficient systems in this field.", acknowledgement = ack-nhfb, articleno = "2350050", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Singh:2023:RML, author = "Shaminder Singh and Anuj Kumar Gupta and Tanvi Arora", title = "A Review of Machine Learning-Based Recognition of Sign Language", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "06", pages = "??--??", month = nov, year = "2023", DOI = "https://doi.org/10.1142/S0219467823500511", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu May 23 07:14:55 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500511", abstract = "Some people in society have impaired cognitive senses like speech and hearing where they cannot behave like normal people. It is quite a complex task for abnormal people to understand as well as recognize the gestures of normal people. This initiates to delve into the study of review of Sign Language Recognition (SLR), in specific to, machine learning techniques. In this work, a review of machine learning techniques based on SLR were portrayed. Several studies related to ML papers have been collected and discussed with their merits and demerits. Thus, the observation dictates that recognition of hand gesture is still a challenging task. There are two sorts of gesture recognition, namely, static and dynamic gesture recognition. Static gesture recognition is developed from the dynamic gesture recognition. Almost, Convolutional Neural Networks (CNNs), Hidden Markov Models (HMM) and Histogram analysis were used as recognition classifiers for sign language. Dynamic gesture recognition process operates on tracking the centroid of hand gesture. It changes the visual information in time basis. Henceforth, study on dynamic gesture recognition needs to be more focused using Machine learning techniques. Comparative analysis is done in perspectives of significance of segmentation models, feature extraction and vision-based approaches.", acknowledgement = ack-nhfb, articleno = "2350051", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Sharma:2023:VAM, author = "Tejpal Sharma and Dhavleesh Rattan", title = "Visualizing {Android} Malicious Applications Using Texture Features", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "06", pages = "??--??", month = nov, year = "2023", DOI = "https://doi.org/10.1142/S0219467823500523", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu May 23 07:14:55 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500523", abstract = "Context: Due to the change and advancement in technology, day by day the internet service usages are also increasing. Smartphones have become the necessity for every person these days. It is used to perform all basic daily activities such as calling, SMS, banking, gaming, entertainment, education, etc. Therefore, malware authors are developing new variants of malwares or malicious applications especially for monetary benefits. Objective: Objective of this research paper is to develop a technique that can be used to detect malwares or malicious applications on the android devices that will work for all types of packed or encrypted malicious applications, which usually evade decompiling tools. Method: In the proposed approach, visualization method is used for the detection of malware. In the first phase, application files are converted into images and then in second phase, texture feature of images are extracted using Grey Level Co-occurrence Matrix (GLCM). In the last phase, machine learning classification algorithms are used to classify the malicious and benign applications. Results: The proposed approach is run on different datasets collected from various repositories. Different efficiency parameters are calculated and the proposed approach is compared with the existing approaches. Conclusion: We have proposed a static technique for efficient detection of malwares. The proposed technique performs better than the existing technique.", acknowledgement = ack-nhfb, articleno = "2350052", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Wankhade:2023:ONN, author = "Megha M. Wankhade and Suvarna S. Chorage", title = "Optimized Neural Network with Refined Features for Categorization of Motor Imaginary Signals", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "06", pages = "??--??", month = nov, year = "2023", DOI = "https://doi.org/10.1142/S0219467823500535", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu May 23 07:14:55 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500535", abstract = "Motor imaginary (MI) is an attractive research field in the brain--computer interfaces (BCIs) function, in which the system is directed by the imaginary arm movement of the subject. This attention is due to the monstrous potential for its pertinence in neurorestoration, neuroprosthetics, and gaming, where the client's considerations of envisioned developments should be decoded. An electroencephalography (EEG) device is regularly utilized for monitoring frontal cortex movements in BCI frameworks. The EEG signals are perceived through the two fundamental processes such as feature extraction and characterization process. This research concentrates on developing a predominant MI categorization model utilizing deep learning techniques. The prominence of this research relies on the combined features + proposed PROA-based RideNN process known as holo-entropy-based WPD, which extracts the most dominant feature from the EEG signals. The extracted features enhance the performance of the RideNN classifier. The analysis is done by utilizing the BCI Competition-IV-2a, -2b, and GigaScience datasets with respect to performance parameters, such as specificity, accuracy, and sensitivity. The analysis revealed the effective performance of the proposed method with respect to the existing state-of-art methodologies.", acknowledgement = ack-nhfb, articleno = "2350053", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Yechuri:2023:GAB, author = "Sivaramakrishna Yechuri and Sunny Dayal Vanabathina", title = "Genetic Algorithm-Based Adaptive {Wiener} Gain for Speech Enhancement Using an Iterative Posterior {NMF}", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "06", pages = "??--??", month = nov, year = "2023", DOI = "https://doi.org/10.1142/S0219467823500547", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu May 23 07:14:55 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500547", abstract = "In this paper, we propose a genetic algorithm-based adaptive Wiener gain for speech enhancement using an iterative posterior non-negative matrix factorization (NMF). In the recent past, NMF-based Wiener filtering methods were used to improve the performance of speech enhancement, which has shown that they provide better performance when compared with conventional NMF methods. But performance degrades in non-stationary noise environments. Template-based approaches are more robust and perform better in non-stationary noise environments compared to statistical model-based approaches but are dependent on {\em a priori\/} information. Combining the approaches avoids the drawbacks of both. To improve the performance further, speech and noise bases are adapted simultaneously in the NMF approach. The usage of Super-Gaussian constraints in iterative NMF still improves the performance in non-stationary noise. The silence frame is a challenging task in the case of NMF; still there will be some amount of noise present in those frames. For further enhancement, we have combined with a genetic algorithm (GA)-based adaptive Wiener filter which performs well in denoising and also the GA search the adaptive {\alpha} `` role=''presentation``{$>$} {\textalpha} {\textalpha} {\textalpha} allows us to control the trade-off between fitting the observed spectrogram of mixed speech and noise achieving high likelihood under our prior model. The proposed method outperforms other benchmark algorithms in terms of the source to distortion ratio (SDR), short-time objective intelligibility (STOI), and perceptual evaluation of speech quality (PESQ).", acknowledgement = ack-nhfb, articleno = "2350054", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Jia:2023:RTM, author = "Baojian Jia and Jie Ren", title = "Real-time Multi-person Pose Tracking Method Using Deep Reinforcement Learning", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "06", pages = "??--??", month = nov, year = "2023", DOI = "https://doi.org/10.1142/S0219467823500559", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu May 23 07:14:55 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500559", abstract = "To address the problem of low tracking accuracy caused by many recognized objects in the existing methods, we propose a real-time multi-person pose tracking method using deep reinforcement learning. First, the convolutional neural network (CNN) is used to predict the human key points and center vector in grid mode, make the human key points point to the human center according to the center vector, group the human key points according to the distance from the human key points to the human center, complete the multi-person pose estimation, and obtain the human pose sequence diagram. Then, the human pose sequence diagram is input into the deep reinforcement learning network, and the pose label and category label are output by the supervised learning and training stage. The best pose tracking strategy obtained in the reinforcement learning and training stage is applied to online tracking. Finally, CNN is used to predict the rectangular frame position of the pose instead of the target pose, and the tracking is completed when the pose stops. At this time, the rectangular frame position is the result of multi-person pose tracking. The results show that the maximum expected average overlap (EAO) of the proposed method is 0.53. When the root mean square error (RMSE) of the position component threshold reaches 8, the accuracy has been stable at 0.98\%. Therefore, the proposed method has high tracking accuracy. In the future, it can be applied to smart home scenarios to realize smart home human pose tracking, effectively identify human dangerous pose and ensure residents' life safety.", acknowledgement = ack-nhfb, articleno = "2350055", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Sukanya:2023:DLB, author = "S. T. Sukanya and S. Jerine", title = "Deep Learning-Based Melanoma Detection with Optimized Features via Hybrid Algorithm", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "06", pages = "??--??", month = nov, year = "2023", DOI = "https://doi.org/10.1142/S0219467823500560", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu May 23 07:14:55 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500560", abstract = "Recently, there had been a massive group of people, who were being rapidly affected by melanoma. Melanoma is a form of skin cancer that develops on the skin's surface layer. This is primarily caused due to excessive skin exposure to UV radiation and severe sunburns. Thus, the early detection of melanoma can aid us to cure it completely. This paper intends to introduce a new melanoma detection framework with four main phases {\em viz.\/} segmentation, feature extraction, optimal feature selection, as well as detection. Initially, the segmentation process takes place to the input skin image {\em via\/} Fuzzy C-Means Clustering (FCM) approach. From the segmented image $ I m_{\rm seg} $ (Imseg), some of the features such as Gray Level Run Length Matrix (GLRM), Local Vector Pattern (LVP), Local Binary Pattern (LBP), Local Directional Pattern (LDP) and Local Tetra Pattern (LTrP) are extracted. As the extracted features $F$ (F) suffered from the issue of ``curse of dimensionality'', this paper utilizes optimization to select optimal features, which makes the detection more precise. As a novelty, a new hybrid algorithm Particle-Assisted Moth Search Algorithm (PA-MSA) is introduced that hybridizes the concept of Moth Search Algorithm (MSA) and Particle Swarm Optimization (PSO), respectively. For the classification process, the optimally chosen features $ F_{\rm opt}$ (Fopt) are fed as input, where Deep Convolution Neural Network (DCNN) is used. Finally, a performance-based comparative analysis is conducted among the proposed PA-MSA as well as the existing models with respect to various measures.", acknowledgement = ack-nhfb, articleno = "2350056", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Padate:2023:WAO, author = "Roshni Padate and Amit Jain and Mukesh Kalla and and Arvind Sharma", title = "A Widespread Assessment and Open Issues on Image Captioning Models", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "06", pages = "??--??", month = nov, year = "2023", DOI = "https://doi.org/10.1142/S0219467823500572", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu May 23 07:14:55 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500572", abstract = "Automated generation of image captions is a demanding AI crisis as it necessitates the exploitation of numerous methods from diverse computer science fields. Deep learning (DL) approaches have revealed marvelous results in a lot of diverse appliances. On the other hand, data augmentation in DL that imitates the quantity and the variety of training data without the need of gathering additional data is a hopeful area in machine learning (ML). Producing textual descriptions for a specified image is a demanding task using the computer. This survey makes a critical analysis of about 65 papers regarding image captioning. More particularly, varied performance measures that are contributed in diverse articles are analyzed. In addition, a comprehensive study is made regarding the maximal performances and varied features deployed in each work. Moreover, chronological analysis and dataset analysis are done and finally, the survey extends with the determination of varied research challenges, which might be productive for the analysts to endorse enhanced upcoming works on image captioning.", acknowledgement = ack-nhfb, articleno = "2350057", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Patel:2023:DLE, author = "Miral Jerambhai Patel and Ashish M. Kothari", title = "Deep Learning-Enabled Road Segmentation and Edge-Centerline Extraction from High-Resolution Remote Sensing Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "06", pages = "??--??", month = nov, year = "2023", DOI = "https://doi.org/10.1142/S0219467823500584", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu May 23 07:14:55 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500584", abstract = "Nowadays, precise and up-to-date maps of road are of great significance in an extensive series of applications. However, it automatically extracts the road surfaces from high-resolution remote sensed images which will remain as a demanding issue owing to the occlusion of buildings, trees, and intricate backgrounds. In order to address these issues, a robust Gradient Descent Sea Lion Optimization-based U-Net (GDSLO-based U-Net) is developed in this research work for road outward extraction from High Resolution (HR) sensing images. The developed GDSLO algorithm is newly devised by the incorporation of Stochastic Gradient Descent (SGD) and Sea Lion Optimization Algorithm (SLnO) algorithm. Input image is pre-processed and U-Net is employed in road segmentation phase for extracting the road surfaces. Meanwhile, training data of U-Net has to be done by using the GDSLO optimization algorithm. Once road segmentation is done, road edge detection and road centerline detection is performed using Fully Convolutional Network (FCN). However, the developed GDSLO-based U-Net method achieved superior performance by containing the estimation criteria, including precision, recall, and F1-measure through highest rate of 0.887, 0.930, and 0.809, respectively.", acknowledgement = ack-nhfb, articleno = "2350058", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Sajitha:2023:AVV, author = "A. S. Sajitha and S. Sridevi Sathya Priya", title = "Analysis of Various Visual Cryptographic Techniques and their Issues Based on Optimization Algorithms", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "06", pages = "??--??", month = nov, year = "2023", DOI = "https://doi.org/10.1142/S0219467823500596", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu May 23 07:14:55 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500596", abstract = "Visual Cryptography (VC) is a process employed for the maintenance of secret information by hiding the secret messages that are embedded within the images. Typically, an image is partitioned into a number of shares that are stacked over one another in order to reconstruct back the original image accurately. The major limitation that existed in the traditional VC techniques is pixel expansion, in which pixel expansion is replaced with a number of sub-pixels in individual share, which causes a considerable impact on the contrast and resolution of the image that further gradually decreases the quality of the image. VC is named for its essential characteristics, such as transmitting the images with two or more shares with an equal number of black pixels and color pixel distribution. The secret message can be decrypted using Human Visual System (HVS). In this paper, 50 research papers are reviewed based on various classification algorithms, which are effectively used for the VC technique. The classification algorithms are categorized into three types, namely, meta-heuristic, heuristic, and evolutionary, and the research issues and challenges confronted by the existing techniques are reported in this survey. Moreover, the analysis is done based on the existing research works by considering the classification algorithms, tools, and evaluation metrics.", acknowledgement = ack-nhfb, articleno = "2350059", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Mustafa:2023:QDD, author = "Adnan A. Mustafa", title = "Quick Dissimilarity Detection for Center-Based Binary Images Via Smart Mapping", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "06", pages = "??--??", month = nov, year = "2023", DOI = "https://doi.org/10.1142/S0219467823500602", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu May 23 07:14:55 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500602", abstract = "In this paper, we present three different smart mapping schemes that improve on the quickness of dissimilarity detection between images. We call the mapping schemes {\em smart\/} because the mapping order is setup intelligently to detect dissimilarity quickly by concentrating its search near the center of the images, which is usually the region of interest in a given scene. Thus, smart mapping is well suited for images when the differences between them are expected to be concentrated near the center of the image. We construct a mapping vector (MV) that contains an ordered list of point mappings which is employed to map points between images in an efficient manner. The focus in this paper is on applying the three different smart mapping schemes to binary images. Furthermore, we test three different mapping densities with each smart mapping scheme and analyze the results. Tests are conducted on two image sets and dissimilarity detection results are compared to results obtained via random mapping, which had been shown to be extremely fast, as predicted by the probabilistic matching model for binary images (PMMBI). We show that by employing smart mapping a great improvement in dissimilarity detection quickness is possible when dissimilarity between images is concentrated near the center of the scene.", acknowledgement = ack-nhfb, articleno = "2350060", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Sahu:2023:SDR, author = "Geeta Abakash Sahu and Manoj Hudnurkar", title = "Sarcasm Detection: a Review, Synthesis and Future Research Agenda", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "06", pages = "??--??", month = nov, year = "2023", DOI = "https://doi.org/10.1142/S0219467823500614", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu May 23 07:14:55 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500614", abstract = "A literature review on sarcasm detection has been undergone in this research work. To have a meaningful study about the existing works on sarcasm detection, a total of 65 research papers have been analyzed in diverse aspects like the datasets utilized, language, pre-processing technique, type of features, feature extraction technique, machine learning/deep learning-based sarcasm classification. All these papers belong to diverse international as well as national journals. Moreover, the performance of each work in terms of accuracy, {\em F\/} -score and recall will also be manifested. To show the superiority of the works, a comparative evaluation has been undergone in terms of analyzed performances of each of the works. Finally, the works that hold the superior or improved values are furnished. In addition, the current challenges faced by the sarcasm detection system are portrayed, and this will be a milestone for future researchers.", acknowledgement = ack-nhfb, articleno = "2350061", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Liu:2023:PIR, author = "Yuan Liu", title = "Product Image Recommendation with Transformer Model Using Deep Reinforcement Learning", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "06", pages = "??--??", month = nov, year = "2023", DOI = "https://doi.org/10.1142/S0219467825500202", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu May 23 07:14:55 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500202", abstract = "A product image recommendation algorithm with transformer model using deep reinforcement learning is proposed. First, the product image recommendation architecture is designed to collect users' historical product image clicking behaviors through the log information layer. The recommendation strategy layer uses collaborative filtering algorithm to calculate users' long-term shopping interest and gated recurrent unit to calculate users' short-term shopping interest, and predicts users' long-term and short-term interest output based on users' positive and negative feedback sequences. Second, the prediction results are fed into the transformer model for content planning to make the data format more suitable for subsequent content recommendation. Finally, the planning results of the transformer model are input to Deep Q-Leaning Network to obtain product image recommendation sequences under the learning of this network, and the results are transmitted to the data result layer, and finally presented to users through the presentation layer. The results show that the recommendation results of the proposed algorithm are consistent with the user's browsing records. The average accuracy of product image recommendation is 97.1\%, the maximum recommended time is 1.0$s$ the coverage and satisfaction are high, and the practical application effect is good. It can recommend more suitable products for users and promote the further development of e-commerce.", acknowledgement = ack-nhfb, articleno = "2550020", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Anonymous:2023:AIV, author = "Anonymous", title = "Author Index (Volume 23)", journal = j-INT-J-IMAGE-GRAPHICS, volume = "23", number = "06", pages = "??--??", month = nov, year = "2023", DOI = "https://doi.org/10.1142/S0219467823990012", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu May 23 07:14:55 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823990012", acknowledgement = ack-nhfb, articleno = "2399001", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kamble:2024:VUM, author = "Tanaji Umaji Kamble and Shrinivas Padmakar Mahajan", title = "{$3$D} Vision Using Multiple Structured Light-Based {Kinect} Depth Cameras", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "01", pages = "??--??", month = jan, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467824500013", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu May 23 07:14:57 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500013", abstract = "Real-time 3D scanning of a scene or object using multiple depth cameras is often required in many applications but is still a challenging task for the computer vision community, especially when the object or scene is partially occluded and dynamic. If active depth sensors are used in this case, their resulting depth map quality gets degraded due to interference between active radiations from each depth sensor. Passive 3D sensors like stereo cameras can avoid the issue of interference as they do not emit any active radiation, but they face correspondence problems. Since releasing the commodity depth sensor Microsoft Kinect, researchers are getting more interested in active depth-sensing. However, Kinect sensors have some easily noticeable limitations concerning 3D reconstruction such as: they can provide depth maps for a limited range, their field of view is restricted and holes are observed in the depth map due to occlusion. The above-mentioned limitations can be overcome if multiple Kinect sensors are used simultaneously instead of a single Kinect sensor. Still, the challenge here is to avoid interference between these sensors. We present a comprehensive review of possible solutions to avoid interference between multiple Kinect sensors. Furthermore, we introduce the Kinect technology in detail along with applications where multiple Kinect sensors are used in the literature. We expect that this paper will be helpful to the researchers who want to use multiple Kinect sensors in sharing the workplace in their research.", acknowledgement = ack-nhfb, articleno = "2450001", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Deepak:2024:ASI, author = "A. V. S. Deepak and Umesh Ghanekhar", title = "Analysis of Single Image Super-Resolution Techniques: an Evolutionary Study", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "01", pages = "??--??", month = jan, year = "2024", DOI = "https://doi.org/10.1142/S0219467824500025", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu May 23 07:14:57 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500025", abstract = "Single image super-resolution (SR) is a technique that reconstructs a high-resolution (HR) image from a single low-resolution (LR) input image. The main objective of super-resolution algorithms is to achieve a high-resolution image that is consistent with the input low-resolution image but has enhanced spectral properties. In this review, several research papers and their corresponding algorithms have been reviewed and are classified based on their methodology. The principal objective of this review is to understand the evolution of SISR techniques from basic interpolation techniques to sophisticated convolutional neural networks. This article also presents design considerations for future advancements.", acknowledgement = ack-nhfb, articleno = "2450002", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Roy:2024:SRM, author = "Srinjoy Roy and D. Binu and B. R. Rajakumar and Vamsidhar Talasila and Abhishek Bhatt", title = "Super Resolved Maize Plant Leaves Disease Detection Using Optimal Generative Adversarial Network", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "01", pages = "??--??", month = jan, year = "2024", DOI = "https://doi.org/10.1142/S0219467824500037", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu May 23 07:14:57 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500037", abstract = "Agriculture plays a vital role in the economy and crop disease causes huge financial losses every year. The losses can be reduced by detecting the disease accurately. The variation in light intensity and complex background of the agricultural field in detecting the maize leaves disease are the biggest challenges. An optimization algorithm, named Cat Swarm Political Optimizer Algorithm (CSPOA) has been developed in this research to detect the disease of a maize plant leaf. Our proposed algorithm is an integration of the Cat Swarm Optimization (CSO) and Political Optimizer (PO) algorithm. Anisotropic filtering performs pre-processing for removing noise and the Region of Interest (ROI) extraction for enhancing the image quality. The super resolution image is obtained from the Low Resolution (LR) images using kernel regression model. After obtaining the super resolution image, the salient map extraction has been carried out for representing the saliency. Finally, the maize plant leaves disease classification process is done using General Adversarial Network (GAN) for identifying the maize leaves disease. The training of GAN develops the CSPOA. On comparing with the existing maize plant leaves disease detection approaches, the developed CSPOA-based GAN performed with a maximum accuracy 0.9056, maximum sensitivity 0.9599, and the maximum specificity 0.9592, respectively.", acknowledgement = ack-nhfb, articleno = "2450003", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Naik:2024:NSE, author = "Manoj Kumar Naik and Monorama Swain and Rutuparna Panda and Ajith Abraham", title = "Novel Square Error Minimization-Based Multilevel Thresholding Method for {COVID-19} {X}-Ray Image Analysis Using Fast Cuckoo Search", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "01", pages = "??--??", month = jan, year = "2024", DOI = "https://doi.org/10.1142/S0219467824500049", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu May 23 07:14:57 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500049", abstract = "Coronavirus outbreaks in 2019 (COVID-19) have been a huge disaster in the fields of health, economics, education, and tourism in the last two years. For diagnosis, a quick interpretation of the COVID-19 chest X-ray image is required. There is also a strong need to find an efficient multiclass segmentation technique for the analysis of COVID-19 X-ray images. Most of the threshold selection techniques are entropy-based. Nevertheless, these techniques suffer from their dependencies on the spatial distribution of grey values. To tackle these issues, a novel non-entropic threshold selection method is proposed, which is the primary key contribution having found a new source of information to the biomedical image processing field. The firsthand Square Error (SE)-based objective function is suggested. The second key contribution is the new optimizer called Fast Cuckoo Search (FCS), which is useful and brings novel ideas into the subject, used to optimize the suggested objective functions for computing the optimal thresholds. To ensure a faster convergence with a quality optimal solution, we include extra exploitation together with a chance factor. The FCS is validated using the well-known classical and CEC 2014 benchmark test functions, which shows a significant improvement over its predecessors --- Adaptive Cuckoo Search (ACS) and other state-of-the-art optimizers. Further, the SE minimization-based optimal multilevel thresholding method using the FCS, coined as SE-FCS, is proposed. To experiment, images are considered from the Kaggle Radiography database. We have compared its performances with Tsallis, Kapur's, and Masi entropy-based techniques using well-known segmentation metrics and achieved a performance increase of 2.95\%, 5.51\% and 10.50\%, respectively. The proposed method shows superiority using Friedman's mean rank statistical test and ranked first.", acknowledgement = ack-nhfb, articleno = "2450004", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Lyu:2024:AIO, author = "Chengang Lyu and Mengqi Zhang and Jie Jin", title = "An Adaptive Illumination Optimization Method for Local Overexposed Image", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "01", pages = "??--??", month = jan, year = "2024", DOI = "https://doi.org/10.1142/S0219467824500050", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu May 23 07:14:57 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500050", abstract = "In order to solve the local overexposure caused by uneven surface reflectance, this paper proposes a fast-adaptive illumination control method with a camera-projector system. At first, an image is captured by the camera and the local overexposed area is segmented using saliency detection. Then the calculated image is projected onto the object by the projector as corrective illumination. The calculation process includes the inversion of the gray value in the overexposed area and the adjustment based on the position and depth information of the object. The high-exposure saturated regional which affects the target recognition is thus reduced, and the original illumination intensity is reserved for the other regions. This process is iterated until the optimal illumination is achieved. The resulting image for each iteration is evaluated using Blind/no Reference Image Space Quality Estimator (BRISQUE). When BRISQUE value reaches the minimum, a high-quality image is achieved. The experiments show that the proposed approach can significantly improve the speed of obtaining normally exposed images, and this system provides new ideas for industry image acquisition.", acknowledgement = ack-nhfb, articleno = "2450005", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Elloumi:2024:FRO, author = "Nessrine Elloumi and Habiba Loukil and Med Salim Bouhlel", title = "Full-Reference Objective Quality Metric for Three-Dimensional Deformed Models", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "01", pages = "??--??", month = jan, year = "2024", DOI = "https://doi.org/10.1142/S0219467824500062", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu May 23 07:14:57 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500062", abstract = "Three-dimensional data are generally represented by triangular meshes. The 3D data are used in several fields including remote 3D games, 3D medical application, 3D virtual worlds and 3D augmented reality application. These applications require displaying, printing or exchanging the 3D models through the network to optimize the rendering of the 3D models and 3D applications, which include different treatments, for example, smoothing, compression, re-meshing, simplification, watermarking, etc. However, these processes generate distortions that affect the quality of the rendered 3D data. Thus, subjective or objective metrics are required for assessing the visual quality of the deformed models to evaluate the efficiency of the applied algorithms. In this context, we introduce a new perceptual full-reference metric that compare two 3D meshes based on their 3D content information. The proposed metric integrates the relativity and selectivity properties of the Human visual system (HVS) independent of the mesh type and connectivity (e.g. Triangular, Quadrilateral, Tetrahedron, Hexahedron), which represent a limit in the existing method, in order to capture the perceptual quantity of the distortion by the observer. The results of the proposed approach outperform the existing metrics and have a high correlation with the subjective measures. We use the two correlation coefficients Spearman Rank (Rs) and Pearson Rank (Rp) in order to assess the performance of the proposed metric.", acknowledgement = ack-nhfb, articleno = "2450006", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Pedalanka:2024:EDN, author = "P. S. Subhashini Pedalanka and Manchikalapudi Satya Sai Ram and Duggirala Sreenivasa Rao", title = "An Enhanced Deep Neural Network-Based Approach for Speaker Recognition Using Triumvirate Euphemism Strategy", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "01", pages = "??--??", month = jan, year = "2024", DOI = "https://doi.org/10.1142/S0219467824500074", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu May 23 07:14:57 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500074", abstract = "Automatic Speech Recognition (ASR) has been an intensive research area during the recent years in internet to enable natural human--machine communication. However, the existing Deep Neutral Network (DNN) techniques need more focus on feature extraction process and recognition accuracy. Thus, an enhanced deep neural network (DNN)-based approach for speaker recognition with a novel Triumvirate Euphemism Strategy (TES) is proposed. This overcomes poor feature extraction from Mel-Frequency Cepstral Coefficient (MFCC) map by extracting the features based on petite, hefty and artistry of the features. Then, the features are trained with Silhouette Martyrs Method (SMM) without any inter-class and intra-class separability problems and margins are affixed between classes with three new loss functions, namely A-Loss, AM-Loss and AAM-Loss. Additionally, the parallelization is done by a mini-batch-based BP algorithm in DNN. A novel Frenzied Heap Atrophy (FHA) with a multi-GPU model is introduced in addition with DNN to enhance the parallelized computing that accelerates the training procedures. Thus, the outcome of the proposed technique is highly efficient that provides feasible extraction features and gives incredibly precise results with 97.5\% accuracy in the recognition of speakers. Moreover, various parameters were discussed to prove the efficiency of the system and also the proposed method outperformed the existing methods in all aspects.", acknowledgement = ack-nhfb, articleno = "2450007", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kumar:2024:OAC, author = "V. Rajesh Kumar and P. Aruna Jeyanthy and R. Kesavamoorthy", title = "Optimization-Assisted {CNN} Model for Fault Classification and Site Location in Transmission Lines", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "01", pages = "??--??", month = jan, year = "2024", DOI = "https://doi.org/10.1142/S0219467824500086", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu May 23 07:14:57 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500086", abstract = "The theme of the paper is to emphasize the detection and classification of faults and their site location in the transmission line using machine learning techniques which help to indemnify the foul-up of the humans in identifying the site and type of occurrence of fault. Moreover, the transient stability is a supreme one in power systems and so the disturbances like faults are required to be separated to preserve the transient stability. In general, the protection of the transmission line includes the installation of relays at both ends of the line that constantly monitor voltages and currents and operate unless a fault occurs on a line. Therefore, this paper intends to introduce a novel transmission line protection model by exploiting the hybrid optimization concept to train the Convolutional Neural Network (CNN). Here, the fault detection, classification and site location are diagnosed by using CNN which is trained and tested by making use of diverse synthetic field data derived from the simulation models of distinct types of transmission lines. Hence, the location and the type of faults will be predicted by the CNN depending on the fault signal characteristics which are optimally trained by a new hybrid algorithm named Chicken Swarm Insisted Spotted Hyena (CSI-SH) Algorithm that hybrids both the concept of Spotted Hyena Optimization (SHO) and Chicken Swarm Optimization (CSO). Finally, the proposed method based on CNN for fault classification and site location of transmission lines is implemented in MATLAB/Simulink and the performances are compared with various measures like classification accuracy, fault detection rate and so on.", acknowledgement = ack-nhfb, articleno = "2450008", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Bapatla:2024:DHO, author = "Sesikala Bapatla and J. Harikiran", title = "Deer Hunting Optimization with {$3$D}-Convolutional Neural Network for Diabetic Retinopathy Classification Model", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "01", pages = "??--??", month = jan, year = "2024", DOI = "https://doi.org/10.1142/S0219467824500098", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu May 23 07:14:57 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500098", abstract = "A retina disease caused by high glucose levels in the blood is called Diabetic Retinopathy (DR) and is the world's leading cause of blindness. To avoid or delay vision degradation and loss, early diagnosis and treatment are required. As a result, the creation of an automated method for accurate DR identification is essential. For this, in this paper, a 3D-Convolution Neural Network (3D-CNN) with Deer Hunting Optimization (DHO) algorithm is proposed for detecting and classifying DR images. The proposed 3D-CNN-DHO approach includes four phases such as pre-processing, segmentation, feature extraction, and classification. The contrast of the DR image is first improved using a Contrast-Limited Adaptive Histogram Equalization (CLAHE) approach. Subsequently, the threshold-based effective segmentation is carried out. Then, the Resnet50 model is implemented to extract the features from the image. Finally, 3D-CNN-DHO-based classifier model is implemented to categorize the various DR stages. The experiments are carried out in detail and evaluated on the Messidor DR benchmark dataset. The acquired experimental result demonstrated the 3D-CNN-DHO model's outstanding qualities by achieving optimal specificity, sensitivity, recall, precision, and accuracy.", acknowledgement = ack-nhfb, articleno = "2450009", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Gadde:2024:CMD, author = "Swetha Gadde and J. Amutharaj and S. Usha", title = "Cloud Multimedia Data Security by Optimization-Assisted Cryptographic Technique", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "01", pages = "??--??", month = jan, year = "2024", DOI = "https://doi.org/10.1142/S0219467824500104", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu May 23 07:14:57 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500104", abstract = "Currently, the size of multimedia data is rising gradually from gigabytes to petabytes, due to the progression of a larger quantity of realistic data. The majority of big data is conveyed via the internet and they were accumulated on cloud servers. Since cloud computing offers internet-oriented services, there were a lot of attackers and malevolent users. They always attempt to deploy the private data of users without any right access. At certain times, they substitute the real data by any counterfeit data. As a result, data protection has turned out to be a noteworthy concern in recent times. This paper aims to establish an optimization-based privacy preservation model for preserving multimedia data by selecting the optimal secret key. Here, the encryption and decryption process is carried out by Improved Blowfish cryptographic technique, where the sensitive data in cloud server is preserved using the optimal key. Optimal key generation is the significant procedure to ensure the objectives of integrity and confidentiality. Likewise, data restoration is the inverse process of sanitization (decryption). In both the cases, key generation remains a major aspect, which is optimally chosen by a novel hybrid algorithm termed as ``Clan based Crow Search with Adaptive Awareness probability (CCS-AAP). Finally, an analysis is carried out to validate the improvement of the proposed method.", acknowledgement = ack-nhfb, articleno = "2450010", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Yang:2024:DDS, author = "Hailong Yang and Yinghao Liu and Tian Xia", title = "Defect Detection Scheme of Pins for Aviation Connectors Based on Image Segmentation and Improved {RESNET-50}", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "01", pages = "??--??", month = jan, year = "2024", DOI = "https://doi.org/10.1142/S0219467824500116", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu May 23 07:14:57 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500116", abstract = "In this paper, a new detection method of pin defects based on image segmentation and ResNe-50 is proposed, which realizes the defect detection of faulty pins in many aviation connectors. In this paper, a new dataset image segmentation method is used to segment many aviation connectors in a single image to generate a dataset, which reduces the tedious work of manually labeling the dataset. In the defect detection model, based on ResNet-50, a ResNet-B residual structure is introduced to reduce the loss of features during information extraction; a continuously differentiable CELU is used as the activation function to reduce the neuron death problem of ReLU; a new deformable convolution network (DCN v2) is introduced as the convolution kernel structure of the model to improve the recognition of aviation connectors with prominent geometric deformation pin recognition. The improved model achieved 97.2\% and 94.4\% accuracy for skewed and missing pins, respectively, in the experiments. The detection accuracy improved by 1.91\% to 96.62\% compared to the conventional ResNet-50. Compared with the traditional model, the improved model has better generalization ability.", acknowledgement = ack-nhfb, articleno = "2450011", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kumar:2024:MLA, author = "K. Antony Kumar and M. J. Carmel Mary Belinda", title = "A Multi-Layer Acoustic Neural Network-Based Intelligent Early Diagnosis System for Rheumatic Heart Disease", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "01", pages = "??--??", month = jan, year = "2024", DOI = "https://doi.org/10.1142/S0219467824500128", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu May 23 07:14:57 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500128", abstract = "Rheumatic Heart Disease (RHD) is a disorder of heart caused by streptococcal throat infection followed by the organ damage, irreversible valve damage and heart failure. Acute Rheumatic Fever (ARF) is a precursor to the disease. Sometimes, RHD can occur without any signs or symptoms, and if there are any symptoms, they occur with the infection in the heart valves and fever. Due to these issues, respiratory problems occur with chest pain and tremors. Additionally, the symptoms include faint, heart murmurs, stroke and unexpected collapse. The techniques available try to detect the RHD as early as possible. Although the recent medical health care department uses crucial techniques, they are not accurate in terms of symptom classification, precision and prediction. On the scope, we are developing Multi-Layered Acoustic Neural (MLAN) Networks to detect the RHD symptoms using heart beat sound and Electrocardiogram (ECG) measurements. In this proposed MLAN system, the novel techniques such as multi-attribute acoustic data sampling model, heart sound sampling procedures, ECG data sampling model, RHD Recurrent Convolutional Network (RRCN) and Acoustic Support Vector Machine (ASVM) are used for increasing the accuracy. In the implementation section, the proposed model has been compared to the Long Short-Term Memory-based Cardio (LSTC) data analysis model, Cardio-Net and Video-Based Deep Learning (VBDL) techniques. In this comparison, the proposed system has 10\%--17\% higher accuracy in RHD detection than existing techniques.", acknowledgement = ack-nhfb, articleno = "2450012", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Duan:2024:ABR, author = "Xueying Duan", title = "Abnormal Behavior Recognition for Human Motion Based on Improved Deep Reinforcement Learning", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "01", pages = "??--??", month = jan, year = "2024", DOI = "https://doi.org/10.1142/S0219467825500299", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu May 23 07:14:57 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500299", abstract = "Recognizing abnormal behavior recognition (ABR) is an important part of social security work. To ensure social harmony and stability, it is of great significance to study the identification methods of abnormal human motion behavior. Aiming at the low accuracy of human motion ABR method, ABR method for human motion based on improved deep reinforcement learning (DRL) is proposed. First, the background image is processed in combination with the Gaussian model; second, the background features and human motion trajectory features are extracted, respectively; finally, the improved DRL model is constructed, and the feature information is input into the improvement model to further extract the abnormal behavior features, and the ABR of human motion is realized through the interaction between the agent and the environment. The different methods were examined based on UCF101 data set and HiEve data set. The results show that the accuracy of human motion key point acquisition and posture estimation accuracy is high, the proposed method sensitivity is good, and the recognition accuracy of human motion abnormal behavior is as high as 95.5\%. It can realize the ABR for human motion and lay a foundation for the further development of follow-up social security management.", acknowledgement = ack-nhfb, articleno = "2550029", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Rao:2024:HSE, author = "K. Venkateswara Rao and B. Venkata Ramana Reddy", title = "{HM-SMF}: an Efficient Strategy Optimization using a Hybrid Machine Learning Model for Stock Market Prediction", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "02", pages = "??--??", month = mar, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1142/S021946782450013X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Jun 5 09:06:54 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S021946782450013X", abstract = "Stock market forecasting is a significant task, and investing in the stock marketplace is a significant part of monetary research due to its high risk. Therefore, accurate forecasting of stock market analysis is still a challenge. Due to stable and volatile data, stock market forecasting remains a major challenge for investors. Recent machine learning (ML) models have been able to reduce the risk of stock market forecasting. However, diversity remains a key challenge in developing better erudition models and extracts more intellectually priceless qualities to auxiliary advanced predictability. In this paper, we propose an efficient strategy optimization using a hybrid ML model for stock market prediction (HM-SMP). The first contribution of the proposed HM-SMP model is to introduce chaos-enhanced firefly bowerbird optimization (CEFBO) algorithm for optimal feature selection among multiple features which reduce the data dimensionality. Second, we develop a hybrid multi-objective capuchin with a recurrent neural network (HC-RNN) for the prediction of the stock market which enhances the prediction accuracy. We use supervised RNN to predict the closing price. Finally, to estimate the presence of the proposed HM-SMP model through the benchmark, stock market datasets and the performance can be compared with the existing state-of-the-art models in terms of accuracy, precision, recall, and $F$-measure.", acknowledgement = ack-nhfb, articleno = "2450013", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Moradi:2024:IIE, author = "Saed Moradi and Jahed Moradi and Saeid Aghaziyarati and Hadi Shahraki", title = "Infrared Image Enhancement Based on Optimally Weighted Multi-Scale {Laplacian} of {Gaussian} and Local Statistics Using Particle Swarm Optimization", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "02", pages = "??--??", month = mar, year = "2024", DOI = "https://doi.org/10.1142/S0219467824500141", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Jun 5 09:06:54 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500141", abstract = "Infrared imagery is extensively used in defense, remote sensing and medical applications. While the infrared images have many advantages over RGB images, the details in these images are usually blurred which in turn leads to some difficulties for human operators. In this paper, a new method based on Laplacian of Gaussian scale-space and local variance is presented to improve the visual quality of the infrared images. At the first step, the Gaussian scale-space is constructed by convolving the original image with different Gaussian kernels. Then, the two-dimensional Laplacian kernels are convolved with the Gaussian scale-space to achieve details with both positive as well as negative contrasts. The weighted details are added to the original image to deblur the dim areas. At the final step, to increase the dynamic range of the image and have better visual quality, the local variance of the image is also added to the output of the previous step. Since finding optimum weighting coefficients is a difficult task empirically, here, we use a population-based meta-heuristic optimization algorithm called particle swarm optimization (PSO) to find the optimum values for weighting coefficient values. Beside qualitative comparison, Structural Similarity (SSIM) and second-derivative-like measure of enhancement (SDME) are used to quantitatively investigate the images quality. The proposed method outperforms the baseline algorithms in both qualitative and quantitative perspectives.", acknowledgement = ack-nhfb, articleno = "2450014", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Thakkar:2024:EBP, author = "Priyanka Bibay Thakkar and R. H. Talwekar", title = "An Efficient Blood Pressure Estimation and Risk Analysis System of {PPG} Signals Using {IDA} and {MPPIW-DLNN} Algorithms", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "02", pages = "??--??", month = mar, year = "2024", DOI = "https://doi.org/10.1142/S0219467824500153", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Jun 5 09:06:54 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500153", abstract = "The non-invasive Blood Pressure Estimation (BPE) utilizing the technology of photoplethysmography (PPG) gains significant interest because PPG could be extensively employed to wearable sensors. Here, a method for estimating Systolic Blood pressure (SBP), as well as Diastolic Blood pressure (DBP), grounded only on a PPG signal utilizing the Image Denoising Algorithms (IDA) algorithms is proposed. Also, a classification methodology to execute the risk analysis (RA) of the BP patients utilizing Moore--Penrose Pseudo-Inverse Matrix-Deep Learning Neural Network (MPPIW-DLNN) is proposed. The preprocessing is then done on the input PPG signal utilizing the Modified--Chebyshev Filter (CF) to eradicate the unwanted information existent in the signal. Afterward, the BPE is done utilizing IDA, which categorizes those components into (i) SBP and (ii) DBP. The MPPIW-DLNN provides the results of four sorts of risk classes like (i) stroke, (ii) heart failure (HF), (iii) heart attack (HA), and (iv) aneurysm identified from the inputted PPG signal.", acknowledgement = ack-nhfb, articleno = "2450015", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kumawat:2024:TVS, author = "Manisha Kumawat and Arti Khaparde", title = "Time-Variant Satellite Vegetation Classification Enabled by Hybrid Metaheuristic-Based Adaptive Time-Weighted Dynamic Time Warping", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "02", pages = "??--??", month = mar, year = "2024", DOI = "https://doi.org/10.1142/S0219467824500165", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Jun 5 09:06:54 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500165", abstract = "Land cover data is very significant for designing the earth system, managing the natural resources, and also for performing conservation planning. Time-series data are captured with their dynamic vegetation behavior using remote sensing technology, which is broadly utilized in land cover mapping. Most of the Vegetation Index (VI) such as the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) comprises commonly employed features that are obtained from the time-series spectral data. But, these VIs are not validated as the optimal techniques for generating the temporal profiles. Recent researches highly depend on optical satellite imagery for performing these above-mentioned tasks. Dynamic Time Warping (DTW) is said to be an effective optimal solution for solving the existing challenges, especially the improved version of DTW named Time-Weighted Dynamic Time Warping (TWDTW) is used for time-series analysis regarding the time-series vegetation classification. Yet, the TWDTW efficiency is not shown with other comparative machine learning approaches owing to the classification of vegetation type in the mountain areas. The major goal of this paper is to research and create a novel approach for distinguishing the kind of vegetation in a farm region near Ujani Dam in Solapur District, Maharashtra using time-series analysis. For time-series analysis employing satellite images, the suggested model offers a unique Adaptive Time-Weighted Dynamic Time Warping (ATWDTW). The farm's satellite images are first pre-processed before being sent to ATWDTW for examination. The TWDTW idea is optimized for classification performance using a new hybrid metaheuristic technique named Adaptive Coyote Crow Search Optimization (ACCSO). From the experimental results, the performance of the suggested ACCSO-ATWDTW correspondingly provides superior performance to the traditional approaches, where the designed model using ACCSO-ATWDTW provides 7.2\%, 5.2\%, 9.9\%, 4.55\%, and 2.33\% higher MCC than the MFO-ATWDTW, BSA-ATWDTW, MF-BSA-ATWDTW, CSA-ATWDTW, and COA-ATWDTW at the maximum iteration of 200. This proved the robustness and less sensitivity to training samples of the TWDTW method when applied to mountain vegetation-type classifications.", acknowledgement = ack-nhfb, articleno = "2450016", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Sowmya:2024:CNB, author = "M. N. Sowmya and Keshava Prasanna", title = "Convoluted Neighborhood-Based Ordered-Dither Block Truncation Coding for Ear Image Retrieval", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "02", pages = "??--??", month = mar, year = "2024", DOI = "https://doi.org/10.1142/S0219467824500177", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Jun 5 09:06:54 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500177", abstract = "Image retrieval is a significant and hot research topic among researchers that drives the focus of researchers from keyword toward semantic-based image reconstruction. Nevertheless, existing image retrieval investigations still have a shortage of significant semantic image definition and user behavior consideration. Hence, there is a necessity to offer a high level of assistance towards regulating the semantic gap between low-level visual patterns and high-level ideas for a better understanding between humans and machines. Hence, this research devises an effective medical image retrieval strategy using convoluted neighborhood-based Ordered-dither block truncation coding (ODBTC). The developed approach is devised by modifying the ODBTC concept using a convoluted neighborhood mechanism. Here, the convoluted neighborhood-based color co-occurrence feature (CCF) and convoluted neighborhood-based bit pattern feature (BBF) are extracted. Finally, cross-indexing is performed to convert the feature points into binary codes for effective image retrieval. Meanwhile, the proposed convoluted neighborhood-based ODBTC has achieved maximum precision, recall, and f-measure with values of 0.740, 0.680, and 0.709.", acknowledgement = ack-nhfb, articleno = "2450017", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Chaurasiya:2024:RRV, author = "Rashmi Chaurasiya and Dinesh Ganotra", title = "Reflection Removal with Varied Field of View Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "02", pages = "??--??", month = mar, year = "2024", DOI = "https://doi.org/10.1142/S0219467824500189", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Jun 5 09:06:54 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500189", abstract = "Due to the presence of an additional glass pane between the camera and the scene, an additional reflection scene is captured in the image apart from the desired object sometimes. Images are more often captured from mobile handsets these days which have multiple cameras. This paper gives the advantage of multiple cameras. There exists a disparity and varied field of view when images are captured with multiple cameras. We use these two factors to act as a cue to remove reflection, as reflection intensity across the image pairs change with different field-of-view. The proposed method is robust and convenient to implement as it does not require an additional hardware, for example, light field camera for stereo images. Also, it does not make assumptions about the appearance or intensity of reflection.", acknowledgement = ack-nhfb, articleno = "2450018", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Shaikh:2024:RTM, author = "Shakil A. Shaikh and Jayant J. Chopade and Mohini Pramod Sardey", title = "Real-Time Multi-Object Detection Using Enhanced {Yolov5-7S} on Multi-{GPU} for High-Resolution Video", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "02", pages = "??--??", month = mar, year = "2024", DOI = "https://doi.org/10.1142/S0219467824500190", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Jun 5 09:06:54 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500190", abstract = "Multiple objects tracking in a video sequence can be performed by detecting and distinguishing the objects that appear in the sequence. In the context of computer vision, the robust multi-object tracking problem is a difficult problem to solve. Visual tracking of multiple objects is a vital part of an autonomous driving vehicle's vision technology. Wide-area video surveillance is increasingly using advanced imaging devices with increased megapixel resolution and increased frame rates. As a result, there is a huge increase in demand for high-performance computation system of video surveillance systems for real-time processing of high-resolution videos. As a result, in this paper, we used a single stage framework to solve the MOT problem. We proposed a novel architecture in this paper that allows for the efficient use of one and multiple GPUs are used to process Full High Definition video in real time. For high-resolution video and images, the suggested approach is real-time multi-object detection based on Enhanced Yolov5-7S on Multi-GPU Vertex. We added one more layer at the top in backbone to increase the resolution of feature extracted image to detect small object and increase the accuracy of model. In terms of speed and accuracy, our proposed approach outperforms the state-of-the-art techniques.", acknowledgement = ack-nhfb, articleno = "2450019", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Du:2024:SSS, author = "Fuhe Du and Bo Peng and Zaid Al-huda and Jing Yao", title = "Semi-Supervised Skin Lesion Segmentation via Iterative Mask Optimization", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "02", pages = "??--??", month = mar, year = "2024", DOI = "https://doi.org/10.1142/S0219467824500207", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Jun 5 09:06:54 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500207", abstract = "Deep learning-based skin lesion segmentation methods have achieved promising results in the community. However, they are usually based on fully supervised learning and require many high-quality ground truths. Labeling the ground truths takes a lot of labor, material, and financial resources. We propose a novel semi-supervised skin lesion segmentation method to solve this problem. First, a hierarchical image segmentation algorithm is used to generate optimal segmentation maps. Then, fully supervised training is performed on a small part of the images with ground truths. The resulting pseudo masks are generated to train the rest of the images. The optimal segmentation maps are utilized in this process to refine the pseudo masks. Experiments show that the proposed method can improve the performance of semi-supervised learning for skin lesion segmentation by reducing the gap with fully supervised learning methods. Moreover, it can reduce the workload of labeling the ground truths. Extensive experiments are conducted on the open dataset to validate the efficiency of the proposed method. The results show that our method is competitive in improving the quality of semi-supervised segmentation.", acknowledgement = ack-nhfb, articleno = "2450020", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Mulla:2024:WGE, author = "Samina Mulla and Nuzhat F. Shaikh", title = "Weighted Graph Embedding Feature with Bi-Directional Long Short-Term Memory Classifier for Multi-Document Text Summarization", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "02", pages = "??--??", month = mar, year = "2024", DOI = "https://doi.org/10.1142/S0219467824500220", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Jun 5 09:06:54 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500220", abstract = "In this digital era, there is a tremendous increase in the volume of data, which adds difficulties to the person who utilizes particular applications, such as websites, email, and news. Text summarization targets to reduce the complexity of obtaining statistics from the websites as it compresses the textual document to a short summary without affecting the relevant information. The crucial step in multi-document summarization is obtaining a relationship between the cross-sentence. However, the conventional methods fail to determine the inter-sentence relationship, especially in long documents. This research develops a graph-based neural network to attain an inter-sentence relationship. The significant step in the proposed multi-document text summarization model is forming the weighted graph embedding features. Furthermore, the weighted graph embedding features are utilized to evaluate the relationship between the document's words and sentences. Finally, the bidirectional long short-term memory (BiLSTM) classifier is utilized to summarize the multi-document text summarization. The experimental analysis uses the three standard datasets, the Daily Mail dataset, Document Understanding Conference (DUC) 2002, and Document Understanding Conference (DUC) 2004 dataset. The experimental outcome demonstrates that the proposed weighted graph embedding feature + BiLSTM model exceeds all the conventional methods with Precision, Recall, and F1 score of 0.5352, 0.6296, and 0.5429, respectively.", acknowledgement = ack-nhfb, articleno = "2450022", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Enturi:2024:ODC, author = "B. Krishna Manash Enturi and A. Suhasini and and Narayana Satyala", title = "Optimized Deep {CNN} with Deviation Relevance-based {LBP} for Skin Cancer Detection: Hybrid Metaheuristic Enabled Feature Selection", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "02", pages = "??--??", month = mar, year = "2024", DOI = "https://doi.org/10.1142/S0219467824500232", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Jun 5 09:06:54 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500232", abstract = "Segmentation of skin lesions is a significant and demanding task in dermoscopy images. This paper proposes a new skin cancer recognition scheme, with: ``Pre-processing, Segmentation, Feature extraction, Optimal Feature Selection and Classification''. Here, pre-processing is done with certain processes. The pre-processed images are segmented via the ``Otsu Thresholding model''. The third phase is feature extraction, where Deviation Relevance-based ``Local Binary Pattern (DRLBP), Gray-Level Co-Occurrence Matrix (GLCM) features and Gray Level Run-Length Matrix (GLRM) features'' are extracted. From these extracted features, the optimal features are chosen via Particle Updated WOA (PU-WOA) model. Subsequently, classification occurs via Optimized DCNN and NN to classify the skin lesion. To make the classification more precise, the DCNN is optimized by the introduced algorithm. The result has shown a higher accuracy of 0.998737, when compared with other extant models like IPSO, IWOA, PSO+CNN, WOA+CNN and CNN schemes.", acknowledgement = ack-nhfb, articleno = "2450023", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Pakhare:2024:HML, author = "Jayamala D. Pakhare and Mahadev D. Uplane", title = "Hybrid Mayfly {L{\'e}vy} Flight Distribution Optimization Algorithm-Tuned Deep Convolutional Neural Network for Indoor--Outdoor Image Classification", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "02", pages = "??--??", month = mar, year = "2024", DOI = "https://doi.org/10.1142/S0219467824500244", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Jun 5 09:06:54 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500244", abstract = "Image classification in the image is the persistent task to be computed in robotics, automobiles, and machine vision for sustainability. Scene categorization remains one of the challenging parts of various multi-media technologies implied in human--computer communication, robotic navigation, video surveillance, medical diagnosing, tourist guidance, and drone targeting. In this research, a Hybrid Mayfly L{\'e}vy flight distribution (MLFD) optimization algorithm-tuned deep convolutional neural network is proposed to effectively classify the image. The feature extraction process is a significant task to be executed as it enhances the classifier performance by reducing the execution time and the computational complexity. Further, the classifier is optimally trained by the Hybrid MLFD algorithm which in turn reduces optimization issues. The accuracy of the proposed MLFD-based Deep-CNN using the SCID-2 dataset is 95.2683\% at 80\% of training and 97.6425\% for 10 K-fold. This manifests that the proposed MLFD-based Deep-CNN outperforms all the conventional methods in terms of accuracy, sensitivity, and specificity.", acknowledgement = ack-nhfb, articleno = "2450024", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Xin:2024:SAC, author = "Guangnan Xin and Min Zhu and Yuze Zhou and Guanyu Jiang and Zeyu Cai and Aoyu Pang and Qi Zhu", title = "A Self-Attention {CycleGAN} for Cross-Domain Semi-Supervised Contactless Palmprint Recognition", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "02", pages = "??--??", month = mar, year = "2024", DOI = "https://doi.org/10.1142/S0219467824500256", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Jun 5 09:06:54 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500256", abstract = "Nowadays, there is a growing concern about contactless palmprint recognition because of its high-recognition rate, efficiency, and convenience. With the development of image acquisition equipment, it is an often case that the palmprint images for identification and for registration are captured by different devices. At the same time, a large amount of well-labeled palmprint images are difficult to collect. Therefore, the performance of most existing contactless palmprint recognition methods will be poor in real-life applications. To address these issues, we proposed a self-attention CycleGAN for cross-domain semi-supervised palmprint recognition. Based on CycleGAN, the styles of contactless palmprint images in source domain and target domain can be swapped. Specifically, the spatial features are captured through self-attention modules by modeling long-range dependencies. In addition, an extra source domain classifier is trained with the labeled source domain images to give the unlabeled images in target domain a pseudo-label, by which images in target domain are efficiently utilized. The experiment results showed that our method achieved competitive performance.", acknowledgement = ack-nhfb, articleno = "2450025", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Liu:2024:FSP, author = "Yun Liu", title = "Fault Signal Perception of Nanofiber Sensor for {$3$D} Human Motion Detection Using Multi-Task Deep Learning", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "02", pages = "??--??", month = mar, year = "2024", DOI = "https://doi.org/10.1142/S0219467825500603", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Jun 5 09:06:54 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500603", abstract = "Once a fault occurs in the nanofiber sensor, the scientific and reliable three-dimensional (3D) human motion detection results will be compromised. It is necessary to accurately and rapidly perceive the fault signals of the nanofiber sensor and determine the type of fault, to enable it to continue operating in a sustained and stable manner. Therefore, we propose a fault signal perception method for 3D human motion detection nanofiber sensor based on multi-task deep learning. First, through obtaining the fault characteristic parameters of the nanofiber sensor, the fault of the nanofiber sensor is reconstructed to complete the fault location of the nanofiber sensor. Second, the fault signal of the nanofiber sensor is mapped by the penalty function, and the feature extraction model of the fault signal of the nanofiber sensor is constructed by combining the multi-task deep learning. Finally, the multi-task deep learning algorithm is used to calculate the sampling frequency of the fault signal, and the key variable information of the fault of the nanofiber sensor is extracted according to the amplitude of the state change of the nanofiber sensor, to realize the perception of the fault signal of the nanofiber sensor. The results show that the proposed method can accurately perceive the fault signal of a nanofiber sensor in 3D human motion detection, the maximum sensor fault location accuracy is 97\%, and the maximum noise content of the fault signal is only 5 dB, which shows that the method can be widely used in fault signal perception.", acknowledgement = ack-nhfb, articleno = "2550060", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Chukka:2024:BSM, author = "Demudu Naidu Chukka and James Stephen Meka and S. Pallam Setty and Praveen Babu Choppala", title = "{Bayesian} Selective Median Filtering for Reduction of Impulse Noise in Digital Color Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "03", pages = "??--??", month = may, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467824500268", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Jun 5 09:06:55 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500268", abstract = "The focus of this paper is impulse noise reduction in digital color images. The most popular noise reduction schemes are the vector median filter and its many variants that operate by minimizing the aggregate distance from one pixel to every other pixel in a chosen window. This minimizing operation determines the most confirmative pixel based on its similarity to the chosen window and replaces the central pixel of the window with the determined one. The peer group filters, unlike the vector median filters, determine a set of pixels that are most confirmative to the window and then perform filtering over the determined set. Using a set of pixels in the filtering process rather than one pixel is more helpful as it takes into account the full information of all the pixels that seemingly contribute to the signal. Hence, the peer group filters are found to be more robust to noise. However, the peer group for each pixel is computed deterministically using thresholding schemes. A wrong choice of the threshold will easily impair the filtering performance. In this paper, we propose a peer group filtering approach using principles of Bayesian probability theory and clustering. Here, we present a method to compute the probability that a pixel value is clean (not corrupted by impulse noise) and then apply clustering on the probability measure to determine the peer group. The key benefit of this proposal is that the need for thresholding in peer group filtering is completely avoided. Simulation results show that the proposed method performs better than the conventional vector median and peer group filtering methods in terms of noise reduction and structural similarity, thus validating the proposed approach.", acknowledgement = ack-nhfb, articleno = "2450026", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{He:2024:LBH, author = "Shuhan He and Xueming Li and Qiang Fu", title = "{Laplace}-Based {$3$D} Human Mesh Sequence Compression", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "03", pages = "??--??", month = may, year = "2024", DOI = "https://doi.org/10.1142/S021946782450027X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Jun 5 09:06:55 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S021946782450027X", abstract = "Three-dimensional (3D) human mesh sequences obtained by 3D scanning equipment are often used in film and television, games, the internet, and other industries. However, due to the dense point cloud data obtained by 3D scanning equipment, the data of a single frame of a 3D human model is always large. Considering the different topologies of models between different frames, and even the interaction between the human body and other objects, the content of 3D models between different frames is also complex. Therefore, the traditional 3D model compression method always cannot handle the compression of the 3D human mesh sequence. To address this problem, we propose a sequence compression method of 3D human mesh sequence based on the Laplace operator, and test it on the complex interactive behavior of a soccer player bouncing the ball. This method first detects the mesh separation degree of the interactive object and human body, and then divides the sequence into a series of fragments based on the consistency of separation degrees. In each fragment, we employ a deformation algorithm to map keyframe topology to other frames, to improve the compression ratio of the sequence. Our work can be used for the storage of mesh sequences and mobile applications by providing an approach for data compression.", acknowledgement = ack-nhfb, articleno = "2450027", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Bai:2024:ODN, author = "G. Mercy Bai and P. Venkadesh", title = "Optimized Deep Neuro-Fuzzy Network with {MapReduce} Architecture for Acute Lymphoblastic Leukemia Classification and Severity Analysis", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "03", pages = "??--??", month = may, year = "2024", DOI = "https://doi.org/10.1142/S0219467824500281", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Jun 5 09:06:55 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500281", abstract = "The most common life-threatening disease, acute lymphoblastic leukemia (ALL), can be lethal within a few weeks if untreated. The early detection and analysis of leukemia is a key dilemma in the field of disease diagnosis, and the methods available for the classification process are time-consuming. To overcome the issues, this paper develops a robust classification technique named Horse Herd Whale Optimization-enabled Deep Neuro-Fuzzy Network (HHWO-enabled DNFN method) for ALL classification and severity analysis using the MapReduce framework. The input image is first preprocessed and segmented, and the useful features necessary for improving the classification performance are extracted during the mapper phase, known as HHWO, which incorporates Horse Herd Optimization Algorithm (HOA) and Whale Optimization Algorithm (WOA). Finally, severity analysis of ALL is done to classify the levels of leukemia to offer optimal treatment. As a result, the developed method performed better than other existing methods, achieving superior performance with a greater testing accuracy of 0.959, sensitivity of 0.965, and specificity of 0.966, respectively.", acknowledgement = ack-nhfb, articleno = "2450028", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Joseph:2024:MFT, author = "Jovi Joseph and S. R. Sreela", title = "{MODCN}: Fine-Tuned Deep Convolutional Neural Network with {GAN} Deployed to Forecast Diabetic Eye Damage in Fundus Retinal Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "03", pages = "??--??", month = may, year = "2024", DOI = "https://doi.org/10.1142/S0219467824500293", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Jun 5 09:06:55 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500293", abstract = "Diabetic Retinopathy (DR) and Glaucoma are two of the most common causes of vision loss world-wide. However, it can be averted if therapy is begun early enough. In biomedical applications, the use of digital image processing has assisted in the automated identification of some ailments at an earlier stage. To make this prediction generally neural network classifier models were previously used, but these models have the drawback of being unable to detect multiple illnesses that occur in the eye at the same time and require a big database for successful classifier training. As a result, a model is needed to reliably distinguish DR and Glaucoma in diabetic individuals more accurately and with minimum dataset images. In this view, this study introduced Mayfly Optimized Deep Convolutional Network (MODCN) model for automated disease detection in the fundus retina images. In the MODCN model, the images are initially preprocessed, segmented at generator in the GAN model then a discriminator readily gives synthesis of real images of the fundus retina, thus a wide database has been created and considered as training images for the MODCN classifier. MODCN classifier has a modified high-density layer as a transition layer to avoid overfitting and the errors are minimized by tuning the hyperparameters using Mayfly Optimization Algorithm. After feature mapping, the classes normal, DR and Glaucoma are labeled and stored. At the testing stage, images are preprocessed, feature mapped and classified in the MODCN model. Thus, the proposed MODCN model detects multiple illness such as Diabetic Retinopathy and Glaucoma at the same time even with a small amount of database that performs a successful classifier training. This model is then evaluated and gives an accuracy of 99\% that was higher compared to previous models.", acknowledgement = ack-nhfb, articleno = "2450029", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Shanmugasundaram:2024:DAI, author = "Suresh Shanmugasundaram and Natarajan Palaniappan", title = "Detection Accuracy Improvement on One-Stage Object Detection Using {AP}-Loss-Based Ranking Module and {ResNet-152} Backbone", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "03", pages = "??--??", month = may, year = "2024", DOI = "https://doi.org/10.1142/S021946782450030X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Jun 5 09:06:55 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S021946782450030X", abstract = "Localization-loss and classification-loss are optimized at the same time to train the one-stage object detectors. Because of the large number of anchors, the severe foreground--background class disproportion causes significant classification-loss. This paper discusses using a ranking module instead of the classification module to mitigate this difficulty and also Average-Precision loss (AP-loss) is utilized on the ranking module. An optimization algorithm is used to make the AP-loss as effective as possible. Optimization algorithm blends the error-driven updating method of perceptron learning and the deep network backpropagation technique. This optimization algorithm handles the foreground--background class disproportion issues. One-stage detector with AP-loss and backbone with ResNet-152 attains improvement in the detection performance compared to the classification-losses-based detectors.", acknowledgement = ack-nhfb, articleno = "2450030", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Begum:2024:MDA, author = "Afiya Parveen Begum and Prabha Selvaraj", title = "Multiclass Diagnosis of {Alzheimer}'s Disease Analysis Using Machine Learning and Deep Learning Techniques", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "03", pages = "??--??", month = may, year = "2024", DOI = "https://doi.org/10.1142/S0219467824500311", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Jun 5 09:06:55 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500311", abstract = "Alzheimer's disease (AD) is a popular neurological disorder affecting a critical part of the world's population. Its early diagnosis is extremely imperative for enhancing the quality of patients' lives. Recently, improved technologies like image processing, artificial intelligence involving machine learning, deep learning, and transfer learning have been introduced for detecting AD. This review describes the contribution of image processing, feature extraction, optimization, and classification approach in AD recognition. It deeply investigates different methods adopted for multiclass diagnosis of AD. The paper further presents a brief comparison of existing AD studies in terms of techniques adopted, performance measures, classification accuracy, publication year, and datasets. It then summarizes the important technical barriers in reviewed works. This paper allows the readers to gain profound knowledge regarding AD diagnosis for promoting extensive research in this field.", acknowledgement = ack-nhfb, articleno = "2450031", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kumar:2024:CAP, author = "S. Sathish Kumar and An. Sigappi and G. Arun Sampaul Thomas and Y. Harold Robinson and S. P. Raja", title = "Classification and Analysis of Pistachio Species Through Neural Embedding-Based Feature Extraction and Small-Scale Machine Learning Techniques", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "03", pages = "??--??", month = may, year = "2024", DOI = "https://doi.org/10.1142/S0219467824500323", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Jun 5 09:06:55 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500323", abstract = "Pistachios are a tremendous source of fiber, protein, antioxidants, healthy fats, and other minerals like thiamine and vitamin B6. They may help people lose weight, lower cholesterol, and blood sugar levels, lead to better gut, eye, and blood vessel health. The two main varieties farmed and exported in Turkey are kirmizi and siirt pistachios. Understanding how to detect the type of pistachio is essential as it plays an important role in trade. In this study, it is aimed to classify these two types of pistachios and analyze the performance of the various small-scale machine learning algorithms. 2148 sample images for these two kinds of pistachios were considered for this study which includes 1232 of Kirmizi type and 916 of Siirt type. In order to evaluate the model fairly, stratified random sampling is applied on the dataset. For feature extraction, we used deep neural network-based embeddings to acquire the vector representation of images. The classification of pistachio species is then performed using a variety of small-scale machine learning algorithms$^{29, 31}$ that have been trained using these feature vectors. As a result of this study, the success rate obtained from Logistic Regression through features extracted from the penultimate layer of Painters network is 97.20\%. The performance of the models was evaluated through Class Accuracy, Precision, Recall, F1 Score, and values of Area under the curve (AUC). The outcomes show that the method suggested in this study may quickly and precisely identify different varieties of pistachios while also meeting agricultural production needs.", acknowledgement = ack-nhfb, articleno = "2450032", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Sau:2024:ORE, author = "Paresh Chandra Sau and Manish Gupta and Atul Bansal", title = "Optimized {ResUNet++}-Enabled Blood Vessel Segmentation for Retinal Fundus Image Based on Hybrid Meta-Heuristic Improvement", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "03", pages = "??--??", month = may, year = "2024", DOI = "https://doi.org/10.1142/S0219467824500335", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Jun 5 09:06:55 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500335", abstract = "In recent years, several studies have undergone automatic blood vessel segmentation based on unsupervised and supervised algorithms to reduce user interruption. Deep learning networks have been used to get highly accurate segmentation results. However, the incorrect segmentation of pathological information and low micro-vascular segmentation is considered the challenges present in the existing methods for segmenting the retinal blood vessel. It also affects different degrees of vessel thickness, contextual feature fusion in technique, and perception of details. A deep learning-aided method has been presented to address these challenges in this paper. In the first phase, the preprocessing is performed using the retinal fundus images employed by the black ring removal, LAB conversion, CLAHE-based contrast enhancement, and grayscale image. Thus, the blood vessel segmentation is performed by a new deep learning model termed optimized ResUNet++. As an improvement to this deep learning architecture, the activation function is optimized by the J-AGSO algorithm. The objective function for the optimized ResUNet++-based blood vessel segmentation is to minimize the binary cross-entropy loss function. Further, the post-processing of the images is carried out by median filtering and binary thresholding. By verifying the standard benchmark datasets, the proposed model outperforms and attains enhanced performance.", acknowledgement = ack-nhfb, articleno = "2450033", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Mulchandani:2024:EIE, author = "Mona Mulchandani and Pramod S. Nair", title = "{EBMICQL}: Improving Efficiency of Blockchain Miner Pools via Incremental and Continuous {$Q$}-Learning Framework", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "03", pages = "??--??", month = may, year = "2024", DOI = "https://doi.org/10.1142/S0219467824500347", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Jun 5 09:06:55 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/bitcoin.bib; https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500347", abstract = "Blockchain mining pools assist in reducing computational load on individual miner nodes via distributing mining tasks. This distribution must be done in a non-redundant manner, so that each miner is able to calculate block hashes with optimum efficiency. To perform this task, a wide variety of mining optimization methods are proposed by researchers, and most of them distribute mining tasks via statistical request processing models. These models segregate mining requests into non-redundant sets, each of which will be processed by individual miners. But this division of requests follows a static procedure, and does not consider miner specific parameters for set creation, due to which overall efficiency of the underlying model is limited, which reduces its mining performance under real-time scenarios. To overcome this issue, an Incremental & Continuous Q-Learning Framework for generation of miner-specific task groups is proposed in this text. The model initially uses a Genetic Algorithm (GA) method to improve individual miner performance, and then applies Q-Learning to individual mining requests. The Reason for selecting GA model is that it assists in maintaining better speed-to-power (S2P) ratio by optimization of miner resources that are utilized during computations. While, the reason for selecting Q-Learning Model is that it is able to continuously identify miners performance, and create performance-based mining pools at a per-miner level. Due to application of Q-Learning, the model is able to assign capability specific mining tasks to individual miner nodes. Because of this capability-driven approach, the model is able to maximize efficiency of mining, while maintaining its QoS performance. The model was tested on different consensus methods including Practical Byzantine Fault Tolerance Algorithm (PBFT), Proof-of-Work (PoW), Proof-of-Stake (PoS), and Delegated PoS (DPoS), and its performance was evaluated in terms of mining delay, miner efficiency, number of redundant calculations per miner, and energy efficiency for mining nodes. It was observed that the proposed GA based Q-Learning Model was able to reduce mining delay by 4.9\%, improve miners efficiency by 7.4\%, reduce number of redundant computations by 3.5\%, and reduce energy required for mining by 7.1\% when compared with various state-of-the-art mining optimization techniques. Similar performance improvement was observed when the model was applied on different blockchain deployments, thus indicating better scalability and deployment capability for multiple application scenarios.", acknowledgement = ack-nhfb, articleno = "2450034", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Muraleedharan:2024:CUN, author = "K. M. Muraleedharan and K. T. Bibish Kumar and Sunil John and R. K. Sunil Kumar", title = "Combined Use of Nonlinear Measures for Analyzing Pathological Voices", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "03", pages = "??--??", month = may, year = "2024", DOI = "https://doi.org/10.1142/S0219467824500359", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Jun 5 09:06:55 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500359", abstract = "Automatic voice pathology detection enables an objective assessment of pathologies that influence the voice production strategy. By utilizing the conventional pipeline model as well as the modern deep learning-centric end-to-end methodology, numerous pathological voice analyzing techniques have been developed. The conventional methodology is still a valid choice owing to the lack of enormous amounts of training data in the study region of pathological voice. In the meantime, obtaining higher precision, higher accuracy, and stability is still a complicated task. Therefore, by amalgamating the nonlinear measure, the pathological voices are analyzed to abate such risks. The viability of six nonlinear discriminating measures derived from the phase space realm, involving healthy and pathological voice signals, is studied in this work. The analyzed parameters are Singularity spectrum coefficients ($ \alpha_{\rm min}, \alpha_{\rm max}, \gamma_1 $ and $ \gamma_2$). Correlation entropy at optimum embedding dimension ($ K_{2m}$) and correlation dimension at optimum embedding dimension ($ D_{2m}$). Analyzing the pathological voices with better accuracy rates is the major objective of the proposed methodology. Here, the Support Vector Machine (SVM) was utilized as the classifier. Experimentations were performed on VOiceICarfEDerico (VOICED) databases subsuming 208 healthy, as well as pathological voices, amongst these 50 samples, were utilized. Here, the model obtained 97\% of accuracy with 99\% as of the classifier with Gaussian kernel function. Therefore, to differentiate normal as well as pathological subjects, the six proposed characteristics are highly beneficial; in addition, they will be supportive in pathology diagnosis.", acknowledgement = ack-nhfb, articleno = "2450035", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Liu:2024:ICL, author = "Fuxiang Liu and Chen Zang and Junqi Shi and Weiyu He and Yupeng Liang and Lei Li", title = "An Improved {COVID-19} Lung {X}-Ray Image Classification Algorithm Based on {ConvNeXt} Network", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "03", pages = "??--??", month = may, year = "2024", DOI = "https://doi.org/10.1142/S0219467824500360", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Jun 5 09:06:55 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500360", abstract = "Aiming at the new coronavirus that appeared in 2019, which has caused a large number of infected patients worldwide due to its high contagiousness, in order to detect the source of infection in time and cut off the chain of transmission, we developed a new Chest X-ray (CXR) image classification algorithm with high accuracy, simple operation and fast processing for COVID-19. The algorithm is based on ConvNeXt pure convolutional neural network, we adjusted the network structure and loss function, added some new Data Augmentation methods and introduced attention mechanism. Compared with other classical convolutional neural network classification algorithms such as AlexNet, ResNet-34, ResNet-50, ResNet-101, ConvNeXt-tiny, ConvNeXt-small and ConvNeXt-base, the improved algorithm has better performance on COVID dataset.", acknowledgement = ack-nhfb, articleno = "2450036", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Fauzi:2024:FBO, author = "Nurul Izzatie Husna Fauzi and Zalili Musa and and Fadhl Hujainah", title = "Feature-Based Object Detection and Tracking: a Systematic Literature Review", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "03", pages = "??--??", month = may, year = "2024", DOI = "https://doi.org/10.1142/S0219467824500372", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Jun 5 09:06:55 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500372", abstract = "Correct object detection plays a key role in generating an accurate object tracking result. Feature-based methods have the capability of handling the critical process of extracting features of an object. This paper aims to investigate object tracking using feature-based methods in terms of (1) identifying and analyzing the existing methods; (2) reporting and scrutinizing the evaluation performance matrices and their implementation usage in measuring the effectiveness of object tracking and detection; (3) revealing and investigating the challenges that affect the accuracy performance of identified tracking methods; (4) measuring the effectiveness of identified methods in terms of revealing to what extent the challenges can impact the accuracy and precision performance based on the evaluation performance matrices reported; and (5) presenting the potential future directions for improvement. The review process of this research was conducted based on standard systematic literature review (SLR) guidelines by Kitchenam's and Charters'. Initially, 157 prospective studies were identified. Through a rigorous study selection strategy, 32 relevant studies were selected to address the listed research questions. Thirty-two methods were identified and analyzed in terms of their aims, introduced improvements, and results achieved, along with presenting a new outlook on the classification of identified methods based on the feature-based method used in detection and tracking process.", acknowledgement = ack-nhfb, articleno = "2450037", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Li:2024:ATM, author = "Zhipeng Li and Jun Wang and Lijun Hua and Honghui Liu and Wenli Song", title = "Automatic Tracking Method for {$3$D} Human Motion Pose Using Contrastive Learning", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "03", pages = "??--??", month = may, year = "2024", DOI = "https://doi.org/10.1142/S0219467825500378", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Jun 5 09:06:55 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500378", abstract = "Automatic tracking of three-dimensional (3D) human motion pose has the potential to provide corresponding technical support in various fields. However, existing methods for tracking human motion pose suffer from significant errors, long tracking times and suboptimal tracking results. To address these issues, an automatic tracking method for 3D human motion pose using contrastive learning is proposed. By using the feature parameters of 3D human motion poses, threshold variation parameters of 3D human motion poses are computed. The golden section is introduced to transform the threshold variation parameters and extract the features of 3D human motion poses by comparing the feature parameters with the threshold of parameter variation. Under the supervision of contrastive learning, a constraint loss is added to the local--global deep supervision module of contrastive learning to extract local parameters of 3D human motion poses, combined with their local features. After normalizing the 3D human motion pose images, frame differences of the background image are calculated. By constructing an automatic tracking model for 3D human motion poses, automatic tracking of 3D human motion poses is achieved. Experimental results demonstrate that the highest tracking lag is 9\%, there is no deviation in node tracking, the pixel contrast is maintained above 90\% and only 6 sub-blocks have detail loss. This indicates that the proposed method effectively tracks 3D human motion poses, tracks all the nodes, achieves high accuracy in automatic tracking and produces good tracking results.", acknowledgement = ack-nhfb, articleno = "2550037", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kumar:2024:NIR, author = "Kattela Pavan Kumar and Matcha Venu Gopala Rao and and Moram Venkatanarayana", title = "A Novel Image Recovery from Moving Water Surface Using Multi-Objective Bispectrum Method", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "04", pages = "??--??", month = jul, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467824500384", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Oct 19 15:24:03 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500384", abstract = "Nowadays, the image degradation field suffers from several challenges while processing underwater color images including color distortion and image blurring due to the scattering media. Moreover, to get appropriate multi-frame super-resolution images, there is essential for recovering a better quantity of images. Traditionally, the shift among images is directly evaluated when considering the under-sampled Low-Resolution (LR) images. On the other hand, the high-frequency LR image faces unreliability owing to the aliasing consequences of sub-sampling, but it will also degrade the recovery accuracy. This task design implements a novel image recovery model from the moving water surface by adopting the multi-objective adaptive higher-order spectral analysis. Image pre-processing, lucky region selection, and image recovery are the three main phases of this model. The bicoherence method and dice coefficient method are adopted for performing the lucky region selection. Finally, the adoption of the multi-objective adaptive bispectra method is used for performing the image recovery from the moving water surface. The improved Adaptive Fitness-oriented Random number-based Galactic Swarm Optimization (AFR-GSO) algorithm is used for optimizing the constraints of the bispectrum method. The experimental results verify the enrichment of image quality by the proposed model over the existing techniques.", acknowledgement = ack-nhfb, articleno = "2450038", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Veling:2024:MDC, author = "Shripad S. Veling and T. B. Mohite-patil", title = "Multi-Disease Classification of Mango Tree Using Meta-Heuristic-Based Weighted Feature Selection and {LSTM} Model", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "04", pages = "??--??", month = jul, year = "2024", DOI = "https://doi.org/10.1142/S0219467824500396", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Oct 19 15:24:03 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500396", abstract = "Global food security can be influenced by the diseases in crop plants as several diseases straightforwardly influence the quality of the grains, vegetables, fruits, etc., which also results in affecting of agricultural productivity. Like other plants, the mango tree is also affected by several diseases, and also the identification of multi-disease classification with a single leaf is more complex, and also it is impossible to detect diseases with bare eyes. Based on the other plants, the mango tree is also affected by various diseases, which is more difficult to detect the disorders with bare eyes. It is error-prone, inconsistent, and unreliable. Here, the mango trees are affected during the production, and also affect the plant health regarding multi-diseases. When the plants are affected by the diseases, it may cause fewer amounts of productivity, as a result, impacting the economy. However, it is more critical to detect plant diseases with the large varieties of trees and plants. Various research tasks on deep learning approaches focus on identifying the diseases in plants including leaves and fruits. Thus, the main objective of this paper is to implement an effective and appropriate technique for diagnosing mango tree diseases and their symptoms through fruit and leaf images, and thus, there is a need for an appropriate system for cost-effective and early solutions to this problem. Hence, the main intention of this work is to implement an efficient and suitable technique for diagnosing mango tree diseases and also identify the symptoms through fruit and leaf images. Intending to overcome the existing challenges, there is a need for an appropriate system for achieving cost-effectiveness and also creating an early solution to resolve this problem. This paper intends to present novel deep learning models for mango tree multi-disease classification. Initially, the data collection is done for gathering the diseased parts of the mango tree in terms of leaf and fruit images. Then, the contrast enhancement of the images is performed by the ``Contrast-Limited Adaptive Histogram Equalization (CLAHE)''. For the deep feature extraction of leaf images, and fruit images, Convolutional Neural Network (CNN) is employed, and the features from both inputs are concatenated for further processing. Further, the weighted feature selection is adopted for selecting the most significant features by the Adaptive Squirrel-Grey Wolf Search Optimization (AS-GWSO). Enhanced ``Long Short Term Memory (LSTM)'' is applied in the classification part with parameter optimization using the same AS-GWSO for enhancing classification accuracy. At last, the results of the designed system on various mango tree diseases verify that the designed approach has yielded the highest accuracy by evaluating conventional approaches. Therefore, it would also alleviate and treat the affected mango leaf diseases accurately.", acknowledgement = ack-nhfb, articleno = "2450039", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Hamdi:2024:CAC, author = "Dhekra {El Hamdi} and Ines Elouedi and Ihsen Slim", title = "Computer-Aided Classification of Cell Lung Cancer Via {PET\slash CT} Images Using Convolutional Neural Network", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "04", pages = "??--??", month = jul, year = "2024", DOI = "https://doi.org/10.1142/S0219467824500402", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Oct 19 15:24:03 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500402", abstract = "Lung cancer is the leading cause of cancer-related death worldwide. Therefore, early diagnosis remains essential to allow access to appropriate curative treatment strategies. This paper presents a novel approach to assess the ability of Positron Emission Tomography/Computed Tomography (PET/CT) images for the classification of lung cancer in association with artificial intelligence techniques. We have built, in this work, a multi output Convolutional Neural Network (CNN) as a tool to assist the staging of patients with lung cancer. The TNM staging system as well as histologic subtypes classification were adopted as a reference. The VGG 16 network is applied to the PET/CT images to extract the most relevant features from images. The obtained features are then transmitted to a three-branch classifier to specify Nodal (N), Tumor (T) and histologic subtypes classification. Experimental results demonstrated that our CNN model achieves good results in TN staging and histology classification. The proposed architecture classified the tumor size with a high accuracy of 0.94 and the area under the curve (AUC) of 0.97 when tested on the Lung-PET-CT-Dx dataset. It also has yielded high performance for N staging with an accuracy of 0.98. Besides, our approach has achieved better accuracy than state-of-the-art methods in histologic classification.", acknowledgement = ack-nhfb, articleno = "2450040", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Prasad:2024:FSC, author = "Rajesh S. Prasad and Jayashree Rajesh Prasad and Bhushan S. Chaudhari and Nihar M. Ranjan and Rajat Srivastava", title = "{FCM} with Spatial Constraint Multi-Kernel Distance-Based Segmentation and Optimized Deep Learning for Flood Detection", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "04", pages = "??--??", month = jul, year = "2024", DOI = "https://doi.org/10.1142/S0219467824500414", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Oct 19 15:24:03 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500414", abstract = "Floods are the deadly and catastrophic disasters, causing loss of life and harm to assets, farmland, and infrastructure. To address this, it is necessary to devise and employ an effective flood management system that can immediately identify flood areas to initiate relief measures as soon as possible. Therefore, this research work develops an effective flood detection method, named Anti- Corona-Shuffled Shepherd Optimization Algorithm-based Deep Quantum Neural Network (ACSSOA-based Deep QNN) for identifying the flooded areas. Here, the segmentation process is performed using Fuzzy C-Means with Spatial Constraint Multi-Kernel Distance (MKFCM\_S) wherein the Fuzzy C-Means (FCM) is modified with Spatial Constraints Based on Kernel-Induced Distance (KFCM\_S). For flood detection, Deep QNN has been used wherein the training progression of Deep QNN is done using designed optimization algorithm, called ACSSOA. Besides, the designed ACSSOA is newly formed by the hybridization of Anti Corona Virus Optimization (ACVO) and Shuffled Shepherd Optimization Algorithm (SSOA). The devised method was evaluated using the Kerala Floods database, and it acquires the segmentation accuracy, testing accuracy, sensitivity, and specificity with highest values of 0.904, 0.914, 0.927, and 0.920, respectively.", acknowledgement = ack-nhfb, articleno = "2450041", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Khaki:2024:RCN, author = "Ali Khaki", title = "Robust Convolutional Neural Network Based on {UNet} for Iris Segmentation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "04", pages = "??--??", month = jul, year = "2024", DOI = "https://doi.org/10.1142/S0219467824500426", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Oct 19 15:24:03 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500426", abstract = "Nowadays, the iris recognition system is one of the most widely used and most accurate biometric systems. The iris segmentation is the most crucial stage of iris recognition system. The accurate iris segmentation can improve the efficiency of iris recognition. The main objective of iris segmentation is to obtain the iris area. Recently, the iris segmentation methods based on convolutional neural networks (CNNs) have been grown, and they have improved the accuracy greatly. Nevertheless, their accuracy is decreased by low-quality images captured in uncontrolled conditions. Therefore, the existing methods cannot segment low-quality images precisely. To overcome the challenge, this paper proposes a robust convolutional neural network (R-Net) inspired by UNet for iris segmentation. R-Net is divided into two parts: encoder and decoder. In this network, several layers are added to ResNet-34, and used in the encoder path. In the decoder path, four convolutions are applied at each level. Both help to obtain suitable feature maps and increase the network accuracy. The proposed network has been tested on four datasets: UBIRIS v2 (UBIRIS), CASIA iris v4.0 (CASIA) distance, CASIA interval, and IIT Delhi v1.0 (IITD). UBIRIS is a dataset that is used for low-quality images. The error rate (NICE1) of proposed network is 0.0055 on UBIRIS, 0.0105 on CASIA interval, 0.0043 on CASIA distance, and 0.0154 on IITD. Results show better performance of the proposed network compared to other methods.", acknowledgement = ack-nhfb, articleno = "2450042", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Jannu:2024:SAN, author = "Chaitanya Jannu and Sunny Dayal Vanambathina", title = "Shuffle Attention {$U$}-Net for Speech Enhancement in Time Domain", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "04", pages = "??--??", month = jul, year = "2024", DOI = "https://doi.org/10.1142/S0219467824500438", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Oct 19 15:24:03 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500438", abstract = "Over the past 10 years, deep learning has enabled significant advancements in the improvement of noisy speech. In an end-to-end speech enhancement, the deep neural networks transform a noisy speech signal to a clean speech signal in the time domain directly without any conversion or estimation of mask. Recently, the U-Net-based models achieved good enhancement performance. Despite this, some of them may neglect context-related information and detailed features of input speech in case of ordinary convolution. To address the above issues, recent studies have upgraded the performance of the model by adding various network modules such as attention mechanisms, long and short-term memory (LSTM). In this work, we propose a new U-Net-based speech enhancement model using a novel lightweight and efficient Shuffle Attention (SA), Gated Recurrent Unit (GRU), residual blocks with dilated convolutions. Residual block will be followed by a multi-scale convolution block (MSCB). The proposed hybrid structure enables the temporal context aggregation in time domain. The advantage of shuffle attention mechanism is that the channel and spatial attention are carried out simultaneously for each sub-feature in order to prevent potential noises while also highlighting the proper semantic feature areas by combining the same features from all locations. MSCB is employed for extracting rich temporal features. To represent the correlation between neighboring noisy speech frames, a two Layer GRU is added in the bottleneck of U-Net. The experimental findings demonstrate that the proposed model outperformed the other existing models in terms of short-time objective intelligibility (STOI), and perceptual evaluation of the speech quality (PESQ).", acknowledgement = ack-nhfb, articleno = "2450043", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Visalini:2024:DES, author = "K. Visalini and Saravanan Alagarsamy and S. P. Raja", title = "Detecting Epileptic Seizures Using Symplectic Geometry Decomposition-Based Features and {Gaussian} Deep {Boltzmann} Machines", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "04", pages = "??--??", month = jul, year = "2024", DOI = "https://doi.org/10.1142/S021946782450044X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Oct 19 15:24:03 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S021946782450044X", abstract = "Studies deem that about 1 percent of the human population is affected by epileptic seizures on a global scale. It is characterized as an undue neuronal discharge in the brain and degrades the quality of life of the patients to a large extent. Children being unaware of a sudden onset of seizures could be affected by severe injury or even mortality. Machine-learning-based epileptic seizure detection from EEG (Electro-Encephalogram) signals have always been a hot area of research. However, the majority of the research works rely on correlated non-linear features extracted from the EEG signals, causing a high-computational overhead, and challenging their application in real-time clinical diagnosis. This study proposes a robust seizure detection framework using Gaussian Deep Boltzmann Machine-based classifier and Symplectic Geometric Decomposition (SGD)-based features. The simplified eigenvalues derived through Symplectic Similarity Transform (SST) are employed as feature vectors for the classifier, eliminating the need for a deliberate feature extraction procedure. The study examines the transferability capability of the suggested framework in discriminating seizures in both neonates and pediatric subjects in unison, experimenting with classical annotated datasets. The model yielded a mean accuracy of about 97.91\% and an F1 Score of 0.935 in pediatric seizure detection, and mean sensitivity and specificity of 99.05\% and 98.28\%, in neonatal seizure detection tasks, respectively. Thus, the model can be deemed comparable to the available state-of-the-art seizure detection frameworks.", acknowledgement = ack-nhfb, articleno = "2450044", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Prashanthi:2024:HOB, author = "M. Prashanthi and M. Chandra Mohan", title = "Hybrid Optimization-Based Neural Network Classifier for Software Defect Prediction", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "04", pages = "??--??", month = jul, year = "2024", DOI = "https://doi.org/10.1142/S0219467824500451", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Oct 19 15:24:03 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500451", abstract = "The software is applied in various areas so the quality of the software is very important. The software defect prediction (SDP) is used to solve the software issues and enhance the quality. The robustness and reliability are the major concerns in the existing SDP approaches. Hence, in this paper, the hybrid optimization-based neural network (Optimized NN) is developed for the effective detection of the defects in the software. The two main steps involved in the Optimized NN-based SDP are feature selection and SDP utilizing Optimized NN. The data is fed forwarded to the feature selection module, where relief algorithm selects the significant features relating to the defect and no-defects. The features are fed to the SDP module, and the optimal tuning of NN classifier is obtained by the hybrid optimization developed by the integration of the social spider algorithm (SSA) and gray wolf optimizer (GWO). The comparative analysis of the developed prediction model reveals the effectiveness of the proposed method that attained the maximum accuracy of 93.64\%, maximum sensitivity of 95.14\%, maximum specificity of 99\%, maximum $ F_1$-score of 93.53\%, and maximum precision of 99\% by considering the $K$-fold.", acknowledgement = ack-nhfb, articleno = "2450045", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kulkarni:2024:HBE, author = "Girish Kulkarni and Chiranjeevi Manike", title = "Heuristic-Based Ensemble Model Selection Strategy with Parameter Tuning for Optimal {{\em Diabetes mellitus\/}} Prediction", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "04", pages = "??--??", month = jul, year = "2024", DOI = "https://doi.org/10.1142/S0219467824500463", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Oct 19 15:24:03 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500463", abstract = "Diabetes is a terrible health situation characterized by high-rise blood glucose levels. If it is not predicted at an early stage, then it generates the problems in the human body like kidney failure or premature death, and stroke. Controlling blood glucose levels provides patients with helpful dietary recommendations, which are critical components of diabetes management. In the past decades, diverse conventional approaches have been executed to predict the beginning stages of diabetes mellitus depending on physical and substance tests. Still, developing a new framework that can effectively diagnose diabetes mellitus-affected patients is required. To this end, the major target of this task is to predict diabetes mellitus with an advanced accuracy rate with the help of the Heuristic-based Ensemble Model Selection Strategy (H-EMSS). In the data collection phase, the Pima Indian Diabetes dataset (PID) is taken from the storage area of UCI. The data cleaning is performed in the pre-processing stage, which is the technique of removing or fixing, corrupted, incorrect, duplicate, incomplete data, or incorrectly formatted, inside a dataset. Then, the diabetes prediction is accomplished by the H-EMSS. Here, 10 base learners like Naive Bayes (NB), Convolutional Neural Network (CNN), Linear Regression (LR), Deep Neural Network (DNN), Support Vector Machine (SVM), Artificial Neural Network (ANN), Decision Tree (DT), Random Forest (RF), Auto Encoder (AE) and Recurrent Neural Network (RNN) are considered. From these, three classifiers are optimally selected by the Modified Scalar Factor-based Elephant Herding Optimization (MSF-EHO), so that the prediction rate will be high. The suggested methodology's efficacy is also compared and analyzed, with the findings demonstrating the recommended model's superiority. The overall evaluation is that the Root Mean Square Error (RMSE) of the designed Modified Scalar Factor-based Elephant Herding Optimization-Heuristic-based Ensemble Model Selection Strategy (MSF-EHO-H-EMSS) attains 4.601\% and also the Mean Absolute Error (MAE) on the designed method achieves 0.99\%. Thus, the given outcomes of the designed method revealed that it achieves elevated performance than the other existing techniques regarding diverse error metrics.", acknowledgement = ack-nhfb, articleno = "2450046", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Dharwadkar:2024:HEC, author = "Nagaraj V. Dharwadkar and Ashutosh A. Lonikar and and Mufti Mahmud", title = "High Embedding Capacity Color Image Steganography Scheme Using Pixel Value Differencing and Addressing the Falling-Off Boundary Problem", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "04", pages = "??--??", month = jul, year = "2024", DOI = "https://doi.org/10.1142/S0219467824500475", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Oct 19 15:24:03 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500475", abstract = "In this paper, we changed the methodology for pixel value differencing. The proposed method work on RGB color images improves the existing PVD technique in terms of embedding capacity and overcomes the issue of falling off boundaries in the traditional PVD technique, and provides security to the secret message from histogram quantization attack. Color images are composed of three different color channels (red, green, and blue), so we cannot apply the traditional pixel value differencing algorithm to them. Due to that, the proposed technique divides the RGB photograph in red, blue, and green channels. Following that the modified pixel value differencing algorithm is employed to all successive pixels of color channels. We get the total embedding capacity by adding the embedding capacities of each color component. After embedding the data, we concatenate the color channels to get the stegoimage. On a series of color images, we tested our pixel value differencing approach and found that the stego-picture's visual excellence and payload capacity were reasonable. The variation in histogram between the stego and cover photographs was minor, making it resistant to histogram quantization attacks, and the suggested approach also solves the issue of falling off the boundary.", acknowledgement = ack-nhfb, articleno = "2450047", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Lavanya:2024:ECM, author = "V. Lavanya and P. Chandra Sekhar", title = "Efficient Cybersecurity Model Using Wavelet Deep {CNN} and Enhanced Rain Optimization Algorithm", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "04", pages = "??--??", month = jul, year = "2024", DOI = "https://doi.org/10.1142/S0219467824500487", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Oct 19 15:24:03 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500487", abstract = "Cybersecurity has received greater attention in modern times due to the emergence of IoT (Internet-of-Things) and CNs (Computer Networks). Because of the massive increase in Internet access, various malicious malware have emerged and pose significant computer security threats. The numerous computing processes across the network have a high risk of being tampered with or exploited, which necessitates developing effective intrusion detection systems. Therefore, it is essential to build an effective cybersecurity model to detect the different anomalies or cyber-attacks in the network. This work introduces a new method known as {\em Wavelet Deep Convolutional Neural Network (WDCNN)\/} to classify cyber-attacks. The presented network combines WDCNN with Enhanced Rain Optimization Algorithm (EROA) to minimize the loss in the network. This proposed algorithm is designed to detect attacks in large-scale data and reduces the complexities of detection with maximum detection accuracy. The proposed method is implemented in PYTHON. The classification process is completed with the help of the two most famous datasets, KDD cup 1999 and CICMalDroid 2020. The performance of WDCNN\_EROA can be assessed using parameters like specificity, accuracy, precision F-measure and recall. The results showed that the proposed method is about 98.72\% accurate for the first dataset and 98.64\% for the second dataset.", acknowledgement = ack-nhfb, articleno = "2450048", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Wei:2024:AIS, author = "Yanxi Wei", title = "Artistic Image Style Transfer Based on {CycleGAN} Network Model", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "04", pages = "??--??", month = jul, year = "2024", DOI = "https://doi.org/10.1142/S0219467824500499", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Sat Oct 19 15:24:03 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500499", abstract = "With the development of computer technology, image stylization has become one of the hottest technologies in image processing. To optimize the effect of artistic image style conversion, a method of artistic image style conversion optimized by attention mechanism is proposed. The CycleGAN network model is introduced, and then the generator is optimized by the attention mechanism. Finally, the application effect of the improved model is tested and analyzed. The results show that the improved model tends to be stable after 40 iterations, the loss value remains at 0.3, and the PSNR value can reach up to 15. From the perspective of the generated image effect, the model has a better visual effect than the CycleGAN model. In the subjective evaluation, 63 people expressed satisfaction with the converted artistic image. As a result, the cyclic generative adversarial network model optimized by the attention mechanism improves the clarity of the generated image, enhances the effect of blurring the target boundary contour, retains the detailed information of the image, optimizes the image stylization effect, and improves the image quality of the method and application value of the processing field.", acknowledgement = ack-nhfb, articleno = "2450049", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Sasikala:2024:ECD, author = "P. Sasikala and L. Mary Immaculate Sheela", title = "An Efficient {COVID-19} Disease Outbreak Prediction Using {BI-SSOA-TMLPNN} and {ARIMA}", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "06", pages = "??--??", month = nov, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467823400119", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 21 07:12:35 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823400119", abstract = "Globally, people's health and wealth are affected by the outbreak of the corona virus. It is a virus, which infects from common fever to severe acute respiratory syndrome. It has the potency to transmit from one person to another. It is established that this virus spread is augmenting speedily devoid of any symptoms. Therefore, the prediction of this outbreak situation with mathematical modelling is highly significant along with necessary. To produce informed decisions along with to adopt pertinent control measures, a number of outbreak prediction methodologies for COVID-19 are being utilized by officials worldwide. An effectual COVID-19 outbreaks' prediction by employing Squirrel Search Optimization Algorithm centric Tanh Multi-Layer Perceptron Neural Network (MLPNN) (SSOA-TMLPNN) along with Auto-Regressive Integrated Moving Average (ARIMA) methodologies is proposed here. Initially, from the openly accessible sources, the input time series COVID-19 data are amassed. Then, pre-processing is performed for better classification outcomes after collecting the data. Next, by utilizing Sine-centered Empirical Mode Decomposition (S-EMD) methodology, the data decomposition is executed. Subsequently, the data are input to the Brownian motion Intense (BI) - SSOA-TMLPNN classifier. In this, the diseased, recovered, and death cases in the country are classified. After that, regarding the time-series data, the corona-virus's future outbreak is predicted by employing ARIMA. Afterwards, data visualization is conducted. Lastly, to evaluate the proposed model's efficacy, its outcomes are analogized with certain prevailing methodologies. The obtained outcomes revealed that the proposed methodology surpassed the other existing methodologies.", acknowledgement = ack-nhfb, articleno = "2340011", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Shalini:2024:EJF, author = "L Shalini and K Vijayakumar", title = "An Efficient {JSH-FCM}-Based Thyroid Disease Detection Using {ASH-ANN} with Stage Classification via a Fuzzy Rule-Based Approach", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "06", pages = "??--??", month = nov, year = "2024", DOI = "https://doi.org/10.1142/S0219467823400120", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 21 07:12:35 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823400120", abstract = "One of the most misunderstood and undiagnosed diseases is termed Thyroid Disease (TD), which is a subset of endocrinology. It emerges at the edge of the thyroid gland due to the abnormal development of thyroid tissue. Owing to the lack of awareness and early diagnosis, TD is a critical problem in underdeveloped countries. For TD diagnosis, various theoretical works have been introduced; still, in the early diagnosis of TD, accurate prediction of the thyroid data is a significant problem. Thus, by utilizing Altered SigHyper activation-centric Artificial Neural Network (ANN) (ASH-ANN) with various stage classifications, an effectual Jaccard Similarity and He-initialization induced Fuzzy C-Means (FCM) (JSH-FCM) clustering-centric TD detection system is proposed by means of a fuzzy rule-centric methodology. Initially, for accurate detection, the thyroid dataset is gathered and the data is pre-processed. Next, by JSH-FCM clustering, the age-centric clustering is carried out. After that, by utilizing Pearson Correlation-amalgamated Principal Component Analysis ((PC)$^2$ A), Feature Extraction (FE) and feature selection is conducted. Moreover, to detect the TD kind, an ASH-ANN classifier is wielded. Finally, for differentiating the stages of TD, the fuzzy rule is employed. The experimental outcomes depict that the proposed system achieved superior performance with an accuracy of 97.32% when weighed against the prevailing system; in addition, the stages of TD are differentiated precisely.", acknowledgement = ack-nhfb, articleno = "2340012", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Praveen:2024:CKD, author = "S Phani Praveen and Veerapaneni Esther Jyothi and Chokka Anuradha and K Venugopal and Vahiduddin Shariff and S Sindhura", title = "Chronic Kidney Disease Prediction Using {ML}-Based Neuro-Fuzzy Model", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "06", pages = "??--??", month = nov, year = "2024", DOI = "https://doi.org/10.1142/S0219467823400132", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 21 07:12:35 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823400132", abstract = "Nowadays, in most countries, the most dangerous and life threatening infection is Chronic Kidney Disease (CKD). A progressive malfunctioning of the kidneys and less effectiveness of the kidney are considered CKD. CKD can be a life threatening disease if it continues for longer period of time. Prediction of chronic disease in early stage is very crucial so that sustainable care of the patient is taken to prevent menacing situations. Most of the developing countries are being affected by this deadly disease and treatment applied for this disease is also very expensive, here in this paper, a Machine Learning (ML)-positioned approach called Neuro-Fuzzy model is used for prediction belonging to CKD. Based on the image processing technique, fibrosis proportions are detected in the kidney tissues. It also builds a system for identifying and detection of CKD at an early stage. Neuro-Fuzzy model is based on ML which can detect risk of CKD patients. Compared with other conventional methods such as Support Vector Machine (SVM) and K-Nearest Neighbor (KNN), the proposed method of this paper --- ML-based Neuro-Fuzzy logic method --- obtained 97% accuracy in CKD prediction. This method can be evaluated based on various parameters such as Precision, Accuracy, Recall and F1-Score in CKD prediction. From the results, the patients having high risk of chronic disease can be predicted.", acknowledgement = ack-nhfb, articleno = "2340013", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Bhan:2024:CMS, author = "Anupama Bhan and Partha Sarathi Mangipudi and and Ayush Goyal", title = "Cardiac {MRI} Segmentation Using Efficient {ResNeXT-50}-Based {IEI} Level Set and Anisotropic Sigmoid Diffusion Algorithms", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "06", pages = "??--??", month = nov, year = "2024", DOI = "https://doi.org/10.1142/S0219467823400144", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 21 07:12:35 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467823400144", abstract = "Endocardial and epicardial border identification has been of extensive interest in cardiac Magnetic Resonance Images (MRIs). It is a difficult job to segment the epicardium and endocardium accurately and automatically from cardiac MRI owing to the cardiac tissues' complexity even though the prevailing Deep Learning (DL) methodologies had attained significant success in medical imaging segmentation. Hence, by employing effectual ResNeXT-50-centric Inverse Edge Indicator Level Set (IEILS) and anisotropic sigmoid diffusion algorithms, this system has proposed cardiac MRI segmentation. The work has endured some function for an effectual partition of epicardium and endocardium. Initially, by employing the Truncated Kernel Function (TK)-Trilateral Filter, the noise removal function is executed on the input cardiac MRI. Next, by wielding the ResNeXT-50 IEILS, the Left and Right Ventricular (LV/RV) regions are segmented. The epicardium and endocardium are segmented by the ASD algorithm once the LV/RV is separated from the Left Ventricle (LV) region. Here, the openly accessible Sunnybrook and the Right Ventricle (RV) datasets are wielded. Then, the prevailing state-of-art algorithms are analogized to the outcomes achieved by the proposed framework. Regarding accuracy, sensitivity, and specificity, the proposed methodology executed the cardiac MRI segmentation process precisely along with the other surpassed state-of-the-art methodologies.", acknowledgement = ack-nhfb, articleno = "2340014", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Iqbal:2024:RND, author = "Md. Asim Iqbal and K. Devarajan and Syed Musthak Ahmed", title = "{RDN-NET}: A Deep Learning Framework for Asthma Prediction and Classification Using Recurrent Deep Neural Network", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "06", pages = "??--??", month = nov, year = "2024", DOI = "https://doi.org/10.1142/S0219467824500505", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 21 07:12:35 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500505", abstract = "Asthma is the one of the crucial types of disease, which causes the huge deaths of all age groups around the world. So, early detection and prevention of asthma disease can save numerous lives and are also helpful to the medical field. But the conventional machine learning methods have failed to detect the asthma from the speech signals and resulted in low accuracy. Thus, this paper presented the advanced deep learning-based asthma prediction and classification using recurrent deep neural network (RDN-Net). Initially, speech signals are preprocessed by using minimum mean-square-error short-time spectral amplitude (MMSE-STSA) method, which is used to remove the noises and enhances the speech properties. Then, improved Ripplet-II Transform (IR2T) is used to extract disease-dependent and disease-specific features. Then, modified gray wolf optimization (MGWO)-based bio-optimization approach is used to select the optimal features by hunting process. Finally, RDN-Net is used to predict the asthma disease present from speech signal and classifies the type as either wheeze, crackle or normal. The simulations are carried out on real-time COSWARA dataset and the proposed method resulted in better performance for all metrics as compared to the state-of-the-art approaches.", acknowledgement = ack-nhfb, articleno = "2450050", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Mundada:2024:MIB, author = "Kapil Mundada and Jayant Kulkarni", title = "{MRI} Image-Based Automatic Segmentation and Classification of Brain Tumor and Swelling Using Novel Methodologies", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "06", pages = "??--??", month = nov, year = "2024", DOI = "https://doi.org/10.1142/S0219467824500517", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 21 07:12:35 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500517", abstract = "In the medical image analysis field, brain tumors (BTs) classification is a complicated process. For effortlessly detecting the tumor devoid of any surgical interference, the radiologists are aided with automated along with computerized technology. Currently, in the field of medical image processing along with analysis, admirable progress has been made by deep learning (DL) methodologies. In medical fields, for resolving several issues, huge attention was paid to DL techniques. For automation of several performed by radiologists like (1) lesion detection, (2) segmentation, (3) classification, (4) monitoring, along with (5) also prediction of treatment response that is not achievable without software, DL might be wielded. Nevertheless, classifying BTs by utilizing magnetic resonance imaging (MRI) has various complications like the difficulty of brain structure along with the intertwining of tissues in it; additionally, the brain's higher density nature also makes the BT Classification (BTC) process quite complex. Therefore, by utilizing novel systems, MRI-centric Automatic segmentation together with classifications of BT and swelling have been proposed to overcome the aforementioned issues. The proposed methodology underwent various operations to detect BTs effectively. Initially, by utilizing the Range-centric Otsu's Thresholding (ROT) algorithm, the skull stripping (SS) is conducted. After that, by performing contrast enhancement (CE) along with noise removal, the skull-stripped images are pre-processed. Next, by employing the Rectilinear Watershed Segmentation (RWS) algorithm, the tumor or swelling areas are segmented. Afterward, to obtain the precise tumor or swelling region, the morphological operations are executed on the segmented areas; subsequently, the desired along with relevant features are extracted. Lastly, the features being extracted are inputted to the classifier termed Uniform Convolution neural network (UCNN). The tumor tissues along with the swelling tissues are classified precisely in the classification phase. Here, the openly accessible BT Image Segmentation Benchmark (BRATS) datasets are utilized. Then, the outcomes obtained are analogized with prevailing methodologies. The experiential outcomes displayed that the BTC is performed by the proposed model with a higher accuracy rate; thus, outshined the other prevailing models.", acknowledgement = ack-nhfb, articleno = "2450051", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Mangai:2024:TSS, author = "P. Mangai and M. Kalaiselvi Geetha and G. Kumaravelan", title = "Two-Stream Spatial--Temporal Feature Extraction and Classification Model for Anomaly Event Detection Using Hybrid Deep Learning Architectures", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "06", pages = "??--??", month = nov, year = "2024", DOI = "https://doi.org/10.1142/S0219467824500529", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 21 07:12:35 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500529", abstract = "Identifying events using surveillance videos is a major source that reduces crimes and illegal activities. Specifically, abnormal event detection gains more attention so that immediate responses can be provided. Video processing using conventional techniques identifies the events but fails to categorize them. Recently deep learning-based video processing applications provide excellent performances however the architecture considers either spatial or temporal features for event detection. To enhance the detection rate and classification accuracy in abnormal event detection from video keyframes, it is essential to consider both spatial and temporal features. Earlier approaches consider any one of the features from keyframes to detect the anomalies from video frames. However, the results are not accurate and prone to errors sometimes due to video environmental and other factors. Thus, two-stream hybrid deep learning architecture is presented to handle spatial and temporal features in the video anomaly detection process to attain enhanced detection performances. The proposed hybrid models extract spatial features using YOLO-V4 with VGG-16, and temporal features using optical FlowNet with VGG-16. The extracted features are fused and classified using hybrid CNN-LSTM model. Experimentation using benchmark UCF crime dataset validates the proposed model performances over existing anomaly detection methods. The proposed model attains maximum accuracy of 95.6% which indicates better performance compared to state-of-the-art techniques.", acknowledgement = ack-nhfb, articleno = "2450052", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Jannu:2024:SAB, author = "Chaitanya Jannu and Sunny Dayal Vanambathina", title = "Self-Attention-Based Convolutional {GRU} for Enhancement of Adversarial Speech Examples", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "06", pages = "??--??", month = nov, year = "2024", DOI = "https://doi.org/10.1142/S0219467824500530", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 21 07:12:35 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500530", abstract = "Recent research has identified adversarial examples which are the challenges to DNN-based ASR systems. In this paper, we propose a new model based on Convolutional GRU and Self-attention U-Net called GRU-U-Net_{AT} to improve adversarial speech signals. To represent the correlation between neighboring noisy speech frames, a two-Layer GRU is added in the bottleneck of U-Net and an attention gate is inserted in up-sampling units to increase the adversarial stability. The goal of using GRU is to combine the weights sharing technique with the use of gates to control the flow of data across multiple feature maps. As a result, it outperforms the original 1D convolution used in U-Net_{AT} . Especially, the performance of the model is evaluated by explainable speech recognition metrics and its performance is analyzed by the improved adversarial training. We used adversarial audio attacks to perform experiments on automatic speech recognition (ASR). We saw (i) the robustness of ASR models which are based on DNN can be improved using the temporal features grasped by the attention-based GRU network; (ii) through adversarial training, including some additive adversarial data augmentation, we could improve the generalization power of Automatic Speech Recognition models which are based on DNN. The word-error-rate (WER) metric confirmed that the enhancement capabilities are better than the state-of-the-art U-Net_{AT} . The reason for this enhancement is the ability of GRU units to extract global information within the feature maps. Based on the conducted experiments, the proposed GRU-U-Net_{AT} increases the score of Speech Transmission Index (STI), Perceptual Evaluation of Speech Quality (PESQ), and the Short-term Objective Intelligibility (STOI) with adversarial speech examples in speech enhancement.", acknowledgement = ack-nhfb, articleno = "2450053", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Shanmugasundaram:2024:IBB, author = "Suresh Shanmugasundaram and Natarajan Palaniappan", title = "Improvement of Bounding Box and Instance Segmentation Accuracy Using {ResNet-152 FPN} with Modulated Deformable {ConvNets v2} Backbone-based Mask Scoring {R-CNN}", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "06", pages = "??--??", month = nov, year = "2024", DOI = "https://doi.org/10.1142/S0219467824500542", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 21 07:12:35 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500542", abstract = "A challenging task is to make sure that the deep learning network learns prediction accuracy by itself. Intersection-over-Union (IoU) amidst ground truth and instance mask determines mask quality. There is no relationship between classification score and mask quality. The mission is to investigate this problem and learn the predicted instance mask's accuracy. The proposed network regresses the MaskIoU by comparing the predicted mask and the respective instance feature. The mask scoring strategy determines the disorder among mask score and mask quality, then adjusts the parameters accordingly. Adaptation ability to the object's geometric variations decides deformable convolutional network's performance. Using increased modeling power and stronger training, focusing ability on pertinent image regions is improved by a reformulated Deformable ConvNets. The introduction of modulation technique, which broadens the deformation modeling scope, and the integration of deformable convolution comprehensively within the network enhance the modeling power. The features which resemble region-based convolutional neural network (R-CNN) feature's classification capability and its object focus are learned by the network with the help of feature mimicking scheme of DCNv2. Feature mimicking scheme of DCNv2 guides the network training to efficiently control this enhanced modeling capability. The backbone of the proposed Mask Scoring R-CNN network is designed with ResNet-152 FPN and DCNv2 network. The proposed Mask Scoring R-CNN network with DCNv2 network is also tested with other backbones ResNet-50 and ResNet-101. Instance segmentation and object detection on COCO benchmark and Cityscapes dataset are achieved with top accuracy and improved performance using the proposed network.", acknowledgement = ack-nhfb, articleno = "2450054", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Palanimeera:2024:YPR, author = "J. Palanimeera and K. Ponmozhi", title = "Yoga Posture Recognition by Learning Spatial-Temporal Feature with Deep Learning Techniques", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "06", pages = "??--??", month = nov, year = "2024", DOI = "https://doi.org/10.1142/S0219467824500554", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 21 07:12:35 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500554", abstract = "Yoga posture recognition remains a difficult issue because of crowded backgrounds, varied settings, occlusions, viewpoint alterations, and camera motions, despite recent promising advances in deep learning. In this paper, the method for accurately detecting various yoga poses using DL (Deep Learning) algorithms is provided. Using a standard RGB camera, six yoga poses --- Sukhasana, Kakasana, Naukasana, Dhanurasana, Tadasana, and Vrikshasana --- were captured on ten people, five men and five women. In this study, a brand-new DL model is presented for representing the spatio-temporal (ST) variation of skeleton-based yoga poses in movies. It is advised to use a variety of representation learners to pry video-level temporal recordings, which combine spatio-temporal sampling with long-range time mastering to produce a successful and effective training approach. A novel feature extraction method using Open Pose is described, together with a DenceBi-directional LSTM network to represent spatial-temporal links in both the forward and backward directions. This will increase the efficacy and consistency of modeling long-range action detection. To improve temporal pattern modeling capability, they are stacked and combined with dense skip connections. To improve performance, two modalities from look and motion are fused with a fusion module and compared to other deep learning models are LSTMs including LSTM, Bi-LSTM, Res-LSTM, and Res-BiLSTM. Studies on real-time datasets of yoga poses show that the suggested DenseBi-LSTM model performs better and yields better results than state-of-the-art techniques for yoga pose detection.", acknowledgement = ack-nhfb, articleno = "2450055", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Anonymous:2024:AIV, author = "Anonymous", title = "Author Index (Volume 24)", journal = j-INT-J-IMAGE-GRAPHICS, volume = "24", number = "06", pages = "??--??", month = nov, year = "2024", DOI = "https://doi.org/10.1142/S0219467824990018", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 21 07:12:35 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824990018", acknowledgement = ack-nhfb, articleno = "2499001", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Bania:2025:ABM, author = "Rubul Kumar Bania and Anindya Halder", title = "Automatic Breast Mass Lesion Detection in Mammogram Image", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "01", pages = "??--??", month = jan, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467824500566", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jan 21 06:52:13 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500566", abstract = "Mammography imaging is one of the most successful techniques for breast cancer screening and detecting breast lesions. Detection of the Region of Interest (ROI) (where the possible abnormalities could be present) is the backbone for the success of any Computer-Aided Detection or Diagnosis (CADx) system. In this paper, to assist the CADx system, one computational model is proposed to detect breast mass lesions from mammogram images. At the beginning of the process, pectoral muscles from the mammograms are removed as a pre-processing step. Then by applying an automatic thresholding scheme with the required image processing techniques, different regions of breast tissues are ranked to detect the possible suspected region to refine the further segmentation task. One seeded region growing approach is proposed with an automatic seed selection criterion to detect the suspected region to segment the ROI. The proposed model has very less user intervention as maximum of the parameters are computed automatically. To evaluate the performance of the proposed model, it is compared with four different methods with six different evaluation metrics viz., {\em Jaccard\/} & {\em Dice\/} co-efficient, relative error, segmentation accuracy, error and Fowlkes--Mallows index (FMI). On the proposed model, 57 mammogram images are tested, consisting of four different cases that are collected from the publicly available benchmark database. The qualitative and quantitative analyses are performed to evaluate the proposed model. The best dice co-efficient, Jaccard co-efficient, accuracy, error and FMI values observed are 0.9506, 0.9471, 95.62\%, 4.38\% and 0.932, respectively. The superiority of the model over six state-of-the-art compared methods is well evident from the experimental results.", acknowledgement = ack-nhfb, articleno = "2450056", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Ashwini:2025:NSI, author = "G. Ashwini and T. Ramashri and Mohammad Rasheed Ahmed", title = "{Noise2Split} --- Single Image Denoising Via Single Channeled Patch-Based Learning", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "01", pages = "??--??", month = jan, year = "2025", DOI = "https://doi.org/10.1142/S0219467824500578", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jan 21 06:52:13 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500578", abstract = "The prominence and popularity of Image Denoising in medical image processing has been obvious since its early conception. Medical Image Denoising is primarily a significant pre-processing method for further image processing steps in various fields. Its ability to speed up the diagnosis by enhancing the sensory quality of noisy images is proven to be working in most of the cases. The efficiency of the deep neural networks for Medical Image Denoising has been well proven traditionally. Both noisy and clean images are equal requirements in most of these training methods. However, it is not always possible to procure clean images for various applications such as Dynamic Imaging, Computed Tomography, Magnetic Resonance Imaging, and Camera Photography due to the inevitable presence of naturally occurring noisy signals which are intrinsic to the images. There have been self-supervised single Image Denoising methods proposed recently. Being inspired by these methods, taking this a step further, we propose a novel and better denoising method for single images by training the learning model on each of the channels of the input data, which is termed as ``Noise2Split''. It ultimately proves to reduce the noise granularly in each channel, pixel by pixel, by using Single Channeled Patch-Based (SCPB) learning, which is found to be resulting in a better performance. Further, to obtain optimum results, the method leverages BRISQUE image quality assessment. The model is demonstrated on X-ray, CT, PET, Microscopy, and real-world noisy images.", acknowledgement = ack-nhfb, articleno = "2450057", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kiruthika:2025:NEB, author = "K. Kiruthika and Rashmita Khilar", title = "Novel Enrichment of Brightness-Distorted Chest {X}-Ray Images Using Fusion-Based Contrast-Limited Adaptive Fuzzy Gamma Algorithm", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "01", pages = "??--??", month = jan, year = "2025", DOI = "https://doi.org/10.1142/S021946782450058X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jan 21 06:52:13 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S021946782450058X", abstract = "As innovations for image handling, image enrichment (IE) can give more effective information and image compression can decrease memory space. IE plays a vital role in the medical field for which we have to use a noiseless image. IE applies to all areas of understanding and analysis of images. This paper provides an innovative algorithm called contrast-limited adaptive fuzzy gamma (CLAFG) for IE using chest X-ray (CXR) images. The image dissimilarity is enriched by computing several histograms and membership planes. The proposed algorithm comprises various steps. Firstly, CXR is separated into contextual region (CR). Secondly, the cliplimit, a threshold value which alters the dissimilarity of the CXR and applies it to the histogram which, is generated by CR and then applies the fuzzification technique via the membership plane to the CXR. Thirdly, the clipped histograms are performed in two ways, i.e. it is merged using bi-cubic interpolation techniques and it is modified with membership function. Finally, the resulting output from bi-cubic interpolation and membership function are fond of using upgrade contemplate standard methods for a richer CXR image.", acknowledgement = ack-nhfb, articleno = "2450058", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Ramamoorthy:2025:ECM, author = "Hariharan Ramamoorthy and Mohan Ramasundaram and S. P. Raja and Krunal Randive", title = "An Efficient Classification of Multiclass Brain Tumor Image Using Hybrid Artificial Intelligence with Honey Bee Optimization and Probabilistic {U-RSNet}", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "01", pages = "??--??", month = jan, year = "2025", DOI = "https://doi.org/10.1142/S0219467824500591", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jan 21 06:52:13 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500591", abstract = "The life of the human beings are considered as the most precious and the average life time has reduced from 75 to 50 age over the past two decades. This reduction of average life time is due to various health hazards namely cancer and many more. The brain tumor ranks among the top ten most common source of demise. Although brain tumors are not the leading cause of death globally, 40\% of other cancers (such as breast or lung cancers) metastasize to the brain and become brain tumors. Despite being the gold norm for tumor diagnosis, a biopsy has a number of drawbacks, including inferior sensitivity/specificity, and menace when performing the biopsy, and lengthy wait times for the results. This work employs artificial intelligence integrated with the honey bee optimization (HBO) in detecting the brain tumor with high level of execution in terms of accuracy, recall, precision, {\em F\/} 1 score and Jaccard index when compared to the deep learning algorithms of long short term memory networks (LSTM), convolutional neural networks, generative adversarial networks, recurrent neural networks, and deep belief networks. In this work, to enhance the level of prediction, the image segmentation methodology is performed by the probabilistic U-RSNet. This work is analyzed employing the BraTS 2020, BraTS 2021, and OASIS dataset for the vital parameters like accuracy, precision, recall, {\em F\/} 1 score, Jaccard index and PPV.", acknowledgement = ack-nhfb, articleno = "2450059", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Mao:2025:CCT, author = "Chunxia Mao and Jun Li and Tao Hu and Xuanyu Zhao", title = "{CMVT}: {ConVit} Transformer Network Recombined with Convolutional Layer", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "01", pages = "??--??", month = jan, year = "2025", DOI = "https://doi.org/10.1142/S0219467824500608", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jan 21 06:52:13 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500608", abstract = "Vision transformers are deep neural networks applied to image classification based on a self-attention mechanism and can process data in parallel. Aiming at the structural loss of Vision transformers, this paper combines ConViT and Convolutional Neural Network (CNN) and proposes a new model Convolution Meet Vision Transformers (CMVT). This model adds a convolution module to the ConViT network to solve the structural loss of the transformer. By adding hierarchical data representation, the ability to gradually extract more image classification features is improved. We have conducted comparative experiments on multiple dataset, and all of them have been enhanced to improve the efficiency and performance of the model.", acknowledgement = ack-nhfb, articleno = "2450060", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Sangani:2025:RSP, author = "Dhara J. Sangani and Rajesh A. Thakker and S. D. Panchal and Rajesh Gogineni", title = "Remote Sensing Pansharpening with {TVH$^{-1}$} Decomposition and {PSO}-Based Adaptive Weighting Method", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "01", pages = "??--??", month = jan, year = "2025", DOI = "https://doi.org/10.1142/S021946782450061X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jan 21 06:52:13 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S021946782450061X", abstract = "In remote sensing, owing to existing sensors' limitations and the tradeoff between signal-to-noise ratio (SNR) and instantaneous field of view (IFOV), it is difficult to obtain a single image with good spectral and spatial resolution. Pansharpening (PS) is the technique for sharpening multispectral (MS) images by extracting structural and edge information of panchromatic (PAN) image. Multiscale decomposition methods are used for decomposing image in sub-bands but are affected by ringing artifacts, therefore the resultant image seems to be blurred and misregistered. The proposed method overcomes this drawback by decomposing PAN and four band MS image into cartoon and texture components with total variation (TV) Hilbert {$-$1} `` role=''presentation`` style=''position: relative;``{$>$} [Math Processing Error] {\textminus}1 {\textminus}1 model. The particle swarm optimization (PSO) algorithm is used for finding the optimum weight for fusing texture and cartoon details of PAN and MS images. The proposed method is practically validated on both full-scale and reduced-scale. Robustness of our proposed approach is tested on different geographical areas such as hilly, urban, and vegetation areas. From the visual analysis and qualitative parameters, the proposed method is proved effective compared with other traditional approaches.", acknowledgement = ack-nhfb, articleno = "2450061", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Jannu:2025:OSE, author = "Chaitanya Jannu and Sunny Dayal Vanambathina", title = "An Overview of Speech Enhancement Based on Deep Learning Techniques", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "01", pages = "??--??", month = jan, year = "2025", DOI = "https://doi.org/10.1142/S0219467825500019", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jan 21 06:52:13 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500019", abstract = "Recent years have seen a significant amount of studies in the area of speech enhancement. This review looks at several speech improvement methods as well as Deep Neural Network (DNN) functions in speech enhancement. Speech transmissions are frequently distorted by ambient noise, background noise, and reverberations. There are processing methods, such as Short-time Fourier Transform, Short-time Autocorrelation, and Short-time Energy (STE), that can be used to enhance speech. To reduce speech noise, features such as the Mel-Frequency Cepstral Coefficients (MFCCs), Logarithmic Power Spectrum (LPS), and Gammatone Frequency Cepstral Coefficients (GFCCs) can be retrieved and input to a DNN. DNN is essential to speech improvement since it builds models using a lot of training data and evaluates the efficacy of the enhanced speech using certain performance metrics. Since the beginning of deep learning publications in 1993, a variety of speech enhancement methods have been examined in this study. This review provides a thorough examination of the several neural network topologies, training algorithms, activation functions, training targets, acoustic features, and databases that were employed for the job of speech enhancement and were gathered from various articles published between 1993 and 2022.", acknowledgement = ack-nhfb, articleno = "2550001", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Salman:2025:DFC, author = "Khalid A. Salman and Khalid Shaker and Sufyan Al-janabi", title = "Detection of Fake Colorized Images based on Deep Learning", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "01", pages = "??--??", month = jan, year = "2025", DOI = "https://doi.org/10.1142/S0219467825500020", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jan 21 06:52:13 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500020", abstract = "Image editing technologies have been advanced that can significantly enhance the image, but can also be used maliciously. Colorization is a new image editing technology that uses realistic colors to colorize grayscale photos. However, this strategy can be used on natural color images for a malicious purpose (e.g. to confuse object recognition systems that depend on the colors of objects for recognition). Image forensics is a well-developed field that examines photos of specified conditions to build confidence and authenticity. This work proposes a new fake colorized image detection approach based on the special Residual Network (ResNet) architecture. ResNets are a kind of Convolutional Neural Networks (CNNs) architecture that has been widely adopted and applied for various tasks. At first, the input image is reconstructed via a special image representation that combines color information from three separate color spaces (HSV, Lab, and Ycbcr); then, the new reconstructed images have been used for training the proposed ResNet model. Experimental results have demonstrated that our proposed method is highly generalized and significantly robust for revealing fake colorized images generated by various colorization methods.", acknowledgement = ack-nhfb, articleno = "2550002", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Liu:2025:DCR, author = "Ping Liu and Hangyu Ji", title = "Dual Channel Residual Learning for Denoising Path Tracing", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "01", pages = "??--??", month = jan, year = "2025", DOI = "https://doi.org/10.1142/S0219467825500032", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jan 21 06:52:13 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500032", abstract = "In this paper, we present a denoising method for path tracing using residual learning with convolutional neural networks (CNNs). Noisy artifacts in path tracing are inherited from insufficient sampling, which often generates over- or under-exposed values when integrating the limited bright or dark samples in a pixel. In this paper, we introduce a dual channel residual learning CNNs which separates the over and under-exposed signals in order to provide an efficient denoising filter for the path tracing rendering. Furthermore, we present an advanced CNN comprised of variable-sized kernels in each convolutional layer. Our CNN detects features in different scales providing an adaptive denoising filter capability which is optimal for extracting various contextual details in a complex scene. The experiments demonstrate that our method generates better visual quality than other compared approaches across various rendering effects.", acknowledgement = ack-nhfb, articleno = "2550003", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Sahu:2025:FAM, author = "Hemlata P. Sahu and Ramgopal Kashyap", title = "{Fine\_Denseiganet}: Automatic Medical Image Classification in Chest {CT} Scan Using Hybrid Deep Learning Framework", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "01", pages = "??--??", month = jan, year = "2025", DOI = "https://doi.org/10.1142/S0219467825500044", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Tue Jan 21 06:52:13 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500044", abstract = "Medical image classification is one of the most significant tasks in computer-aided diagnosis. In the era of modern healthcare, the progress of digitalized medical images has led to a crucial role in analyzing medical image analysis. Recently, accurate disease recognition from medical Computed Tomography (CT) images remains a challenging scenario which is important in rendering effective treatment to patients. The infectious COVID-19 disease is highly contagious and leads to a rapid increase in infected individuals. Some drawbacks noticed with RT-PCR kits are high false negative rate (FNR) and a shortage in the number of test kits. Hence, a Chest CT scan is introduced instead of RT-PCR which plays an important role in diagnosing and screening COVID-19 infections. However, manual examination of CT scans performed by radiologists can be time-consuming, and a manual review of each individual CT image may not be feasible in emergencies. Therefore, there is a need to perform automated COVID-19 detection with the advances in AI-based models. This work presents effective and automatic Deep Learning (DL)-based COVID-19 detection using Chest CT images. Initially, the data is gathered and pre-processed through Spatial Weighted Bilateral Filter (SWBF) to eradicate unwanted distortions. The extraction of deep features is processed using Fine\_Dense Convolutional Network (Fine\_DenseNet). For classification, the Softmax layer of Fine\_DenseNet is replaced using Improved Generative Adversarial Network\_Artificial Hummingbird (IGAN\_AHb) model in order to train the data on the labeled and unlabeled dataset. The loss in the network model is optimized using Artificial Hummingbird (AHb) optimization algorithm. Here, the proposed DL model (Fine\_DenseIGANet) is used to perform automated multi-class classification of COVID-19 using CT scan images and attained a superior classification accuracy of 95.73\% over other DL models.", acknowledgement = ack-nhfb, articleno = "2550004", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kumari:2025:ABS, author = "Santoshi Kumari and T. P. Pushphavathi", title = "Aspect-Based Sentiment Analysis Using {Fabricius} Ringlet-Based Hybrid Deep Learning for Online Reviews", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "02", pages = "??--??", month = mar, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467825500056", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Apr 23 08:00:42 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500056", abstract = "The sentiment analysis relying on the aspect of online reviews is utilized for identifying the polarity of the given review. Nowadays, many methods are introduced for aspect-based sentiment analysis (ABSA) using neural networks, and many methods failed to consider contextual information exploitation to make the performance more accurate. Hence, this research proposed an optimized deep learning method for the detection of the aspect and to identify the polarity. Hence, in this research, an optimized deep learning technique for the ABSA is introduced by considering the online reviews, in which the deep learning classifiers are trained with the proposed Fabricius ringlet optimization (FRO) algorithm to reduce the loss that helps to enhance the accuracy of sentiment polarity prediction. The proposed FRO is developed by the hybridization of the behavioral nature of the Fabricius and the ringlet in feeding for the determination of the global best solution. The tuning of the weights and biases of the classifier enhance the performance of the classifier. The objective behind the tuning is to minimize the loss function while training and to enhance the accuracy of aspect extraction and polarity prediction of sentiment. Based on a study of the existing approach, the suggested FRO-based hybrid deep learning method is significantly improved; its accuracy, sensitivity, and specificity are 87.06%, 90.83%, and 79.37%, respectively, with a training percentage of 40%. The accuracy, sensitivity, and specificity of the existing technique have also been enhanced for aspect restaurant values, which are 87.53%, 96.06%, and 79.88% with a 60% training percentage. Similar to that, Twitter values for accuracy, sensitivity, and specificity are reported to be 89.08%, 99.35%, and 79.70%, respectively, with an 80% training percentage. The proposed method obtained the 90.13%, 99.35%, and 81.10% accuracy, sensitivity, and specificity from the assessment of the FRO-based hybrid deep learning.", acknowledgement = ack-nhfb, articleno = "2550005", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Yadav:2025:DCO, author = "Sita M. Yadav and Sandeep M. Chaware", title = "Detection and Classification of Objects in Video Content Analysis Using Ensemble Convolutional Neural Network Model", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "02", pages = "??--??", month = mar, year = "2025", DOI = "https://doi.org/10.1142/S0219467825500068", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Apr 23 08:00:42 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500068", abstract = "Video content analysis (VCA) is the process of analyzing the contents in the video for various applications. Video classification and content analysis are two of the most difficult challenges that computer vision researchers must solve. Object detection plays an important role in the VCA and is used for identification, detection and classification of objects in the images. The Chaser Prairie Wolf optimization-based deep Convolutional Neural Network classifier (CPW opt-deep CNN classifier) is used in this research to identify and classify the objects in the videos. The deep CNN classifier correctly detected the objects in the video, and the CPW optimization boosted the deep CNN classifier's performance, where the decision-making behavior of the chasers is enhanced by the sharing nature of the prairie wolves. The classifier's parameters were successfully tuned by the enabled optimization, which also aids in producing better results. The Ensemble model developed for the object detection adds value to the research and is initiated by the standard hybridization of the YOLOv4 and Resnet 101 model, which evaluated the research's accuracy, sensitivity, and specificity, improving its efficacy. The proposed CPW opt-deep CNN classifier attained the values of 89.74%, 89.50%, and 89.19% while classifying objects in dataset 1, 91.66%, 86.01%, and 91.52% while classifying objects in dataset 2, compared to the preceding method that is efficient.", acknowledgement = ack-nhfb, articleno = "2550006", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kayyidavazhiyil:2025:CTC, author = "Abhilash Kayyidavazhiyil", title = "Combined Tri-Classifiers for {IoT} Botnet Detection with Tuned Training Weights", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "02", pages = "??--??", month = mar, year = "2025", DOI = "https://doi.org/10.1142/S021946782550007X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Apr 23 08:00:42 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S021946782550007X", abstract = "Although IoT sectors seem more popular and pervasively, they struggle with hazards. The botnet is one of the largest security dangers associated with IoT. It enables malicious software to administer and attack private network equipment collectively without the owners' knowledge. Although many studies have used ML to detect botnets, these are either not very effective or only work with specific types of botnets or devices. As a result, the detection model for deep learning ideas is the focus of this research. It entails three key processes: (a) preprocessing, (b) feature extraction, and (c) classification. The input data are initially preprocessed using an improved data normalization approach. The preprocessed data are used to extract a number of features, including Tanimoto coefficient features, improved differential holoentropy-based features, Pearson {\em r\/} correlation-based features, and others. The detection process will be completed by an ensemble classification model that randomly shuffles models like the Deep Belief Network (DBN) model, Bidirectional Gated Recurrent Unit (Bi-GRU), and Long Short-Term Memory (LSTM). Bi-GRU, DBN, and LSTM will be averaged to provide the ensemble results. Bi-GRU is trained using the Self Improved Blue Monkey Optimization (SIBMO) Algorithm by selecting the optimal weights, which increases the detection accuracy. The overall performance of the suggested work is then evaluated in relation to other existing models using various methodologies. In comparison to existing methods, the created ensemble classifier + + + SIBMO scheme obtains the highest accuracy (93%) at a learning percentage of 90%.", acknowledgement = ack-nhfb, articleno = "2550007", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Sharma:2025:IPB, author = "Manvinder Sharma and Sudhakara Reddy Saripalli and Anuj Kumar Gupta and Pankaj Palta and Digvijay Pandey", title = "Image Processing-Based Method of Evaluation of Stress from Grain Structures of Through Silicon Via ({TSV})", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "02", pages = "??--??", month = mar, year = "2025", DOI = "https://doi.org/10.1142/S0219467825500081", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Apr 23 08:00:42 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500081", abstract = "Visualization of material composition across numerous grains and complicated networks of grain boundaries using image processing techniques can reveal fresh insights into the material's structural evolution and upcoming functional capabilities for a variety of applications. Three-dimensional integrated circuits (3D IC) are the most practical technology for increasing transistor density in future semiconductor applications. One of the key benefits of 3D IC is heterogeneous integration, which results in shorter interconnections due to vertical stacking. However, one of the most significant challenges in building higher-density microelectronics devices is the stress generated by material mismatches in the coefficient of thermal expansion (CTE). The purpose of this study is to analyze grain boundary migration caused by variations in strain energy density using image processing methods for 3D grain continuum modeling. Temperature changes in polycrystalline structures generate stresses and strain energy densities, which may be calculated using FEM software. Single crystal Cu's anisotropic elastic properties are twisted to suit grain orientation in space and each grain is treated as a single crystal. Grain boundary speeds are calculated using a simple model that relates grain boundary mobility to variations in strain energy density on both sides of grain boundaries. Using the grain continuum model, researchers will be able to investigate the effect of thermally generated stresses on grain boundary motion caused by atomic flux driven by strain energy. Using finite-element modeling of the grain structure in a Through Silicon Via, the stress effect on grain boundaries caused by grain rotation due to CTE mismatch was investigated (TSV). The structure must be modeled using a scanning electron microscopes Electron Backscatter Diffraction (EBSD) image (SEM). Grain growth and subsequent grain boundary rotation can be performed using the appropriate extrapolation method to measure their influence on stress and, as a result, the TSV's overall reliability.", acknowledgement = ack-nhfb, articleno = "2550008", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Yan:2025:FRB, author = "Tianxing Yan and Yuhang Zhao and Zhichao Xue and and Yaermaimaiti Yilihamu", title = "{3D} Face Reconstruction Based on {ResNet} Feature Extraction and {CBAM}", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "02", pages = "??--??", month = mar, year = "2025", DOI = "https://doi.org/10.1142/S0219467825500743", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Apr 23 08:00:42 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500743", abstract = "In view of the scarcity, high cost and lack of diversity of three-dimensional (3D) face datasets, this paper designs an end-to-end self-supervised learning 3D face reconstruction network, which uses single 2D face image as input. The model bypasses the 3D face datasets and only uses the 2D face datasets for training to achieve high-precision 3D face reconstruction without any 3D face prior. First, the improved ResNet50 feature extraction module is introduced to extract and characterize the input image by deep convolutional network. Then, a lightweight convolutional block attention module is added to the face prediction subnetwork. On the one hand, channel attention extracts different information included in the image, and on the other hand spatial attention finds the location of the information. So, the serialized attention operation could accurately find the features required for different parameter predictions, further improving face reconstruction parameters' prediction accuracy. Finally, training, ablation and comparison experiments were conducted on CelebFaces Attributes, basel face model and Photoface datasets, and the combined loss function of pixel loss and perception loss was selected. The pixel loss function was calculated at the pixel microscopic level, and the perception loss function was calculated at the image macroscopic convolution level. The combination of the two could complement each other. Compared with the historical optimal results of the same network structure, the scale-invariant depth error and mean angle deviation of the proposed algorithm are improved by 5.2% and 8.2%, respectively. Experimental results strongly prove the effectiveness of the algorithm.", acknowledgement = ack-nhfb, articleno = "2550074", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Tiwari:2025:DHD, author = "Devendra Tiwari and Anand Gupta and Rituraj Soni", title = "{DNN-HHOA}: Deep Neural Network Optimization-Based Tabular Data Extraction from Compound Document Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "02", pages = "??--??", month = mar, year = "2025", DOI = "https://doi.org/10.1142/S021946782550010X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Apr 23 08:00:42 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S021946782550010X", abstract = "Text information extraction from a tabular structure within a compound document image (CDI) is crucial to help better understand the document. The main objective of text extraction is to extract only helpful information since tabular data represents the relation between text lying in a tuple. Text from an image may be of low contrast, different style, size, alignment, orientation, and complex background. This work presents a three-step tabular text extraction process, including pre-processing, separation, and extraction. The pre-processing step uses the guide image filter to remove various kinds of noise from the image. Improved binomial thresholding (IBT) separates the text from the image. Then the tabular text is recognized and extracted from CDI using deep neural network (DNN). In this work, weights of DNN layers are optimized by the Harris Hawk optimization algorithm (HHOA). Obtained text and associated information can be used in many ways, including replicating the document in digital format, information retrieval, and text summarization. The proposed process is applied comprehensively to UNLV, TableBank, and ICDAR 2013 image datasets. The complete procedure is implemented in Python, and precision metrics performance is verified.", acknowledgement = ack-nhfb, articleno = "2550010", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Siddiqua:2025:MAO, author = "Maria Siddiqua and Naeem Akhter and Aneela Zameer and Javaid Khurshid", title = "{MCCGAN}: An All-In-One Image Restoration Under Adverse Conditions Using Multidomain Contextual Conditional {GAN}", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "02", pages = "??--??", month = mar, year = "2025", DOI = "https://doi.org/10.1142/S0219467825500111", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Apr 23 08:00:42 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500111", abstract = "Clear images are crucial for the optimal performance of various high-level vision-based tasks. However, some inevitable causes, such as bad weather and underwater conditions degrade scene visibility. The tiny particles present in the air absorb and scatter light, causing severe attenuation that results in unclear, low-brightness, and poor-contrast images. Several techniques have been introduced to restore the degradation. However, no model exists to date that can restore multiple degradations using a single model. Therefore, to improve the scene visibility, a unified model called a Multidomain Contextual Conditional Generative Adversarial Network (MCCGAN) is designed, which uses the same parameters across the domains to restore multiple degradations such as fog, haze, rain streaks, snowflakes, smoke, shadows, underwater, and muddy underwater. The proposed model has a novel addition of multiple 1 {\texttimes} 1 1 \{\texttimes} 1 1 {\texttimes} 1 convolutional context encoding bottleneck layers between a simple lightweight eight-block encoder and decoder with skip connections which learns the context of each input domain thoroughly, thus generating better-restored images. The MCCGAN is qualitatively and quantitatively compared to various state-of-the-art image-to-image translation models and tested on a few real unseen image domains such as smog, dust, and lightning, and the obtained results successfully improved scene visibility, proving the generalizability of MCCGAN. Moreover, the MS-COCO 2017 validation dataset is used for comparing the performance of object detection, instance segmentation, and image captioning on (1) weather-degraded images, (2) restored images by MCCGAN, and (3) ground truth images, and the results demonstrated the success of our model. An ablation study is also carried out to check the significance of the discriminator, skip connections, and bottleneck layers in MCCGAN, and the analysis suggests that MCCGAN performs better by adding a discriminator, skip connections, and four bottleneck layers in the generator architecture.", acknowledgement = ack-nhfb, articleno = "2550011", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Bhat:2025:IIW, author = "Padmanayana Bhat and B. K. Anoop", title = "Improved Invasive Weed Social Ski-Driver Optimization-Based Deep Convolution Neural Network for Diabetic Retinopathy Classification", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "02", pages = "??--??", month = mar, year = "2025", DOI = "https://doi.org/10.1142/S0219467825500123", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Apr 23 08:00:42 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500123", abstract = "The eye-related problem of diabetes is called diabetic retinopathy (DR), which is the main factor contributing to visual loss. This research develops an enhanced deep model for DR classification. Here, deep convolutional neural network (Deep CNN) is trained with the improved invasive weed social ski-driver optimization (IISSDO), which is generated by fusing improved invasive weed optimization (IIWO) and social ski-driver (SSD). The IISSDO-based Deep CNN classifies DR severity into normal, mild, non-proliferative DR (NPDR), moderate NPDR, severe NPDR, and proliferative. Initially, a type 2 fuzzy and cuckoo search (T2FCS) filter performs pre-processing and the quality of the data is improved by data augmentation. The lesion is then divided using DeepJoint segmentation. Then, the Deep CNN determines the DR. The analysis uses the Indian DR image database. The IISSDO-based Deep CNN has the highest accuracy, sensitivity, and specificity of 96.566%, 96.773%, and 96.517%, respectively.", acknowledgement = ack-nhfb, articleno = "2550012", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Fatma:2025:SES, author = "Nusrat Fatma and Pawan Singh and Mohammad Khubeb Siddiqui", title = "Survey on Epileptic Seizure Detection on Varied Machine Learning Algorithms", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "02", pages = "??--??", month = mar, year = "2025", DOI = "https://doi.org/10.1142/S0219467825500135", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Apr 23 08:00:42 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500135", abstract = "Epilepsy is an unavoidable major persistent and critical neurological disorder that influences the human brain. Moreover, this is apparently distinguished via its recurrent malicious seizures. A seizure is a phase of synchronous, abnormal innervations of a neuron's population which might last from seconds to a few minutes. In addition, epileptic seizures are transient occurrences of complete or partial irregular unintentional body movements that combine with consciousness loss. As epileptic seizures rarely occurred in each patient, their effects based on physical communications, social interactions, and patients' emotions are considered, and treatment and diagnosis are undergone with crucial implications. Therefore, this survey reviews 65 research papers and states an important analysis on various machine-learning approaches adopted in each paper. The analysis of different features considered in each work is also done. This survey offers a comprehensive study on performance attainment in each contribution. Furthermore, the maximum performance attained by the works and the datasets used in each work is also examined. The analysis on features and the simulation tools used in each contribution is examined. At the end, the survey expanded with different research gaps and their problem which is beneficial to the researchers for promoting advanced future works on epileptic seizure detection.", acknowledgement = ack-nhfb, articleno = "2550013", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Eshwarappa:2025:OCM, author = "Laxmikant Eshwarappa and G. G. Rajput", title = "Optimal Classification Model for Text Detection and Recognition in Video Frames", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "02", pages = "??--??", month = mar, year = "2025", DOI = "https://doi.org/10.1142/S0219467825500147", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Apr 23 08:00:42 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500147", abstract = "Currently, the identification of text from video frames and normal scene images has got amplified awareness amongst analysts owing to its diverse challenges and complexities. Owing to a lower resolution, composite backdrop, blurring effect, color, diverse fonts, alternate textual placement among panels of photos and videos, etc., text identification is becoming complicated. This paper suggests a novel method for identifying texts from video with five stages. Initially, ``video-to-frame conversion'', is done during pre-processing. Further, text region verification is performed and keyframes are recognized using CNN. Then, improved candidate text block extraction is carried out using MSER. Subsequently, ``DCT features, improved distance map features, and constant gradient-based features'' are extracted. These characteristics subsequently provided ``Long Short-Term Memory (LSTM)'' for detection. Finally, OCR is done to recognize the texts in the image. Particularly, the Self-Improved Bald Eagle Search (SI-BESO) algorithm is used to adjust the LSTM weights. Finally, the superiority of the SI-BESO-based technique over many other techniques is demonstrated.", acknowledgement = ack-nhfb, articleno = "2550014", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Sameera:2025:ODL, author = "K. Sameera and P. Swarnalatha", title = "Optimization with Deep Learning Classifier-Based Foliar Disease Classification in Apple Trees Using {IoT} Network", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "03", pages = "??--??", month = may, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467825500159", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Apr 23 08:00:43 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500159", abstract = "The development of any country is influenced by the growth in the agriculture sector. The prevalence of pests and diseases in plants affects the productivity of any agricultural product. Early diagnosis of the disease can substantially decrease the effort and the fund required for disease management. The Internet of Things (IoT) provides a framework for offering solutions for automatic farming. This paper devises an automated detection technique for foliar disease classification in apple trees using an IoT network. Here, classification is performed using a hybrid classifier, which utilizes the Deep Residual Network (DRN) and Deep Q Q Q Network (DQN). A new Adaptive Tunicate Swarm Sine--Cosine Algorithm (TSSCA) is used for modifying the learning parameters as well as the weights of the proposed hybrid classifier. The TSSCA is developed by adaptively changing the navigation foraging behavior of the tunicates obtained from the Tunicate Swarm Algorithm (TSA) in accordance with the Sine--Cosine Algorithm (SCA). The outputs obtained from the Adaptive TSSCA-based DRN and Adaptive TSSCA-based DQN are merged using cosine similarity measure for detecting the foliar disease. The Plant Pathology 2020 --- FGVC7 dataset is utilized for the experimental process to determine accuracy, sensitivity, specificity and energy and we achieved the values of 98.36%, 98.58%, 96.32% and 0.413 J, respectively.", acknowledgement = ack-nhfb, articleno = "2550015", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Fang:2025:ATV, author = "Li Fang and Wang Xianghai", title = "Adaptive Total-Variation and Nonconvex Low-Rank Model for Image Denoising", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "03", pages = "??--??", month = may, year = "2025", DOI = "https://doi.org/10.1142/S0219467825500160", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Apr 23 08:00:43 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500160", abstract = "In recent years, image denoising methods based on total variational regularization have attracted extensive attention. However, the traditional total variational regularization method is an approximate solution based on convex method, and does not consider the particularity of the region with rich details. In this paper, the adaptive total-variation and nonconvex low-rank model for image denoising is proposed, which is a hybrid regularization model. First, the image is decomposed into sparse terms and low rank terms, and then the total variational regularization is used to denoise. At the same time, an adaptive coefficient based on gradient is constructed to adaptively judge the flat area and detail texture area, slow down the denoising intensity of detail area, and then play the role of preserving detail information. Finally, by constructing a nonconvex function, the optimal solution of the function is obtained by using the alternating minimization method. This method not only effectively removes the image noise, but also retains the detailed information of the image. The experimental results show the effectiveness of the proposed model, and SNR and SSIM of the denoised image are improved.", acknowledgement = ack-nhfb, articleno = "2550016", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Bachiphale:2025:OMI, author = "Pramod M. Bachiphale and Nitish S. Zulpe", title = "Optimal Multisecret Image Sharing Using Lightweight Visual Sign-Cryptography Scheme With Optimal Key Generation for Gray\slash Color Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "03", pages = "??--??", month = may, year = "2025", DOI = "https://doi.org/10.1142/S0219467825500172", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Apr 23 08:00:43 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500172", abstract = "Problem: Digital devices are becoming increasingly powerful and smart, which is improving quality of life, but presents new challenges to privacy protection. Visual cryptographic schemes provide data sharing privacy, but have drawbacks such as extra storage space, lossy secret images, and the need to store permutation keys. Aim: This paper proposes a light-weight visual sign-cryptography scheme based on optimal key generation to address the disadvantages of existing visual cryptographic schemes and improve the security, sharing quality, and time consumption of multisecret images. Methods: The proposed light-weight visual sign-cryptography (LW-VSC) scheme consists of three processes: band separation, shares generation, and signcryption/un-signcryption. The process of separation and shares generation is done by an existing method. The multiple shares of the secret images are then encrypted/decrypted using light-weight sign-cryptography. The proposed scheme uses a novel harpy eagle search optimization (HESO) algorithm to generate optimal keys for both the encrypt/decrypt processes. Results: Simulation results and comparative analysis showed the proposed scheme is more secure and requires less storage space, with faster encryption/decryption and improved key generation quality. Conclusion: The proposed light-weight visual sign-cryptography scheme based on optimal key generation is a promising approach to enhance security and improve data sharing quality. The HESO algorithm shows promise in improving the quality of key generation, providing better privacy protection in the face of increasingly powerful digital devices.", acknowledgement = ack-nhfb, articleno = "2550017", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Tan:2025:JBN, author = "Lunzheng Tan and Yanfei Liu and Limin Xia and Shangsheng Chen and Zhanben Zhou", title = "A Jeap-{BiLSTM} Neural Network for Action Recognition", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "03", pages = "??--??", month = may, year = "2025", DOI = "https://doi.org/10.1142/S0219467825500184", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Apr 23 08:00:43 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500184", abstract = "Human action recognition in videos is an important task in computer vision with applications in fields such as surveillance, human--computer interaction, and sports analysis. However, it is a challenging task due to the complex background changes and redundancy of long-term video information. In this paper, we propose a novel bi-directional long short-term memory method with attention pooling based on joint motion and difference entropy (JEAP-BiLSTM) to address these challenges. To obtain discriminative features, we introduce a joint entropy map that measures both the entropy of motion and the entropy of change. The Bi-LSTM method is then applied to capture visual and temporal associations in both forward and backward directions, enabling efficient capture of long-term temporal correlation. Furthermore, attention pooling is used to highlight the region of interest and to mitigate the effects of background changes in video information. Experiments on the UCF101 and HMDB51 datasets demonstrate that the proposed JEAP-BiLSTM method achieves recognition rates of 96.4% and 75.2%, respectively, outperforming existing methods. Our proposed method makes significant contributions to the field of human action recognition by effectively capturing both spatial and temporal patterns in videos, addressing background changes, and achieving state-of-the-art performance.", acknowledgement = ack-nhfb, articleno = "2550018", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Pan:2025:RPI, author = "Kai Pan and Hongyan Chi", title = "Research on Printmaking Image Classification and Creation Based on Convolutional Neural Network", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "03", pages = "??--??", month = may, year = "2025", DOI = "https://doi.org/10.1142/S0219467825500196", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Apr 23 08:00:43 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500196", abstract = "As an important form of expression in modern civilization art, printmaking has a rich variety of types and a prominent sense of artistic hierarchy. Therefore, printmaking is highly favored around the world due to its unique artistic characteristics. Classifying print types through image feature elements will improve people's understanding of print creation. Convolutional neural networks (CNNs) have good application effects in the field of image classification, so CNN is used for printmaking analysis. Considering that the classification effect of the traditional convolutional neural image classification model is easily affected by the activation function, the T-ReLU activation function is introduced. By utilizing adjustable parameters to enhance the soft saturation characteristics of the model and avoid gradient vanishing, a T-ReLU convolutional model is constructed. A better convolutional image classification model is proposed based on the T-ReLU convolutional model, taking into account the issue of subpar multi-level feature fusion in deep convolutional image classification models. Utilize normalization to analyze visual input, an eleven-layer convolutional network with residual units in the convolutional layer, and cascading thinking to fuse convolutional network defects. The performance test results showed that in the data test of different styles of artificial prints, the GT-ReLU model can obtain the best image classification accuracy, and the image classification accuracy rate is 0.978. The GT-ReLU model maintains a classification accuracy above 94.4% in the multi-dataset test classification performance test, which is higher than that of other image classification models. For the use of visual processing technology in the field of classifying prints, the research content provides good reference value.", acknowledgement = ack-nhfb, articleno = "2550019", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Bosco:2025:CBI, author = "P. John Bosco and S. Janakiraman", title = "Content-Based Image Retrieval ({CBIR}): Using Combined Color and Texture Features {(TriCLR} and {HistLBP)}", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "03", pages = "??--??", month = may, year = "2025", DOI = "https://doi.org/10.1142/S0219467825500214", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Apr 23 08:00:43 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500214", abstract = "Content-Based Image Retrieval (CBIR) is a broad research field in the current digital world. This paper focuses on content-based image retrieval based on visual properties, consisting of high-level semantic information. The variation between low-level and high-level features is identified as a semantic gap. The semantic gap is the biggest problem in CBIR. The visual characteristics are extracted from low-level features such as color, texture and shape. The low-level feature increases CBIRs performance level. The paper mainly focuses on an image retrieval system called combined color (TriCLR) (RGB, YCbCr, and L {\^a} a {\^a} b {\^a} L^{\{\^a} a}^{\{\^a} b}^{\{\^a} } L{\^a} a{\^a} b{\^a} ) with the histogram of texture features in LBP (HistLBP), which, is known as a hybrid of three colors (TriCLR) with Histogram of LBP (TriCLR and HistLBP). The study also discusses the hybrid method in light of low-level features. Finally, the hybrid approach uses the (TriCLR and HistLBP) algorithm, which provides a new solution to the CBIR system that is better than the existing methods.", acknowledgement = ack-nhfb, articleno = "2550021", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Mohakud:2025:HMC, author = "Rasmiranjan Mohakud and Rajashree Dash", title = "A Hybrid Model for Classification of Skin Cancer Images After Segmentation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "03", pages = "??--??", month = may, year = "2025", DOI = "https://doi.org/10.1142/S0219467825500226", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Apr 23 08:00:43 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500226", abstract = "For dermatoscopic skin lesion images, deep learning-based algorithms, particularly convolutional neural networks (CNN), have demonstrated good classification and segmentation capabilities. The impact of utilizing lesion segmentation data on classification performance, however, is still up for being subject to discussion. Being driven in this direction, in this work we propose a hybrid deep learning-based model to classify the skin cancer using segmented images. In the first stage, a fully convolutional encoder--decoder network (FCEDN) is employed to segment the skin cancer image and then in the second phase, a CNN is applied on the segmented images for classification. As the model's success depends on the hyper-parameters it uses and fine-tuning these hyper-parameters by hand is time-consuming, so in this study the hyper-parameters of the hybrid model are optimized by utilizing an exponential neighborhood gray wolf optimization (ENGWO) technique. Extensive experiments are carried out using the International Skin Imaging Collaboration (ISIC) 2016 and ISIC 2017 datasets to show the efficacy of the model. The suggested model has been evaluated on both balanced and unbalanced datasets. With the balanced dataset, the proposed hybrid model achieves training accuracy up to 99.98%, validation accuracy up to 92.13% and testing accuracy up to 89.75%. It is evident from the findings that the proposed hybrid model outperforms previous known models in a competitive manner over balanced data.", acknowledgement = ack-nhfb, articleno = "2550022", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Chhabra:2025:EBT, author = "Sumit Chhabra and Khushboo Bansal", title = "An Efficient Brain Tumor Prediction Using Pteropus Unicinctus Optimization on Deep Neural Network", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "03", pages = "??--??", month = may, year = "2025", DOI = "https://doi.org/10.1142/S0219467825500238", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Apr 23 08:00:43 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500238", abstract = "Human brain tumors are now the most serious and horrible diseases for people, causing certain deaths. The patient's life also becomes more complicated over time as a result of the brain tumor. Thus, it is essential to find tumors early to safeguard and extend the patient's life. Hence, new improvements are highly essential in the techniques of brain tumor detection in medical areas. To address this, research has introduced automatic brain tumor prediction using {\em Pteropus unicinctus\/} optimization on deep neural networks (PUO-deep NNs). Initially, the data are gathered from the BraTS MICCAI brain tumor dataset and preprocessing and ROI extraction are performed to remove the noise from the data. Then the extracted RoI is forwarded to the fuzzy c-means (FCM) clustering to segment the brain image. The parameters of the FCM tune the PUO algorithm so the image is segmented into the tumor region and the non-tumor region. Then the feature extraction takes place on ResNet. Finally, the deep NN classifier successfully predicted the brain tumor by utilizing the PUO method, which improved the classifier performance and produced extremely accurate results. For dataset 1, the PUO-deep NN achieved values of 87.69% accuracy, 93.81% sensitivity, and 99.01% specificity. The suggested PUO-deep NN also attained the values for dataset 2 of 98.49%, 98.55%, and 95.60%, which is significantly more effective than the current approaches.", acknowledgement = ack-nhfb, articleno = "2550023", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Yechuri:2025:SER, author = "Sivaramakrishna Yechuri and Sunny Dayal Vanabathina", title = "Speech Enhancement: A Review of Different Deep Learning Methods", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "03", pages = "??--??", month = may, year = "2025", DOI = "https://doi.org/10.1142/S021946782550024X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Apr 23 08:00:43 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S021946782550024X", abstract = "Speech enhancement methods differ depending on the degree of degradation and noise in the speech signal, so research in the field is still difficult, especially when dealing with residual and background noise, which is highly transient. Numerous deep learning networks have been developed that provide promising results for improving the perceptual quality and intelligibility of noisy speech. Innovation and research in speech enhancement have been opened up by the power of deep learning techniques with implications across a wide range of real time applications. By reviewing the important datasets, feature extraction methods, deep learning models, training algorithms and evaluation metrics for speech enhancement, this paper provides a comprehensive overview. We begin by tracing the evolution of speech enhancement research, from early approaches to recent advances in deep learning architectures. By analyzing and comparing the approaches to solving speech enhancement challenges, we categorize them according to their strengths and weaknesses. Moreover, we discuss the challenges and future directions of deep learning in speech enhancement, including the demand for parameter-efficient models for speech enhancement. The purpose of this paper is to examine the development of the field, compare and contrast different approaches, and highlight future directions as well as challenges for further research.", acknowledgement = ack-nhfb, articleno = "2550024", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Rai:2025:ECM, author = "Shabana Rai and Arif Ullah and Wong Lai Kuan and and Rifat Mustafa", title = "An Enhanced Compression Method for Medical Images Using {SPIHT} Encoder for Fog Computing", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "03", pages = "??--??", month = may, year = "2025", DOI = "https://doi.org/10.1142/S0219467825500251", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Apr 23 08:00:43 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500251", abstract = "When it comes to filtering and compressing data before sending it to a cloud server, fog computing is a rummage sale. Fog computing enables an alternate method to reduce the complexity of medical image processing and steadily improve its dependability. Medical images are produced by imaging processing modalities using X-rays, computed tomography (CT) scans, magnetic resonance imaging (MRI) scans, and ultrasound (US). These medical images are large and have a huge amount of storage. This problem is being solved by making use of compression. In this area, lots of work is done. However, before adding more techniques to Fog, getting a high compression ratio (CR) in a shorter time is required, therefore consuming less network traffic. Le Gall5/3 integer wavelet transform (IWT) and a set partitioning in hierarchical trees (SPIHT) encoder were used in this study's implementation of an image compression technique. MRI is used in the experiments. The suggested technique uses a modified CR and less compression time (CT) to compress the medical image. The proposed approach results in an average CR of 84.8895%. A 40.92% peak signal-to-noise ratio (PSNR) PNSR value is present. Using the Huffman coding, the proposed approach reduces the CT by 36.7434 s compared to the IWT. Regarding CR, the suggested technique outperforms IWTs with Huffman coding by 12%. The current approach has a 72.36% CR. The suggested work's shortcoming is that the high CR caused a decline in the quality of the medical images. PSNR values can be raised, and more effort can be made to compress colored medical images and 3-dimensional medical images.", acknowledgement = ack-nhfb, articleno = "2550025", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Manochandar:2025:DLB, author = "T. Manochandar and P. Kumaraguru Diderot", title = "Deep Learning-Based Magnetic Resonance Image Segmentation and Classification for {Alzheimer}'s Disease Diagnosis", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "03", pages = "??--??", month = may, year = "2025", DOI = "https://doi.org/10.1142/S0219467825500263", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Apr 23 08:00:43 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500263", abstract = "Accurate and rapid detection of Alzheimer's disease (AD) using magnetic resonance imaging (MRI) gained considerable attention among research workers because of an increased number of current researches being driven by deep learning (DL) methods that have accomplished outstanding outcomes in variety of domains involving medical image analysis. Especially, convolution neural network (CNN) is primarily applied for the analyses of image datasets according to the capability of handling massive unstructured datasets and automatically extracting significant features. Earlier detection is dominant to the success and development interferences, and neuroimaging characterizes the potential regions for earlier diagnosis of AD. The study presents and develops a novel Deep Learning-based Magnetic Resonance Image Segmentation and Classification for AD Diagnosis (DLMRISC-ADD) model. The presented DLMRISC-ADD model mainly focuses on the segmentation of MRI images to detect AD. To accomplish this, the presented DLMRISC-ADD model follows a two-stage process, namely, skull stripping and image segmentation. At the preliminary stage, the presented DLMRISC-ADD model employs U-Net-based skull stripping approach to remove skull regions from the input MRIs. Next, in the second stage, the DLMRISC-ADD model applies QuickNAT model for MRI image segmentation, which identifies distinct parts such as white matter, gray matter, hippocampus, amygdala, and ventricles. Moreover, densely connected network (DenseNet201) feature extractor with sparse autoencoder (SAE) classifier is used for AD detection process. A brief set of simulations is implemented on ADNI dataset to demonstrate the improved performance of the DLMRISC-ADD method, and the outcomes are examined extensively. The experimental results exhibit the effectual segmentation results of the DLMRISC-ADD technique.", acknowledgement = ack-nhfb, articleno = "2550026", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Pasha:2025:DTO, author = "Md Azam Pasha and M. Narayana", title = "Development of Trio Optimal Feature Extraction Model for Attention-Based Adaptive Weighted {RNN}-Based Lung and Colon Cancer Detection Framework Using Histopathological Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "03", pages = "??--??", month = may, year = "2025", DOI = "https://doi.org/10.1142/S0219467825500275", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Wed Apr 23 08:00:43 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500275", abstract = "Due to the combination of genetic diseases as well as a variety of biomedical abnormalities, the fatal disease named cancer is caused. Colon and lung cancer are regarded as the two leading diseases for disability and death. The most significant component for demonstrating the best course of action is the histopathological identification of such malignancies. So, in order to minimize the mortality rate caused by cancer, there is a need for early detection of the aliment on both fronts accordingly. In this case, both the deep and machine learning techniques have been utilized to speed up the detection process of cancer which may also help the researchers to study a huge amount of patients over a short period and less loss. Hence, it is highly essential to design a new lung and colon detection model based on deep learning approaches. Initially, a different set of histopathological images is collected from benchmark resources to perform effective analysis. Then, to attain the first set of features, the collected image is offered to the dilated net for attaining deep image features with the help of the Visual Geometry Group (VGG16) and Residual Neural Network (ResNet). Further, the second set of features is attained by the below process. Here, the collected image is given to pre-processing phase and the image is pre--pre-processed with the help of Contrast-limited Adaptive Histogram Equalization (CLAHE) and filter technique. Then, the pre-processed image is offered to the segmentation phase with the help of adaptive binary thresholding and offered to a dilated network that holds VGG16 and ResNet and attained the second set of features. The parameters of adaptive binary thresholding are tuned with the help of a developed hybrid approach called Sand Cat swarm JAya Optimization (SC-JAO) via Sand Cat swarm Optimization (SCO) and JAYA (SC-JAO). Finally, the third set of features is attained by offering the image to pre-processing phase. Then, the pre-processed image is offered to the segmentation phase and the image is a segmented phase and features are tuned by developed SC-JAO. Further, the segmented features are offered to attain the textural features like Gray-Level Co-Occurrence Matrix (GLCM) and Local Weber Pattern (LWP) and attained the third set of features. Then, the attained three different sets of features are given to the optimal weighted feature phase, where the parameters are optimized by the SC-JAO algorithm and then given to the disease prediction phase. Here, disease prediction is made with the help of Attention-based Adaptive Weighted Recurrent Neural Networks (AAW-RNN), and their parameters are tuned by developed SC-JAO. Thus, the developed model achieved an effective lung and colon detection rate over conventional approaches over multiple experimental analyses.", acknowledgement = ack-nhfb, articleno = "2550027", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Nagdeote:2025:EPL, author = "Sushma Nagdeote and Sapna Prabhu and And Jayashri Chaudhari", title = "Enhanced Power Law Transformation for Histopathology Images of Breast Cancer", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "04", pages = "??--??", month = jul, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467825500287", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 6 07:40:19 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500287", abstract = "Different image enhancement techniques are applied to improve the visual quality of an image on a display device. Contrast stretching, intensity level slicing with and without background, histogram equalization, logarithmic transformation and power law transformation are some image enhancement techniques. Most of the research work focuses on adaptive gamma correcting factors for better visualization of extremely low contrast images, giving less importance to the constant for enhanced visualization. This research proposes an efficient and less complex enhanced power law transformation (EPLT) approach to improve the contrast of dimmed and extremely bright images. The approach is a quick way to compute the value of C, i.e. constant for enhanced visualization. For better picture quality, it is very important to determine C automatically and the gamma correcting factor. This technique offers a novel and unique perspective on image contrast manipulation. The proposed enhancement technique is experimented on histopathology images of breast cancer, bright images and extremely dark images. The average peak signal-to-noise ratio (PSNR) for clinical data and Break His dataset is high for the proposed method are 16.52487 and 17.69335 respectively. The average RMSE for clinical data and BreakHis dataset is low for the proposed method are 40.88251 and 44.2546 respectively. It is observed that the proposed method yields the most satisfactory contrast enhancements based on performance comparison with other state-of-art enhancement algorithms and works efficiently on all types of images.", acknowledgement = ack-nhfb, articleno = "2550028", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Sandhu:2025:CRG, author = "Manbir Sandhu and Sumit Kushwaha and And Tanvi Arora", title = "A Comprehensive Review of {GAN}-Based Denoising Models for Low-Dose Computed Tomography Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "04", pages = "??--??", month = jul, year = "2025", DOI = "https://doi.org/10.1142/S0219467825500305", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 6 07:40:19 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500305", abstract = "Computed Tomography (CT) offers great visualization of the intricate internal body structures. To protect a patient from the potential radiation-related health risks, the acquisition of CT images should adhere to the ``as low as reasonably allowed'' (ALARA) standard. However, the acquired Low-dose CT (LDCT) images are inadvertently corrupted by artifacts and noise during the processes of acquisition, storage, and transmission, degrading the visual quality of the image and also causing the loss of image features and relevant information. Most recently, generative adversarial network (GAN) models based on deep learning (DL) have demonstrated ground-breaking performance to minimize image noise while maintaining high image quality. These models' ability to adapt to uncertain noise distributions and representation-learning ability makes them highly desirable for the denoising of CT images. The state-of-the-art GANs used for LDCT image denoising have been comprehensively reviewed in this research paper. The aim of this paper is to highlight the potential of DL-based GAN for CT dose optimization and present future scope of research in the domain of LDCT image denoising.", acknowledgement = ack-nhfb, articleno = "2550030", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Singh:2025:ERL, author = "Rajesh Singh", title = "An Extensive Review on Lung Cancer Detection Models", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "04", pages = "??--??", month = jul, year = "2025", DOI = "https://doi.org/10.1142/S0219467825500317", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 6 07:40:19 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500317", abstract = "The categorization and identification of lung disorders in medical imageries are made easier by recent advances in deep learning (DL). As a result, various studies using DL to identify lung illnesses were developed. This study aims to analyze different publications that have been contributed to in order to recognize lung cancer. This literature review examines the many methods for detecting lung cancer. It analyzes several segmentation models that have been used and reviews different research papers. It examines several feature extraction methods, such as those using texture-based and other features. The investigation then concentrates on several cancer detection strategies, including ``DL models'' and machine learning (ML) models. It is possible to examine and analyze the performance metrics. Finally, research gaps are presented to encourage additional investigation of lung detection models.", acknowledgement = ack-nhfb, articleno = "2550031", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Rajasree:2025:DEC, author = "R. S. Rajasree and S. Brintha Rajakumari", title = "Deep Ensemble of Classifiers for {Alzheimer}'s Disease Detection with Optimal Feature Set", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "04", pages = "??--??", month = jul, year = "2025", DOI = "https://doi.org/10.1142/S0219467825500329", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 6 07:40:19 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500329", abstract = "Machine learning (ML) and deep learning (DL) techniques can considerably enhance the process of making a precise diagnosis of Alzheimer's disease (AD). Recently, DL techniques have had considerable success in processing medical data. They still have drawbacks, like large data requirements and a protracted training phase. With this concern, we have developed a novel strategy with the four stages. In the initial stage, the input data is subjected to data imbalance processing, which is crucial for enhancing the accuracy of disease detection. Subsequently, entropy-based, correlation-based, and improved mutual information-based features will be extracted from these pre-processed data. However, the curse of dimensionality will be a serious issue in this work, and hence we have sorted it out via optimization strategy. Particularly, the tunicate updated golden eagle optimization (TUGEO) algorithm is proposed to pick out the optimal features from the extracted features. Finally, the ensemble classifier, which integrates models like CNN, DBN, and improved RNN is modeled to diagnose the diseases by training the selected optimal features from the previous stage. The suggested model achieves the maximum F-measure as 97.67, which is better than the extant methods like TSO {\= }72 {\. }39% , BMO {\= }78 , SSA {\= }84 {\. }15% , GEO {\= }70 {\. }39% , and FFLY {\= }73 {\. }13% , respectively. The suggested TUGEO-based AD detection is then compared to the traditional models like various performance matrices including accuracy, sensitivity, specificity, and precision.", acknowledgement = ack-nhfb, articleno = "2550032", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Hajare:2025:BGD, author = "Neha Hajare and Anand Singh Rajawat", title = "Black {Gram} Disease Classification via Deep Ensemble Model with Optimal Training", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "04", pages = "??--??", month = jul, year = "2025", DOI = "https://doi.org/10.1142/S0219467825500330", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 6 07:40:19 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500330", abstract = "Black gram crop belongs to the Fabaceae family and its scientific name is {\em Vigna Mungo.\/} It has high nutritional content, improves the fertility of the soil, and provides atmospheric nitrogen fixation in the soil. The quality of the black gram crop is degraded by diseases such as Yellow mosaic, Anthracnose, Powdery Mildew, and Leaf Crinkle which causes economic loss to farmers and degraded production. The agriculture sector needs to classify plant nutrient deficiencies in order to increase crop quality and yield. In order to handle a variety of difficult challenges, computer vision and deep learning technologies play a crucial role in the agricultural and biological sectors. The typical diagnostic procedure involves a pathologist visiting the site and inspecting each plant. However, manually crop disease assessment is limited due to lesser accuracy and limited access of personnel. To address these problems, it is necessary to develop automated methods that can quickly identify and classify a wide range of plant diseases. In this paper, black gram disease classifications are done through a deep ensemble model with optimal training and the procedure of this technique is as follows: Initially, the input dataset is processed to increase its size via data augmentation. Here, the processes like shifting, rotation, and shearing take place. Then, the model starts with the noise removal of images using median filtering. Subsequent to the preprocessing, segmentation takes place via the proposed deep joint segmentation model to determine the ROI and non-ROI regions. The next process is the extraction of the feature set that includes the features like improved multi-texton-based features, shape-based features, color-based features, and local Gabor X-OR pattern features. The model combines the classifiers like Deep Belief Networks, Recurrent Neural Networks, and Convolutional Neural Networks. For tuning the optimal weights of the model, a new algorithm termed swarm intelligence-based Self-Improved Dwarf Mongoose Optimization algorithm (SIDMO) is introduced. Over the past two decades, nature-based metaheuristic algorithms have gained more popularity because of their ability to solve various global optimization problems with optimal solutions. This training model ensures the enhancement of classification accuracy. The accuracy of the SIDMO, which is around 94.82%, is substantially higher than that of the existing models, which are FPA {\= }88.86%, SSOA {\= }88.99%, GOA {\= }85.84%, SMA {\= }85.11%, SRSR {\= }85.32%, and DMOA {\= }88.99%, respectively.", acknowledgement = ack-nhfb, articleno = "2550033", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Chandan:2025:CSD, author = "Madhavarapu Chandan and S. G. Santhi and And T. Srinivasa Rao", title = "Combined Shallow and Deep Learning Models for Malware Detection in {WSN}", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "04", pages = "??--??", month = jul, year = "2025", DOI = "https://doi.org/10.1142/S0219467825500342", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 6 07:40:19 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500342", abstract = "Due to the major operating restrictions, ensuring security is the fundamental problem of Wireless Sensor Networks (WSNs). Because of their inadequate security mechanisms, WSNs are indeed a simple point for malware (worms, viruses, malicious code, etc.). According to the epidemic nature of worm propagation, it is critical to develop a worm defense mechanism in the network. This concept aims to establish novel malware detection in WSN that consists of several phases: ``(i) Preprocessing, (ii) feature extraction, as well as (iii) detection''. At first, the input data is subjected for preprocessing phase. Then, the feature extraction takes place, in which principal component analysis (PCA), improved linear discriminant analysis (LDA), and autoencoder-based characteristics are retrieved. Moreover, the retrieved characteristics are subjected to the detection phase. The detection is performed employing combined shallow learning and DL. Further, the shallow learning includes decision tree (DT), logistic regression (LR), and Naive Bayes (NB); the deep learning (DL) includes deep neural network (DNN), convolutional neural network (CNN), and recurrent neural network (RNN). Here, the DT output is given to the DNN, LR output is subjected to CNN, and the NB output is given to the RNN, respectively. Eventually, the DNN, CNN, and RNN outputs are averaged to generate a successful outcome. The combination can be thought of as an Ensemble classifier. The weight of the RNN is optimally tuned through the Self Improved Shark Smell Optimization with Opposition Learning (SISSOOL) model to improve detection precision and accuracy. Lastly, the outcomes of the suggested approach are computed in terms of different measures.", acknowledgement = ack-nhfb, articleno = "2550034", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Gao:2025:RRD, author = "Zhijing Gao and Weilin Qiu and Ren Wenqi and And Yan Xiao", title = "Research on Robust Digital Watermarking Based on Reversible Information Hiding", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "04", pages = "??--??", month = jul, year = "2025", DOI = "https://doi.org/10.1142/S0219467825500354", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 6 07:40:19 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500354", abstract = "The development of modern Internet communication technology and the popularization of multimedia technology have brought convenience to the sharing and storage of multimedia information such as images, videos, and audio. However, at the same time, it has brought about the problem of copyright theft of multimedia information, causing serious information security risks. Digital watermarking technology embeds copyright information in multimedia information in an invisible way, which can effectively realize copyright protection and traceability of infringement. Aiming at the problem that the existing learning model-based methods cannot fully extract and fuse the features of carrier images and watermark images, a robust digital watermarking method based on reversible information hiding is proposed. First, a watermark embedding model based on reversible information hiding is established, and the features of the download volume image and the watermark image in different dimensions are fully extracted and fused to generate a dense image with excellent visual quality. Then, a watermark extraction model based on reversible information hiding is established, and a noise layer is added between the embedding and the extraction model, and the attacked dense image is input to the watermark extraction model to extract the watermark. Under the constraint of the loss function, the network model learns to embed watermark information in the area that is more robust to the attack and is not easy to cause visual quality degradation, so as to optimize the comprehensive performance of the method. Experimental results show that the proposed method effectively improves the imperceptibility and robustness.", acknowledgement = ack-nhfb, articleno = "2550035", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Zhou:2025:ADF, author = "Yuze Zhou and Liwei Yan and And Qi Zhu", title = "Adversarial Detection and Fusion Method for Multispectral Palmprint Recognition", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "04", pages = "??--??", month = jul, year = "2025", DOI = "https://doi.org/10.1142/S0219467825500366", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 6 07:40:19 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500366", abstract = "As a kind of promising biometric technology, multispectral palmprint recognition methods have attracted increasing attention in security due to their high recognition accuracy and ease of use. It is worth noting that although multispectral palmprint data contains rich complementary information, multispectral palmprint recognition methods are still vulnerable to adversarial attacks. Even if only one image of a spectrum is attacked, it can have a catastrophic impact on the recognition results. Therefore, we propose a robustness-enhanced multispectral palmprint recognition method, including a model interpretability-based adversarial detection module and a robust multispectral fusion module. Inspired by the model interpretation technology, we found there is a large difference between clean palmprint and adversarial examples after CAM visualization. Using visualized images to build an adversarial detector can lead to better detection results. Finally, the weights of clean images and adversarial examples in the fusion layer are dynamically adjusted to obtain the correct recognition results. Experiments have shown that our method can make full use of the image features that are not attacked and can effectively improve the robustness of the model.", acknowledgement = ack-nhfb, articleno = "2550036", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Jyothula:2025:CLE, author = "Samrajam Jyothula and S. Chandrasekhar", title = "{CNN-LandCoverNet}: an Effective Framework of Land Cover Classification Using Hybrid Metaheuristic-Aided Ensemble-Based Convolutional Neural Network", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "04", pages = "??--??", month = jul, year = "2025", DOI = "https://doi.org/10.1142/S021946782550038X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 6 07:40:19 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S021946782550038X", abstract = "Land cover (LC) categorization is considered a necessary task of intelligent interpretation technology for remote sensing imagery that is intended to categorize every pixel to perform the predefined LC classification. Land Use and Land Cover (LULC) information has the ability to provide various insights in order to overcome environmental and socioeconomic impacts such as disaster risk, climate change, poverty, and food insecurity. Therefore, image categorization tasks are involved in conventional works, where the classical visual interpretation techniques completely depend upon professional knowledge as well as a professional's classification experience, which is more susceptible to subjective awareness, inefficient, and time consuming. By overcoming this issue, the latest deep-structured approach is suggested to perform the LC image classification. Initially, the land images are gathered. Further, the collected images are employed for patch splitting, where the images are split into multiple patches. After splitting, the patches are fed to the Ensemble-based Convolutional Neural Network (ECNN), which is constructed with a Fully Convolutional Network (FCN), U-Net, DeepLabv3, and Mask Region-based Convolutional Neural Network (Mask R-CNN) for performing segmentation. Here, the hyperparameters are optimally tuned with the Hybrid Billiards-inspired Water Wave Algorithm (HB-WWA) by integrating the Billiards-inspired Optimization Algorithm (BOA) and Water Wave Algorithm (WWA). Finally, the classification is carried out with a fuzzy classifier. Thus, the performance is validated and measured through diverse metrics. Consequently, the developed work has demonstrated enhanced classification accuracy when tested on other existing algorithms.", acknowledgement = ack-nhfb, articleno = "2550038", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Ravi:2025:SLR, author = "Jampani Ravi and R. Narmadha", title = "A Systematic Literature Review on Multimodal Image Fusion Models with Challenges and Future Research Trends", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "04", pages = "??--??", month = jul, year = "2025", DOI = "https://doi.org/10.1142/S0219467825500391", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 6 07:40:19 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500391", abstract = "Imaging technology has undergone extensive development since 1985, which has practical implications concerning civilians and the military. Recently, image fusion is an emerging tool in image processing that is adept at handling diverse image types. Those image types include remote sensing images and medical images for upgrading the information through the fusion of visible and infrared light based on the analysis of the materials used. Presently, image fusion has been mainly performed in the medical industry. With the constraints of diagnosing a disease via single-modality images, image fusion could be able to meet up the prerequisites. Hence, it is further suggested to develop a fusion model using different modalities of images. The major intention of the fusion approach is to achieve higher contrast, enhancing the quality of images and apparent knowledge. The validation of fused images is done by three factors that are: (i) fused images should sustain significant information from the source images, (ii) artifacts must not be present in the fused images and (iii) the flaws of noise and misregistration must be evaded. Multimodal image fusion is one of the developing domains through the implementation of robust algorithms and standard transformation techniques. Thus, this work aims to analyze the different contributions of various multimodal image fusion models using intelligent methods. It will provide an extensive literature survey on image fusion techniques and comparison of those methods with the existing ones. It will offer various state-of-the-arts of image fusion methods with their diverse levels as well as their pros and cons. This review will give an introduction to the current fusion methods, modes of multimodal fusion, the datasets used and performance metrics; and finally, it also discusses the challenges of multimodal image fusion methods and the future research trends.", acknowledgement = ack-nhfb, articleno = "2550039", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Prasath:2025:CSI, author = "B. Prasath and M. Akila and And M. Mohan", title = "A Comprehensive Survey on {IoT}-Aided Pest Detection and Classification in Agriculture Using Different Image Processing Techniques", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "04", pages = "??--??", month = jul, year = "2025", DOI = "https://doi.org/10.1142/S0219467825500408", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 6 07:40:19 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500408", abstract = "Insect and rodents constantly cause trouble to the farmers leading to different kinds of diseases in the crop. Controlling as well as crop maintenance becomes a highly essential task for the farmers to ensure the health of the crop. However, they cause various social as well as environmental issues. Excessive pesticide usage may affect the contamination of soil and water, and also, it becomes highly toxic to plants. Hence, bugs and insects become more cautious against plants along with constant exposure, which pushes the farmer to utilize heavy pesticides. However, genetic seed manipulation is mainly used to provide high robustness against pest attacks, and they are highly expensive for practical execution. Implementation of the Internet-of-Things (IoT) in the agricultural domain has brought an enhanced improvement in on-field pest management. Several pest detections, as well as classification models, have been implemented in prior works, and they are based on effective techniques. The main purpose of this survey paper is to provide a literature review of IoT-aided pest detection and classification using different images. The datasets used in different pest detection and classification, the simulated platforms, and performance measures are analyzed. Further, the recent trends of machine learning and deep learning methods in this field are reviewed and categorized. Thus, the given survey impacts the economy for analyzing pest detection in the early stage, which provides better crop production, and also maximizes the protection of crops. Moreover, it helps to minimize human errors, and also it provides the best efforts to increase the automated monitoring system for large fields.", acknowledgement = ack-nhfb, articleno = "2550040", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Bhosale:2025:AVT, author = "Manoj Krishna Bhosale and Shubhangi B. Patil and And Babasaheb B Patil", title = "Automatic Video Traffic Surveillance System with Number Plate Character Recognition Using Hybrid Optimization-Based {YOLOv3} and Improved {CNN}", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "04", pages = "??--??", month = jul, year = "2025", DOI = "https://doi.org/10.1142/S021946782550041X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 6 07:40:19 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S021946782550041X", abstract = "Recently, the increased count of surveillance cameras has manipulated the demand criteria for a higher effective video coding process. Moreover, the ultra-modern video coding standards have appreciably enhanced the efficiency of video coding, which has been developed for gathering common videos over surveillance videos. Various vehicle recognition techniques have provided a challenging and promising role in computer vision applications and intelligent transport systems. In this case, most of the conventional techniques have recognized the vehicles along with bounding box depiction and thus failed to provide the proper locations of the vehicles. Moreover, the position details have been vigorous in terms of various real-time applications trajectory of vehicle's motion on the road as well as movement estimation. Numerous advancements have been offered throughout the years in the traffic surveillance area through the random propagation of intelligent traffic video surveillance techniques. The ultimate goal of this model is to design and enhance intelligent traffic video surveillance techniques by utilizing the developed deep learning techniques. This model has the ability to handle video traffic surveillance by measuring the speed of vehicles and recognizing their number plates. The initial process is considered the data collection, in which the traffic video data is gathered. Furthermore, the vehicle detection is performed by the Optimized YOLOv3 deep learning classifier, in which the parameter optimization is performed by using the newly recommended Modified Coyote Spider Monkey Optimization (MCSMO), which is the combination of Coyote Optimization Algorithm (COA) and Spider Monkey Optimization (SMO). Furthermore, the speed of the vehicles has been measured from each frame. For high-speed vehicles, the same Optimized YOLOv3 is used for detecting the number plates. Once the number plates are detected, plate character recognition is performed by the Improved Convolutional Neural Network (ICNN). Thus, the information about the vehicles, which are violating the traffic rules, can be conveyed to the vehicle owners and Regional Transport Office (RTO) to take further action to avoid accidents. From the experimental validation, the accuracy and precision rate of the designed method achieves 97.53% and 96.83%. Experimental results show that the proposed method achieves enhanced performance when compared to conventional models, thus ensuring the security of the transport system.", acknowledgement = ack-nhfb, articleno = "2550041", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Zhang:2025:MSA, author = "Peng Zhang and Yangyang Miao and Dongri Shan and And Shuang Li", title = "Model Self-Adaptive Display for {2D}--{3D} Registration", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "05", pages = "??--??", month = sep, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467825500421", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 6 07:40:21 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500421", abstract = "In the 2D--3D registration process, due to the differences in CAD model sizes, models may be too large to be displayed in full or too small to have obvious features. To address these problems, previous studies have attempted to adjust parameters manually; however, this is imprecise and frequently requires multiple adjustments. Thus, in this paper, we propose the model self-adaptive display of fixed-distance and maximization (MSDFM) algorithm. The uncertainty of the model display affects the storage costs of pose images, and pose images themselves occupy a large amount of storage space; thus, we also propose the storage optimization based on the region of interest (SOBROI) method to reduce storage costs. The proposed MSDFM algorithm retrieves the farthest point of the model and then searches for the maximum pose image of the model display through the farthest point. The algorithm then changes the projection angle until the maximum pose image is maximized within the window. The pose images are then cropped by the proposed SOBROI method to reduce storage costs. By labeling the connected domains in the binary pose image, an external rectangle of the largest connected domain is applied to crop the pose image, which is then saved in the lossless compression portable network image (PNG) format. Experimental results demonstrate that the proposed MSDFM algorithm can automatically adjust models of different sizes. In addition, the results show that the proposed SOBROI method reduces the storage space of pose libraries by at least 89.66% and at most 99.86%.", acknowledgement = ack-nhfb, articleno = "2550042", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Nithya:2025:MAC, author = "B. N. Nithya and D. Evangelin Geetha and And Manish Kumar", title = "Metaheuristic-Assisted Contextual Post-Filtering Method for Event Recommendation System", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "05", pages = "??--??", month = sep, year = "2025", DOI = "https://doi.org/10.1142/S0219467825500433", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 6 07:40:21 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500433", abstract = "In today's world, the web is a prominent communication channel. However, the variety of strategies available on event-based social networks (EBSNs) also makes it difficult for users to choose the events that are most relevant to their interests. In EBSNs, searching for events that better fit a user's preferences are necessary, complex, and time consuming due to a large number of events available. Toward this end, a community-contributed data event recommender framework assists consumers in filtering daunting information and providing appropriate feedback, making EBSNs more appealing to them. A novel customized event recommendation system that uses the ``multi-criteria decision-making (MCDM) approach'' to rank the events is introduced in this research work. The calculation of categorical, geographical, temporal, and social factors is carried out in the proposed model, and the recommendation list is ordered using a contextual post-filtering system that includes Weight and Filter. To align the recommendation list, a new probabilistic weight model is added. To be more constructive, this model incorporates metaheuristic reasoning, which will fine-tune the probabilistic threshold value using a new hybrid algorithm. The proposed hybrid model is referred to as Beetle Swarm Hybridized Elephant Herding Algorithm (BSH-EHA), which combines the algorithms like Elephant Herding Optimization (EHO) and Beetle Swarm Optimization (BSO) Algorithm. Finally, the top recommendations will be given to the users.", acknowledgement = ack-nhfb, articleno = "2550043", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Welde:2025:CVQ, author = "Tesfayee Meshu Welde and Lejian Liao", title = "Counting in Visual Question Answering: Methods, Datasets, and Future Work", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "05", pages = "??--??", month = sep, year = "2025", DOI = "https://doi.org/10.1142/S0219467825500445", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 6 07:40:21 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500445", abstract = "Visual Question Answering (VQA) is a language-based method for analyzing images, which is highly helpful in assisting people with visual impairment. The VQA system requires a demonstrated holistic image understanding and conducts basic reasoning tasks concerning the image in contrast to the specific task-oriented models that simply classifies object into categories. Thus, VQA systems contribute to the growth of Artificial Intelligence (AI) technology by answering open-ended, arbitrary questions about a given image. In addition, VQA is also used to assess the system's ability by conducting Visual Turing Test (VTT). However, because of the inability to generate the essential datasets and being incapable of evaluating the systems due to flawlessness and bias, the VQA system is incapable of assessing the system's overall efficiency. This is seen as a possible and significant limitation of the VQA system. This, in turn, has a negative impact on the progress of performance observed in VQA algorithms. Currently, the research on the VQA system is dealing with more specific sub-problems, which include counting in VQA systems. The counting sub-problem of VQA is a more sophisticated one, riddling with several challenging questions, especially when it comes to complex counting questions such as those that demand object identifications along with detection of objects attributes and positional reasoning. The pooling operation that is considered to perform an attention mechanism in VQA is found to degrade the counting performance. A number of algorithms have been developed to address this issue. In this paper, we provide a comprehensive survey of counting techniques in the VQA system that is developed especially for answering questions such as ``How many?''. However, the performance progress achieved by this system is still not satisfactory due to bias that occurs in the datasets from the way we phrase the questions and because of weak evaluation metrics. In the future, fully-fledged architecture, wide-size datasets with complex counting questions and a detailed breakdown in categories, and strong evaluation metrics for evaluating the ability of the system to answer complex counting questions, such as positional and comparative reasoning will be executed.", acknowledgement = ack-nhfb, articleno = "2550044", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Toofanee:2025:DUA, author = "Mohammud Shaad Ally Toofanee and Omar Boudraa and And Karim Tamine", title = "{DLMDish}: Using Applied Deep Learning and Computer Vision to Automatically Classify Mauritian Dishes", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "05", pages = "??--??", month = sep, year = "2025", DOI = "https://doi.org/10.1142/S0219467825500457", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 6 07:40:21 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500457", abstract = "The benefits of using an automatic dietary assessment system for accompanying diabetes patients and prediabetic persons to control the risk factor also referred to as the obesity ``pandemic'' are now widely proven and accepted. However, there is no universal solution as eating habits of people are dependent on context and culture. This project is the cornerstone for future works of researchers and health professionals in the field of automatic dietary assessment of Mauritian dishes. We propose a process to produce a food dataset for Mauritian dishes using the Generative Adversarial Network (GAN) and a fine Convolutional Neural Network (CNN) model for identifying Mauritian food dishes. The outputs and findings of this research can be used in the process of automatic calorie calculation and food recommendation, primarily using ubiquitous devices like mobile phones via mobile applications. Using the Adam optimizer with carefully fixed hyper-parameters, we achieved an Accuracy of 95.66% and Loss of 3.5% as concerns the recognition task.", acknowledgement = ack-nhfb, articleno = "2550045", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Jayasri:2025:NDP, author = "N. P. Jayasri and R. Aruna", title = "A Novel Diabetes Prediction Model in Big Data Healthcare Systems Using {DA-KNN} Technique", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "05", pages = "??--??", month = sep, year = "2025", DOI = "https://doi.org/10.1142/S0219467825500469", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 6 07:40:21 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500469", abstract = "In the past decades, there is a wide increase in the number of people affected by diabetes, a chronic illness. Early prediction of diabetes is still a challenging problem as it requires clear and sound datasets for a precise prediction. In this era of ubiquitous information technology, big data helps to collect a large amount of information regarding healthcare systems. Due to explosion in the generation of digital data, selecting appropriate data for analysis still remains a complex task. Moreover, missing values and insignificantly labeled data restrict the prediction accuracy. In this context, with the aim of improving the quality of the dataset, missing values are effectively handled by three major phases such as (1) pre-processing, (2) feature extraction, and (3) classification. Pre-processing involves outlier rejection and filling missing values. Feature extraction is done by a principal component analysis (PCA) and finally, the precise prediction of diabetes is accomplished by implementing an effective distance adaptive-KNN (DA-KNN) classifier. The experiments were conducted using Pima Indian Diabetes (PID) dataset and the performance of the proposed model was compared with the state-of-the-art models. The analysis after implementation shows that the proposed model outperforms the conventional models such as NB, SVM, KNN, and RF in terms of accuracy and ROC.", acknowledgement = ack-nhfb, articleno = "2550046", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Tripathy:2025:RTS, author = "Santosh Kumar Tripathy and Poonkuntran Shanmugam", title = "Real-Time Spatial-Temporal Depth Separable {CNN} for Multi-Functional Crowd Analysis in Videos", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "05", pages = "??--??", month = sep, year = "2025", DOI = "https://doi.org/10.1142/S0219467825500470", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 6 07:40:21 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500470", abstract = "Crowd behavior prediction (CBP) and crowd counting (CC) are the essential functions of vision-based crowd analysis (CA), which play a crucial role in controlling crowd disasters. The CA using different models for the CBP and the CC will increase computational overheads and have synchronization issues. The state-of-the-art approaches utilized deep convolutional architectures to exploit spatial-temporal features to accomplish the objective, but such models suffer from computational complexities during convolution operations. Thus, to sort out the issues as mentioned earlier, this paper develops a single deep model which performs two functionalities of CA: CBP and CC. The proposed model uses multilayers of depth-wise separable CNN (DSCNN) to extract fine-grained spatial-temporal features from the scene. The DSCNN can minimize the number of matrix multiplications during convolution operation compared to traditional CNN. Further, the existing datasets are available to accomplish the single functionality of CA. In contrast, the proposed model needs a dual-tasking CA dataset which should provide the ground-truth labels for CBP and CC. Thus, a dual functionality CA dataset is prepared using a benchmark crowd behavior dataset, i.e. MED. Around 41 000 frames have been manually annotated to obtain ground-truth crowd count values. This paper also demonstrates an experiment on the proposed multi-functional dataset and outperforms the state-of-the-art methods regarding several performance metrics. In addition, the proposed model processes each test frame at 3.40 milliseconds, and thus is easily applicable in real-time.", acknowledgement = ack-nhfb, articleno = "2550047", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Tang:2025:FMB, author = "Yawen Tang and Jianhong Ren", title = "Feature Matching-Based Undersea Panoramic Image Stitching in {VR} Animation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "05", pages = "??--??", month = sep, year = "2025", DOI = "https://doi.org/10.1142/S0219467825500482", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 6 07:40:21 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500482", abstract = "The continuous development of virtual reality animation has brought people a new viewing experience. However, there is still a large research space for the construction of virtual scenes. Underwater scenes are complex and diverse, and to obtain more realistic virtual scenes, it is necessary to use video panoramic images as reference modeling in advance. To this end, the study uses the K -means clustering method to extract key frames from underwater video, and adaptively adjusts the number of clusters to improve the extraction algorithm according to the differences in features. To address the problems of low contrast and severe blurring in underwater images, the study uses an improved non-local {\em a priori\/} recovery method to achieve the recovery process of underwater images. Finally, the final underwater panoramic image is obtained by fading-out image fusion and frame to stitching image synthesis strategy. The experimental analysis shows that the runtime of Model 1 is 21.46 s, the root mean square error value is 1.89, the structural similarity value is 0.9678, and the average gradient value is 12.59. It can achieve efficient and high-quality panoramic image generation.", acknowledgement = ack-nhfb, articleno = "2550048", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Jadhav:2025:RAS, author = "Sharad B. Jadhav and N. K. Deshmukh and And Sahebrao B. Pawar", title = "Robust Authentication System with Privacy Preservation for Hybrid Deep Learning-Based Person Identification System Using Multi-Modal Palmprint, Ear, and Face Biometric Features", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "05", pages = "??--??", month = sep, year = "2025", DOI = "https://doi.org/10.1142/S0219467825500494", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 6 07:40:21 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500494", abstract = "Conventional biometric systems are vulnerable to a range of harmful threats and privacy violations, putting the users who have registered with them in grave danger. Therefore, there is a need to develop a Privacy-Preserving and Authenticating Framework for Biometric-based Systems (PPAF-BS) that allows users to access multiple applications while also protecting their privacy. There are various existing works on biometric-based systems, but most of them do not address privacy concerns. Conventional biometric systems require the storage of biometric data, which can be easily accessed by attackers, leading to privacy violations. Some research works have used differential privacy techniques to address this issue, but they have not been widely applied in biometric-based systems. The existing biometric-based systems have a significant privacy concern, and there is a lack of privacy-preserving techniques in such systems. Therefore, there is a need to develop a PPAF-BS that can protect the user's privacy and maintain the system's efficiency. The proposed method uses Hybrid Deep Learning (HDL) with palmprint, ear, and face biometric features for person identification. Additionally, Discrete Cosine Transform (DCT) feature transformation and Lagrange's interpolation-based image transformation are used as part of the authentication scheme. Sensors are used to record three biometric traits: palmprint, ear, and face. The combination of biometric characteristics provides an accuracy of 96.4% for the 8 \{\texttimes} 8 image size. The proposed LI-based image transformation lowers the original 512 \{\texttimes} 512 pixels to an 8 \{\texttimes} 8 hidden pattern. This drastically decreases the database size, thereby reducing storage needs. The proposed method offers a safe authentication system with excellent accuracy, a fixed-size database, and the privacy protection of multi-modal biometric characteristics without sacrificing overall system efficiency. The system achieves an accuracy of 96.4% for the 8 \{\texttimes} 8 image size, and the proposed LI-based picture transformation significantly reduces the database size, which is a significant achievement in terms of storage requirements. Therefore, the proposed method can be considered an effective solution to the privacy and security concerns of biometric-based systems.", acknowledgement = ack-nhfb, articleno = "2550049", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Joshi:2025:MMI, author = "Aruna Kumar Joshi and Shrinivasrao B. Kulkarni", title = "Multi-Modal Information Fusion for Localization of Emergency Vehicles", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "05", pages = "??--??", month = sep, year = "2025", DOI = "https://doi.org/10.1142/S0219467825500500", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 6 07:40:21 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500500", abstract = "In urban and city environments, road transportation contributes significantly to the generation of substantial traffic. However, this surge in vehicles leads to complex issues, including hindered emergency vehicle movement due to high density and congestion. Scarcity of human personnel amplifies these challenges. As traffic conditions worsen, the need for automated solutions to manage emergency situations becomes more evident. Intelligent traffic monitoring can identify and prioritize emergency vehicles, potentially saving lives. However, categorizing emergency vehicles through visual analysis faces difficulties such as clutter, occlusions, and traffic variations. Visual-based techniques for vehicle detection rely on clear rear views, but this is problematic in dense traffic. In contrast, audio-based methods are resilient to the Doppler Effect from moving vehicles, but handling diverse background noises remains unexplored. Using acoustics for emergency vehicle localization presents challenges related to sensor range and real-world noise. Addressing these issues, this study introduces a novel solution: combining visual and audio data for enhanced detection and localization of emergency vehicles in road networks. Leveraging this multi-modal approach aims to bolster accuracy and robustness in emergency vehicle management. The proposed methodology consists of several key steps. The presence of an emergency vehicle is initially detected through the preprocessing of visual images, involving the removal of clutter and occlusions via an adaptive background model. Subsequently, a cell-wise classification strategy utilizing a customized Visual Geometry Group Network (VGGNet) deep learning model is employed to determine the presence of emergency vehicles within individual cells. To further reinforce the accuracy of emergency vehicle presence detection, the outcomes from the audio data analysis are integrated. This involves the extraction of spectral features from audio streams, followed by classification utilizing a support vector machine (SVM) model. The fusion of information derived from both visual and audio sources is utilized in the construction of a more comprehensive and refined traffic state map. This augmented map facilitates the effective management of emergency vehicle transit. In empirical evaluations, the proposed solution demonstrates its capability to mitigate challenges like visual clutter, occlusions, and variations in traffic density common issues encountered in traditional visual analysis methods. Notably, the proposed approach achieves an impressive accuracy rate of approximately 98.15% in the localization of emergency vehicles.", acknowledgement = ack-nhfb, articleno = "2550050", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Ye:2025:MML, author = "Mengting Ye and Zhenxue Chen and Yixin Guo and Kaili Yu and And Longcheng Liu", title = "{MRCNet}: Multi-Level Residual Connectivity Network for Image Classification", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "05", pages = "??--??", month = sep, year = "2025", DOI = "https://doi.org/10.1142/S0219467825500512", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 6 07:40:21 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500512", abstract = "Computer vision obtains object and environment information by simulating human visual senses and borrowing human sensory activity. As one of the main tasks of computer vision, image classification can be used not only for face recognition, traffic scene recognition, image retrieval, and automatic photo categorization but also as a theoretical basis for target detection and image segmentation. In this paper, we use the existing CNN architecture network-ConvNeXt. By adapting and modifying the residual connectivity and convolutional structure of the network, we achieve a balance between classification accuracy and inference speed. These modifications are able to reduce both computation and memory consumption while keeping accuracy largely unchanged, thus better facilitating network lightweighting.", acknowledgement = ack-nhfb, articleno = "2550051", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Babu:2025:DIN, author = "Gorla Babu and Pinjari Abdul Khayum", title = "Design and Implementation of Novel Hybrid and Multiscale-Assisted {CNN} and {ResNet} Using Heuristic Advancement of Adaptive Deep Segmentation for Iris Recognition", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "05", pages = "??--??", month = sep, year = "2025", DOI = "https://doi.org/10.1142/S0219467825500524", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 6 07:40:21 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500524", abstract = "Due to its significant applications in security, the iris recognition process has been considered as the most active research area over the last few decades. In general, the iris recognition framework has been crucially utilized for various security applications because it includes a set of features as well as does not alter its character according to the time. In recent times, emerging deep learning techniques have attained huge success, particularly in the field of the iris recognition framework model. Moreover, in considering the field of iris recognition, there is no possibility for the remarkable capability of the deep learning model as well as to attain superior performance. To handle the issues in the conventional model of iris recognition, a novel heuristic-aided deep learning framework has been implemented for recognizing the iris system. Initially, the required source iris images are gathered from the data sources. It is then followed by the pre-processing stage, where the pre-processed image is obtained. Consequently, the image segmentation process is carried out by Adaptive Deeplabv3+layers, in which the parameters are optimized using the Modified Weighted Flow Direction Algorithm (MWFDA). Finally, the iris recognition is accomplished by hybrid Hybridization of Multiscale Dilated-Assisted Learning (MDAL) that will be composed of both a Convolutional Neural Network (CNN) and a Residual Network (ResNet). To achieve optimal recognition results, the parameters in CNN and ResNet are tuned optimally by using MWFDA. The experimental results are estimated with the help of distinct measures. Contrary to conventional methods, the empirical results prove that the recommended model achieves the desired value to enhance the recognition performance.", acknowledgement = ack-nhfb, articleno = "2550052", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Samhitha:2025:DMO, author = "B. Keerthi Samhitha and R. Subhashini", title = "Dwarf Mongoose Optimization with Transfer Learning-Based Fish Behavior Classification Model", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "05", pages = "??--??", month = sep, year = "2025", DOI = "https://doi.org/10.1142/S0219467825500536", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 6 07:40:21 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500536", abstract = "Behavioral monitoring can be used to monitor aquatic ecosystems and water quality over time. Using precise and rapid fish performance detection, fishermen may make educated management decisions on recirculating aquaculture systems while decreasing labor. Sensors and procedures for recognizing fish behavior are often developed and prepared by researchers in big numbers. Deep learning (DL) techniques have revolutionized the capability to automatically analyze videos, which were utilized for behavior analysis, live fish detection, biomass estimation, water quality monitoring, and species classification. The benefit of DL is that it could automatically study the extraction of image features and reveals brilliant performance in identifying sequential actions. This paper focuses on the design of Dwarf Mongoose Optimization with Transfer Learning-based fish behavior classification (DMOTLB-FBC) model. The presented DMOTLB-FBC technique intends to effectively monitor and classify fish behaviors. Initially, the DMOTLB-FBC technique follows Gaussian filtering (GFI) technique for noise removal process. Besides, a transfer learning (TL)-based neural architectural search network (NASNet) model is used to produce a collection of feature vectors. For fish behavior classification, graph convolution network (GCN) model is employed in this work. To improve the fish behavior classification results of the DMOTLB-FBC technique, the DWO algorithm is applied as a hyperparameter optimizer of the GCN model. The experimentation analysis of the DMOTLB-FBC technique is tested on fish video dataset and the widespread comparison study reported the enhancements of the DMOTLB-FBC technique over other recent approaches.", acknowledgement = ack-nhfb, articleno = "2550053", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Susan:2025:FEV, author = "Seba Susan and Maduri Tuteja", title = "Feature Engineering Versus Deep Learning for Scene Recognition: a Brief Survey", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "06", pages = "??--??", month = nov, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467825500548", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 6 07:40:22 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500548", abstract = "Scene recognition is an important computer vision task that has evolved from the study of the biological visual system. Its applications range from video surveillance, autopilot systems, to robotics. The early works were based on feature engineering that involved the computation and aggregation of global and local image descriptors. Several popular image features such as SIFT, SURF, HOG, ORB, LBP, KAZE, etc. have been proposed and applied to the task with successful results. Features can be either computed from the entire image on a global scale, or extracted from local sub-regions and aggregated across the image. Suitable classifier models are deployed that learn to classify these features. This review paper analyzes several of these handcrafted features that have been applied to the scene recognition task over the past decades, and tracks the transition from the traditional feature engineering to deep learning which forms the current state of the art in computer vision. Deep learning is now deemed to have overtaken feature engineering in several computer vision applications. Deep convolutional neural networks and vision transformers are the current state of the art for object recognition. However, scenes from urban landscapes are bound to contain similar objects posing a challenge to deep learning solutions for scene recognition. In our study, a critical analysis of feature engineering and deep learning methodologies for scene recognition is provided, and results on benchmark scene datasets are presented, concluding with a discussion on challenges and possible solutions that may facilitate more accurate scene recognition.", acknowledgement = ack-nhfb, articleno = "2550054", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kumar:2025:CAT, author = "Ajay Kumar and Naveen Hemrajani", title = "Congestion Avoidance in {TCP} Based on Optimized Random Forest with Improved Random Early Detection Algorithm", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "06", pages = "??--??", month = nov, year = "2025", DOI = "https://doi.org/10.1142/S021946782550055X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 6 07:40:22 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S021946782550055X", abstract = "Transmission control protocol (TCP) ensures that data are safely and accurately transported over the network for applications that use the transport protocol to allow reliable information delivery. Nowadays, internet usage in the network is growing and has been developing many protocols in the network layer. Congestion leads to packet loss, the high time required for data transmission in the TCP protocol transport layer for end-to-end connections is one of the biggest issues with the internet. An optimized random forest algorithm (RFA) with improved random early detection (IRED) for congestion prediction and avoidance in transport layer is proposed to overcome the drawbacks. Data are initially gathered and sent through data pre-processing to improve the data quality. For data pre-processing, KNN-based missing value imputation is applied to replace the values that are missing in raw data and z -score normalization is utilized to scale the data in a certain range. Following that, congestion is predicted using an optimized RFA and whale optimization algorithm (WOA) is used to set the learning rate as efficiently as possible in order to reduce error and improve forecast accuracy. To avoid congestion, IRED method is utilized for a congestion-free network in the transport layer. Performance metrics are evaluated and compared with the existing techniques with respect to accuracy, precision, recall, specificity, and error, whose values that occur for the proposed model are 98%, 98%, 99%, 98%, and 1%. Throughput and latency are also evaluated in the proposed method to determine the performance of the network. Finally, the proposed method performs better when compared to the existing techniques and prediction, and avoidance of congestion is identified accurately in the network.", acknowledgement = ack-nhfb, articleno = "2550055", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Butt:2025:UFR, author = "Asif Raza Butt and Zahid {Ur Rahman} and Anwar {Ul Haq} and Bilal Ahmed and And Sajjad Manzoor", title = "Unconstrained Face Recognition Using Infrared Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "06", pages = "??--??", month = nov, year = "2025", DOI = "https://doi.org/10.1142/S0219467825500561", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 6 07:40:22 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500561", abstract = "Recently, face recognition (FR) has become an important research topic due to increase in video surveillance. However, the surveillance images may have vague non-frontal faces, especially with the unidentifiable face pose or unconstrained environment such as bad illumination and dark environment. As a result, most FR algorithms would not show good performance when they are applied on these images. On the contrary, it is common at surveillance field that only Single Sample per Person (SSPP) is available for identification. In order to resolve such issues, visible spectrum infrared images were used which can work in entirely dark condition without having any light variations. Furthermore, to effectively improve FR for both the low-quality SSPP and unidentifiable pose problem, an approach to synthesize 3D face modeling and pose variations is proposed in this paper. A 2D frontal face image is used to generate a 3D face model. Then several virtual face test images with different poses are synthesized from this model. A well-known Surveillance Camera's Face (SCface) database is utilized to evaluate the proposed algorithm by using PCA, LDA, KPCA, KFA, RSLDA, LRPP-GRR, deep KNN and DLIB deep learning. The effectiveness of the proposed method is verified through simulations, where increase in average recognition rates up to 10%, 27.69%, 14.62%, 25.38%, 57.46%, 57.43, 37.69% and 63.28%, respectively, for SCface database as observed.", acknowledgement = ack-nhfb, articleno = "2550056", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Wang:2025:DOD, author = "Baomin Wang and Hewei Ding and Fei Teng and Zhirong Wang and And Hongqin Liu", title = "Damage Object Detection of Steel Wire Rope-Core Conveyor Belts Based on the Improved {YOLOv5}", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "06", pages = "??--??", month = nov, year = "2025", DOI = "https://doi.org/10.1142/S0219467825500573", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 6 07:40:22 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500573", abstract = "In response to the challenges in detecting damage features in X-ray images of steel wire rope-cores in conveyor belts, such as complex damage shapes, small sizes, low detection precision, and poor generalization ability, an improved YOLOv5 algorithm was proposed. The aim of the model is to accurately and efficiently identify and locate damage in the X-ray images of steel wire rope-cores in conveyor belts. First, the Adaptive Histogram Equalization (AHE) method is used to preprocess the images, reducing the interference of harsh mining environments and improving the quality of the dataset. Second, to better retain image details and enhance the detection ability of damage features, transpose convolutional upsampling is adopted, and the C3 module in the backbone network is replaced by C2f to ensure lightweight network models, meanwhile, it obtains richer gradient flow information and optimizing the loss function. Finally, the improved algorithm is compared with four classical detection algorithms using the damage feature dataset of steel wire rope-core conveyor belts. The experimental result shows that the proposed algorithm achieves an average detection precision of 91.8% and a detection speed of 40 frames per second (FPS) for images collected in harsh mining environments. The designed detection model provides a reference for the automatic recognition and detection of damage to steel wire rope-core conveyor belts.", acknowledgement = ack-nhfb, articleno = "2550057", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Patil:2025:PCS, author = "Veena I. Patil and Shobha R. Patil", title = "Pelican Crow Search Optimization Enabled {MIRNet}-Based Image Enhancement of Histopathological Images of Uterine Tissue", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "06", pages = "??--??", month = nov, year = "2025", DOI = "https://doi.org/10.1142/S0219467825500585", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 6 07:40:22 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500585", abstract = "The digitalized image enhancement methods offer multiple options to improve the visual quality of images. The histopathological image assessment is the golden standard to diagnose endometrial cancer, which is also called uterine cancer that seriously affects the reproductive system of females. Owing to the limited capability, complex relationship among histopathological images and its elucidation utilizing existing methods frequently fails to obtain satisfying outcomes. As a result, in this study, the Pelican crow search optimization_multiple identities representation network (PCSO_MIRNet) is presented for improving the quality of histopathology images of uterine tissue. First, the histopathological images are given to pre-processing stage, which is performed by the median filter. The image enhancement is done utilizing MIRNet, which is trained by devised PCSO. The PCSO is developed by incorporating Pelican Optimization Algorithm (POA) and Crow Search Algorithm (CSA). Furthermore, PCSO_MIRNet attained the best outcomes with a maximal peak signal-to-noise ratio (PSNR) of 44.741 dB, minimal mean squared error (MSE) of 0.937, and minimal degree of distortion (DD) value achieved is 0.068 dB.", acknowledgement = ack-nhfb, articleno = "2550058", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Chen:2025:ORB, author = "Xi Chen and Zhenyu Zhang", title = "Optimization Research of Bird Detection Algorithm Based on {YOLO} in Deep Learning Environment", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "06", pages = "??--??", month = nov, year = "2025", DOI = "https://doi.org/10.1142/S0219467825500597", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 6 07:40:22 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500597", abstract = "Recent environmental degradation has led to an unparalleled decline in wild bird habitats, resulting in a worldwide decrease in bird populations. To prevent extinction, it is vital to implement protective measures. One effective solution could be the application of deep learning techniques to identify bird species and habitats, which would prove useful for bird enthusiasts and rescuers. Therefore, a dataset of 20 globally prized bird species has been collated and analyzed. The Bird-YOLO algorithm precisely identifies avian creatures by combining neural architecture search and knowledge distillation. To diminish noise interference, preprocessing of images and dimension clustering of prior boxes are carried out prior to the training. The experiments show that the Bird-YOLO algorithm attains an 88.23% bird recognition rate, with a frames per second (FPS) of 47.", acknowledgement = ack-nhfb, articleno = "2550059", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Zhang:2025:UCB, author = "Qi Zhang and Zuobin Ying and Jian Shen and Seng-ka Kou and Jingzhang Sun and And Bob Zhang", title = "Unsupervised Color-Based Nuclei Segmentation in Histopathology Images with Various Color Spaces and {K} Values Selection", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "06", pages = "??--??", month = nov, year = "2025", DOI = "https://doi.org/10.1142/S0219467825500615", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 6 07:40:22 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500615", abstract = "The development of digital pathology offers a significant opportunity to evaluate and analyze the whole slides of disease tissue effectively. In particular, the segmentation of nuclei from histopathology images plays an important role in quantitatively measuring and evaluating the acquired diseased tissue. There are many automatic methods to segment cell nuclei in histopathology images. One widely used unsupervised segmentation approach is based on standard {\em k\/} -means or fuzzy {\em c\/} -means (FCM) to process the color histopathology images to segment cell nuclei. Compared with the supervised learning method, this approach can obtain segmented nuclei without annotated nuclei labels for training, which saves a lot of labeling and training time. The color space and k value among this method plays a crucial role in determining the nuclei segmentation performance. However, few works have investigated various color spaces and k value selection simultaneously in unsupervised color-based nuclei segmentation with k -means or FCM algorithms. In this study, we will present color-based nuclei segmentation methods with standard k -means and FCM algorithms for histopathology images. Several color spaces of Haematoxylin and Eosin (H&E) stained histopathology data and various k values among k -means and FCM are investigated correspondingly to explore the suitable selection for nuclei segmentation. A comprehensive nuclei dataset with 7 different organs is used to validate our proposed method. Related experimental results indicate that \ldots{}", acknowledgement = ack-nhfb, articleno = "2550061", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Nikhil:2025:SEC, author = "U. Vijay Nikhil and Z. Stamenkovic and And S. P. Raja", title = "A Study of Elliptic Curve Cryptography and Its Applications", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "06", pages = "??--??", month = nov, year = "2025", DOI = "https://doi.org/10.1142/S0219467825500627", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 6 07:40:22 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500627", abstract = "This paper aims to provide a comprehensive review on Elliptic Curve Cryptography (ECC), a public key cryptographic system and its applications. The paper discusses important mathematical properties and operations of elliptic curves, like point addition and multiplication operations and its implementation in cryptographic methods such as encryption and decryption. This paper provides a detailed workout on important mathematical problems on elliptic curves and ECC which provides insight into working of essential cryptographic techniques in ECC. And the paper also provides a literature review of research works based on ECC in various fields such as Internet of Things (IoT), Cloud computing, Blockchain and Image Security. And the paper further provides insight into the recent applications of ECC in fields like IoT and Blockchain by comprehensively discussing the proposed mechanism for each of the recent applications and also briefly discussing the security of the proposed mechanism.", acknowledgement = ack-nhfb, articleno = "2550062", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Sharanyaa:2025:HHL, author = "S. Sharanyaa and M. Sambath", title = "{HLNBO}: Hybrid Leader Namib Beetle Optimization Algorithm-Based {LeNet} for Classification of {Parkinson}'s Disease", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "06", pages = "??--??", month = nov, year = "2025", DOI = "https://doi.org/10.1142/S0219467825500639", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 6 07:40:22 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500639", abstract = "Parkinson's disease (PD) occurs while particular cells of the brain are not able to create dopamine that is required for regulating the count of non-motor as well as motor activities of the human body. One of the earlier symptoms of PD is voice disorder and current research shows that approximately about 90% of patients affected by PD suffer from vocal disorders. Hence, it is vital to extract pathology information in voice signals for detecting PD, which motivates to devise the approaches for feature selection and classification of PD. Here, an effectual technique is devised for the classification of PD, which is termed as Hybrid Leader Namib beetle optimization algorithm-based LeNet (HLNBO-based LeNet). The considered input voice signal is subjected to pre-processing of the signal phase. The pre-processing is carried out to remove the noises and calamities using a Gaussian filter whereas in the feature extraction phase, several features are extracted. The extracted features are given to the feature selection stage that is performed employing the Hybrid Leader Squirrel Search Water algorithm (HLSSWA), which is the combination of Hybrid Leader-Based Optimization (HLBO), Squirrel Search Algorithm (SSA), and Water Cycle Algorithm (WCA) by considering the Canberra distance as the fitness function. The PD classification is conducted using LeNet, which is tuned by the designed HLNBO. Additionally, HLNBO is newly presented by merging HLBO and the Namib beetle optimization algorithm (NBO). Thus, the new technique achieved maximal values of accuracy, TPR, and TNR of about 0.949, 0.957, and 0.936, respectively.", acknowledgement = ack-nhfb, articleno = "2550063", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Niazi:2025:EKG, author = "Mehrnaz Niazi and Kambiz Rahbar", title = "Entropy Kernel Graph Cut Feature Space Enhancement with {SqueezeNet} Deep Neural Network for Textural Image Segmentation", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "06", pages = "??--??", month = nov, year = "2025", DOI = "https://doi.org/10.1142/S0219467825500640", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 6 07:40:22 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500640", abstract = "Recently, image segmentation based on graph cut methods has shown remarkable performance on a set of image data. Although the kernel graph cut method provides good performance, its performance is highly dependent on the data mapping to the transformation space and image features. The entropy-based kernel graph cut method is suitable for segmentation of textured images. Nonetheless, its segmentation quality remains significantly contingent on the accuracy and richness of feature space representation and kernel centers. This paper introduces an entropy-based kernel graph cut method, which leverages the discriminative feature space extracted from SqueezeNet, a deep neural network. The fusion of SqueezeNet's features enriches the segmentation process by capturing high-level semantic information. Moreover, the extraction of kernel centers is refined through a weighted k-means approach, contributing further to the segmentation's precision and effectiveness. The proposed method, while exploiting the benefits of suitable computational load of graph cut methods, will be a suitable alternative for segmenting textured images. Laboratory results have been taken on a set of well-known datasets that include textured shapes in order to evaluate the efficiency of the algorithm compared to other well-known methods in the field of kernel graph cut.", acknowledgement = ack-nhfb, articleno = "2550064", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Wu:2025:SOM, author = "Yunhuan Wu and Lei Xiao", title = "Severity-Oriented Multi-Objective Crowdsourced Test Reports Prioritization", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "06", pages = "??--??", month = nov, year = "2025", DOI = "https://doi.org/10.1142/S0219467825500652", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 6 07:40:22 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500652", abstract = "Crowdsourced testing for mobile applications is widespread. Efficiently and swiftly inspecting enormous test reports is a crucial challenge in crowdsourced testing. Most existing methods focus on finding different bugs earlier, i.e. the diversity of bugs, while ignoring bug severity. We present a multi-objective prioritization method for crowdsourced test reports that takes into account the diversity and severity of bugs. In the beginning, we ranked the test reports according to severity, then clustered the test reports by fusing textual and image features, and finally used the clustering results to adjust the initial ranking to ensure diversity. To validate our method, we conducted experiments using an industrial crowdsourced test report dataset comprised of six mobile application projects. The results demonstrate that our method can find more different high-severity bugs earlier than existing methods.", acknowledgement = ack-nhfb, articleno = "2550065", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Sravani:2025:IDF, author = "K. Sravani and V. Ravisankar", title = "Intelligent Differentiation Framework for {Lewy} Body Dementia and {Alzheimer}'s Disease Using Adaptive Multi-Cascaded {ResNet}--Autoencoder--{LSTM} Network", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "06", pages = "??--??", month = nov, year = "2025", DOI = "https://doi.org/10.1142/S0219467825500664", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 6 07:40:22 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500664", abstract = "In recent years, most of the patients with dementia have acquired healthcare systems within the primary care system and they also have some challenging psychiatric and medical issues. Here, dementia-based symptoms are not identified in the primary care center, because they are affected by various factors like psychological symptoms, clinically relevant behavior, numerous psychotropic medications, and multiple chronic medical conditions. To enhance the healthcare-related applications, the primary healthcare system with additional resources like coordination with interdisciplinary dementia specialists, feasible diagnosis, and screening process need to be improved. Therefore, the differentiation between Alzheimer's Disease (AD) and Lewy Body Dementia (LBD) has been acquired to provide the best clinical support to the patients. In this research work, the deep structure depending on AD and LBD systems has been implemented with the help of an adaptive algorithm to provide promising outcomes over dementia detection. Initially, the input images are collected from online sources. Thus, the collected images are forwarded to the newly designed Multi-Cascaded Deep Learning (MSDL), where the ResNet, Autoencoder, and weighted Long-Short Term Memory (LSTM) networks are serially cascaded to provide effective classification results. Then, the fully connected layer of ResNet is given to the Autoencoder structure. Here, the output from the encoder phase is optimized by using the Adaptive Water Wave Cuttlefish Optimization (AWWCO), which is derived from the Water Wave Optimization (WWO) and Cuttlefish Algorithm (CA), and the resultant selected output is fed to the weight-optimized LSTM network. Further, the parameters in the MSDL network are optimized by using the same AWWCO algorithm. Finally, the performance comparison over different heuristic algorithms and conventional dementia detection approaches is done for the validation of the overall effectiveness of the suggested model in terms of various estimation measures.", acknowledgement = ack-nhfb, articleno = "2550066", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Anonymous:2025:AIV, author = "Anonymous", title = "Author Index (Volume 25)", journal = j-INT-J-IMAGE-GRAPHICS, volume = "25", number = "06", pages = "??--??", month = nov, year = "2025", DOI = "https://doi.org/10.1142/S0219467825990013", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 6 07:40:22 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825990013", acknowledgement = ack-nhfb, articleno = "2599001", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Parisae:2026:SUN, author = "Veeraswamy Parisae and S Nagakishore Bhavanam", title = "Stacked {U}-Net with Time--Frequency Attention and Deep Connection Net for Single Channel Speech Enhancement", journal = j-INT-J-IMAGE-GRAPHICS, volume = "26", number = "01", pages = "??--??", month = jan, year = "2026", CODEN = "????", DOI = "https://doi.org/10.1142/S0219467825500676", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 6 07:40:23 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500676", abstract = "Deep neural networks have significantly promoted the progress of speech enhancement technology. However, a great number of speech enhancement approaches are unable to fully utilize context information from various scales, hindering performance enhancement. To tackle this issue, we introduce a method called TFADCSU-Net (Stacked U-Net with Time-Frequency Attention (TFA) and Deep Connection Layer (DCL)) for enhancing noisy speech in the time--frequency domain. TFADCSU-Net adopts an encoder-decoder structure with skip links. Within TFADCSU-Net, a multiscale feature extraction layer (MSFEL) is proposed to effectively capture contextual data from various scales. This allows us to leverage both global and local speech features to enhance the reconstruction of speech signals. Moreover, we incorporate deep connection layer and TFA mechanisms into the network to further improve feature extraction and aggregate utterance level context. The deep connection layer effectively captures rich and precise features by establishing direct connections starting from the initial layer to all subsequent layers, rather than relying on connections from earlier layers to subsequent layers. This approach not only enhances the information flow within the network but also avoids a significant rise in computational complexity as the number of network layers increases. The TFA module consists of two attention branches operating concurrently: one directed towards the temporal dimension and the other towards the frequency dimension. These branches generate distinct forms of attention --- one for identifying relevant time frames and another for selecting frequency wise channels. These attention mechanisms assist the models in discerning ``where'' and ``what'' to prioritize. Subsequently, the TA and FA branches are combined to produce a comprehensive attention map in two dimensions. This map assigns specific attention weights to individual spectral components in the time--frequency representation, enabling the networks to proficiently capture the speech characteristics in the T-F representation. The results confirm that the proposed method outperforms other models in terms of objective speech quality as well as intelligibility.", acknowledgement = ack-nhfb, articleno = "2550067", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Gite:2026:TSG, author = "Kavita R. Gite and Praveen Gupta", title = "{Taylor Shepherd Golden} Optimization-Enabled {ResUNet} for Forest Change Detection Using Satellite Images", journal = j-INT-J-IMAGE-GRAPHICS, volume = "26", number = "01", pages = "??--??", month = jan, year = "2026", DOI = "https://doi.org/10.1142/S0219467825500688", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 6 07:40:23 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500688", abstract = "The pivotal task of remote sensing image (RSI) processing change detection (CD) highly aims to accurately detect changes in land cover based on multi-temporal images. With the advent of deep learning, technology has delivered remarkable results in the last years in the detection of variations in forest land cover data. Some of the conventional CD techniques are weak and are highly susceptible to errors and can result even in inaccurate outcomes. Thus, certain techniques are not desirable for real-time CD applications. To abridge this gap, this research introduces an innovative work for forest CD utilizing the proposed Taylor Shepherd Golden Optimization_ResUNet (TSGO_ResUNet) and Fuzzy Neural network (Fuzzy NN) for segment mapping. Here, the segmentation process is accomplished using ResUNet to determine the exact boundary or shape of each object for every pixel in the image. Furthermore, TSGO is achieved by consolidating Taylor Shuffled Shepherd Optimization (TSSO) with Golden Search Optimization (GSO). In addition, the devised TSGO_ResUNet+ Fuzzy NN has gained maximum accuracy and kappa coefficient of 0.952 and 0.785, and minimum error rate of 0.051.", acknowledgement = ack-nhfb, articleno = "2550068", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Chen:2026:AGA, author = "Meiling Chen and Yao Shi and And Lvfen Zhu", title = "Application of Generative Adversarial Network in Image Color Correction", journal = j-INT-J-IMAGE-GRAPHICS, volume = "26", number = "01", pages = "??--??", month = jan, year = "2026", DOI = "https://doi.org/10.1142/S021946782550069X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 6 07:40:23 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S021946782550069X", abstract = "The popularity of electronic products has increased with the development of technology. Electronic devices allow people to obtain information through the transmission of images. However, color distortion can occur during the transmission process, which may hinder the usefulness of the images. To this end, a deep residual network and a deep convolutional network were used to define the generator and discriminator. Then, self-attention-enhanced convolution was applied to the generator network to construct an image resolution correction model based on coupled generative adversarial networks. On this basis, a generative network model integrating multi-scale features and contextual attention mechanism was constructed to achieve image restoration. Finally, performance and image restoration application tests were conducted on the constructed model. The test showed that when the coupled generative adversarial network was tested on the Set5 dataset, the image peak signal-to-noise ratio and image structure similarity values were 31.2575 and 0.8173. On the Set14 dataset, they were 30.8521 and 0.8079, respectively. The multi-scale feature-fusion algorithm was tested on the BSDS100 dataset with an image peak signal-to-noise ratio of 30.2541 and an image structure similarity value of 0.8352. Based on the data presented, it can be concluded that the image correction model constructed in this study has a strong image restoration ability. The reconstructed image has the highest similarity with the real high-resolution image and a low distortion rate. It can achieve the task of repairing problems such as color distortion during image transmission. In addition, this study can provide technical support for similar information correction and restoration work.", acknowledgement = ack-nhfb, articleno = "2550069", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Gao:2026:PCD, author = "Ming Gao and Zhiyan Zhou and Jinjie Huang and And Kewei Ding", title = "{PECT} Composite Defect Detection Algorithm Based on {DualGAN}", journal = j-INT-J-IMAGE-GRAPHICS, volume = "26", number = "01", pages = "??--??", month = jan, year = "2026", DOI = "https://doi.org/10.1142/S0219467825500706", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 6 07:40:23 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500706", abstract = "To address the problems of insufficient accuracy and slow reconstruction speed of Planar Electrical Capacitance Tomography (PECT) detection of damaged specimens, a Dual Generative Adversarial Networks (DualGAN)-based PECT image defect detection method is proposed in this paper. The improved particle swarm algorithm with adaptive particle number and L2-norm is used to optimize the sensitivity field, combined with the parallel Landweber algorithm to solve the PECT inverse problem to obtain the dielectric constant distribution map. In the DualGAN network, the Unet generator utilizes an Adam-based local attention mechanism to adjust module weights, facilitating feature extraction and the generation of high-quality transformation images of the Landweber dielectric constant distribution. A PatchGAN discriminator is employed to distinguish between transformation images and real images, using the generated transformation images as target images. Experimental results demonstrate that the sensitivity field, enhanced by the improved particle swarm algorithm and L2-norm normalization, achieves better balance. Furthermore, the addition of a network transformation using the Adam-based local attention weight mechanism on the DualGAN network reduces artifacts in the reconstructed images, resulting in more accurate PECT reconstructions. The PECT image defect detection method, integrating DualGAN, an improved particle swarm optimization algorithm, and a local attention mechanism, has made significant strides in addressing challenges related to image reconstruction accuracy and speed. This technological advancement has enhanced the precision and efficiency of defect detection in carbon fiber composite materials, thereby fostering the broader utilization of planar capacitance tomography technology in industrial damage detection and material defect analysis.", acknowledgement = ack-nhfb, articleno = "2550070", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Sulthana:2026:TPS, author = "S. L. Shabana Sulthana and M. Sucharitha", title = "Two-Phase Speckle Noise Removal in {US} Images: Speckle Reducing Improved Anisotropic Diffusion and Optimal {Bayes} Threshold", journal = j-INT-J-IMAGE-GRAPHICS, volume = "26", number = "01", pages = "??--??", month = jan, year = "2026", DOI = "https://doi.org/10.1142/S0219467825500718", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 6 07:40:23 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500718", abstract = "Medial images are contaminated by multiplicative speckle noise, which dramatically reduces ultrasound images and has a detrimental impact on a variety of image interpretation tasks. Hence, to overcome this issue, this paper presented a Two-Phase Speckle Reduction approach with Improved Anisotropic Diffusion and Optimal Bayes Threshold termed TPSR-IADOT, which includes the phases like image enhancement and two-level decomposition processes. Initially, the speckle noise is subjected to an image enhancement process where the Speckle Reducing Improved Anisotropic Diffusion (SRAID) filtering process is carried out for the speckle removal process. Afterwards, two-level decomposition takes place which utilizes Discrete Wavelet Transform (DWT) to remove the residual noise. As the speckle noise is mostly present in the high-frequency band, Improved Bayes Threshold will be applied to the high- frequency subbands. Finally, to provide the best outcomes, an optimization algorithm termed Self Improved Pelican Optimization Algorithm (SI-POA) in this work via choosing the optimal threshold value. The efficiency of the proposed method has been validated on an ultrasound image database using Simulink in terms of PSNR, SSIM, SDME and MAPE. Accordingly, from the analysis, it is proved that the proposed TPSR-IADOT attains the PSNR of 40.074, whereas the POA is 38.572, COOT is 38.572, BES is 37.003, PRO is 30.419, WOA is 33.218, RFU-LA is 29.935 and SSI-COA is 39.256, for noise variance {\= }0.1.", acknowledgement = ack-nhfb, articleno = "2550071", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Sehgal:2026:DRN, author = "Rashmita Sehgal and Dr. Vandana Dixit Kaushik", title = "Deep Residual Network and Wavelet Transform-Based Non-Local Means Filter for Denoising Low-Dose Computed Tomography", journal = j-INT-J-IMAGE-GRAPHICS, volume = "26", number = "01", pages = "??--??", month = jan, year = "2026", DOI = "https://doi.org/10.1142/S021946782550072X", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 6 07:40:23 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S021946782550072X", abstract = "Image denoising helps to strengthen the image statistics and the image processing scenario. Because of the inherent physical difficulties of various recording technologies, images are prone to the emergence of some noise during image acquisition. In the existing methods, poor illumination and atmospheric conditions affect the overall performance. To solve these issues, in this paper Political Taylor-Anti Coronavirus Optimization (Political Taylor-ACVO) algorithm is developed by integrating the features of Political Optimizer (PO) with Taylor series and Anti Coronavirus Optimization (ACVO). The input medical image is subjected to noisy pixel identification step, in which the deep residual network (DRN) is used to discover noise values and then pixel restoration process is performed by the created Political Taylor-ACVO algorithm. Thereafter image enhancement mechanism strategy is done using vectorial total variation (VTV) norm. On the other hand, original image is applied to discrete wavelet transform (DWT) such that transformed result is fed to non-local means (NLM) filter. An inverse discrete wavelet transform (IDWT) is utilized to the filtered outcome for generating the denoised image. Finally, image enhancement result is fused with denoised image computed through filtering model to compute fused output image. The proposed model observed the value for Peak signal-to-noise ratio (PSNR) of 29.167 dB, Second Derivative like Measure of Enhancement (SDME) of 41.02 dB, and Structural Similarity Index (SSIM) of 0.880 for Gaussian noise.", acknowledgement = ack-nhfb, articleno = "2550072", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Balamurugan:2026:DAR, author = "M. Balamurugan and R. Balamurugan", title = "Double attention {Res-U-Net}-based Deep Neural Network Model for Automatic Detection of Tuberculosis in Human Lungs", journal = j-INT-J-IMAGE-GRAPHICS, volume = "26", number = "01", pages = "??--??", month = jan, year = "2026", DOI = "https://doi.org/10.1142/S0219467825500731", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 6 07:40:23 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500731", abstract = "Tuberculosis (TB) stands as the leading cause of death and a significant threat to humanity in the contemporary world. Early detection of TB is crucial for precise identification and treatment, and Chest X-Rays (CXR) serve as a valuable tool in this regard. Computer-Aided Diagnosis (CAD) systems play a vital role in easing the classification process of active and latent TB. This paper uses an approach called the Double Attention Res-U-Net-based Deep Neural Network (DARUNDNN) to enhance TB detection in the lungs. The detection process involves pre-processing, noise removal, image level balancing, the application of the DARUNDNN model and using the Whale Optimization Algorithm (WOA) for improved accuracy. Experimental validation using Montgomery Country (MC), Shenzhen China (SC), and NIH CXR Datasets compares the results with U-Net, AlexNet, GoogleNet, and convolutional neural network (CNN) models. The findings, particularly from the SC dataset, demonstrate the efficiency of the proposed DARUNDNN model with an accuracy of 98.6%, specificity of 96.24%, and sensitivity of 97.66%, outperforming benchmarked deep learning models. Additionally, validation with the MC dataset reveals an excellent accuracy of 98%, specificity of 97.56%, and sensitivity of 98.52%.", acknowledgement = ack-nhfb, articleno = "2550073", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Wang:2026:MAO, author = "Chencheng Wang and Lijuan Pu and Zhihui Zhao and And Jiefu Zhang", title = "A Method for Analyzing the Operating Data of Electric Energy Meters Based on Data Mining Analysis", journal = j-INT-J-IMAGE-GRAPHICS, volume = "26", number = "01", pages = "??--??", month = jan, year = "2026", DOI = "https://doi.org/10.1142/S0219467826500014", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 6 07:40:23 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467826500014", abstract = "Aiming at the problem of error estimation of smart meters in distribution network, a method of error estimation of smart meters based on particle swarm optimization convolutional neural network is proposed. This method establishes an intelligent energy meter error estimation model through data collection, data prediction, and preprocessing. To address the convergence issue in training, the interlayer distribution of weights is adjusted to improve training quality. This method fully utilizes template calibration information to transform indicator detection under complex conditions into simple and effective isometric segmentation, transforming label recognition from complex text detection and recognition tasks to simple and efficient binary detection tasks, with better robustness. The effectiveness and high robustness of the proposed method have been demonstrated through experimental verification.", acknowledgement = ack-nhfb, articleno = "2650001", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Sreela:2026:SSA, author = "S. R. Sreela and Sumam Mary Idicula", title = "A Systematic Survey of Automatic Image Description Generation Systems", journal = j-INT-J-IMAGE-GRAPHICS, volume = "26", number = "01", pages = "??--??", month = jan, year = "2026", DOI = "https://doi.org/10.1142/S0219467826500026", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 6 07:40:23 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467826500026", abstract = "Automatic image description generation is an active research area in Computer Vision and Natural Language Processing. Objects, their attributes, actions, and spatial relationships are identified in the image description generation system. Earlier, these systems used classical machine learning approaches. Later majority of these works follow deep learning strategies. The essential goal of these systems is to produce syntactically and semantically correct sentences. This review aims to synthesize the studies conducted from 2010 to 2023 to get a deeper view of various image description generation systems and their applications. The prominent contribution of this review is that it covers the different aspects of image captioning systems, such as the methods used, the various applied domains, evaluation measures, and the datasets used. A single synthesized study directs scholars regarding developing image captioning systems to date utilizing machine learning approaches. It also offers suggestions for researchers in this sector for the future. Image captioning is applied in many fields like natural images, medical images, remote sensing images, videos, etc. This review paper reviews the various taxonomies of image description generation systems. We also analyzed multiple methods used in the architecture of image captioning systems. The datasets and the evaluation metrics used in these systems are discussed. We studied the performance of the system under various circumstances.", acknowledgement = ack-nhfb, articleno = "2650002", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", } @Article{Kurva:2026:DTB, author = "Thirupathanna Kurva and Mudigonda Malini", title = "Dilated {TransUNet++}-Based Segmentation with Multi-Scale Adaptive {DenseNet} with Bi-{LSTM} Layer-Aided Prostate Cancer Classification Model", journal = j-INT-J-IMAGE-GRAPHICS, volume = "26", number = "01", pages = "??--??", month = jan, year = "2026", DOI = "https://doi.org/10.1142/S0219467826500038", ISSN = "0219-4678", ISSN-L = "0219-4678", bibdate = "Thu Nov 6 07:40:23 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib", URL = "https://www.worldscientific.com/doi/10.1142/S0219467826500038", abstract = "In common, prostate cancer is regarded as the type of cancer, which occurs over the small walnut-shaped gland in men termed as the prostate. In addition to that, the prostate is considered as the most generally identified type of cancer among men. Here, the gland has been aided in the production of seminal fluid that has been utilized for transporting and nourishing the sperm. In order to exclude the existence of cancer in the tissues, prostate biopsy techniques are utilized. Moreover, the mortality rate due to this disease may be low in the last few years, but it is regarded as the leading cause of cancer. In this case, the automated intelligent techniques are helpful for aiding the pathologists in minimizing fatigue and enhancing the routing process. Moreover, there are some limitations in the traditional model, and it is tackled with the help of a new prostate cancer segmentation and classification approach. Firstly, images related to prostate cancer are attained from standard resources and offered as input to lesion segmentation. Here, lesion segmentation is performed with the help of Adaptive Dilated TransUNet++ to get the segmented image features. The parameters of Dilated TransUNet++ are tuned by utilizing a hybrid approach named Position-aided Pelican-Sea Lion Optimization (PPSLO). Then, the segmented images are offered as the input to Region-of-Interest (ROI) cropping, and the ROI cropped image is attained as the output. Further, the ROI cropped image is fed as the input to the prostate cancer classification phase. In this phase, prostate cancer is classified using Multiscale Adaptive DenseNet with Bi-directional Long Short Term Memory (MAD-Bi-LSTM) Layer, in which the parameters in the network are tuned by the developed approach PPSLO. Hence, the developed prostate cancer segmentation and classification model helps for securing an enhanced disease classification rate than other experimental observations.", acknowledgement = ack-nhfb, articleno = "2650003", fjournal = "International Journal of Image and Graphics (IJIG)", journal-URL = "http://www.worldscientific.com/worldscinet/ijig", }