Anisotropic Wavelet-Based Image Nearness Measure

References

• Bruce , V. , Green , P. R. and Georgeson , M. A. 1996 . Visual perception: physiology, psychology, and ecology , Hove, East Sussex , , UK : Psychology Press .
   Google Scholar
• Candès , E. J. and Donoho , D. L. 1999 . Curvelets – a surprisingly effective nonadaptive representation for objects with edges http://www-stat.stanford.edu/~donoho/ Reports/1999/curveletsurprise.pdf
   Google Scholar
• Candès , E. J. and Donoho , D. L. 2002 . New tight frames of curvelets and optimal representations of objects with piecewise-c2 singularities . Comm. Pure Appl. Math , 57 : 219 – 266 .
   Web of Science ®Google Scholar
• Do , M. N. and Vetterli , M. 2005 . The contourlet transform: an efficient directional multiresolution image representation . IEEE Transactions on Image Processing , 14 ( 12 ) : 2091 – 2106 .
   PubMed Web of Science ®Google Scholar
• Brunner , G. 2006 . Structure features for content-based image retrieval and classification problems , Albert-Ludwigs-Universität Freiburg . Ph.d. diss
   Google Scholar
• Fashandi , H. , Peters , J. and Ramanna , S. 2009 . l2 norm length-based image similarity measures: Concrescence of image feature histogram distances . Signal and Image Processing , in press
   Google Scholar
• Angulo , J. and Serra , J. 2002 . Morphological color size distribution for image classification and retrieval . Proc. Advanced Concepts for Intelligent Vision Systems . 2002 , Ghent , Belgium.
   Google Scholar
• Deselares , T. 2003 . Features for image retrival , Rheinisch-Westfalische Technische Hochschule Aachen . Phd diss
   Google Scholar
• Bartol , W. , Miró , J. , Pióro , K. and Rosselló , F. 2004 . On the coverings by tolerance classes . Inf. Sci. Inf. Comput. Sci. , 166 ( 1-4 ) : 193 – 211 .
   Google Scholar
• Geetha , M. and Palanivel , S. 2009 . Video classification and shot detection for video retrieval applications . International Journal of Computational Intelligence Systems , 2 ( 1 ) : 39 – 50 .
   Web of Science ®Google Scholar
• Gerasin , S. N. , Shlyakhov , V. V. and Yakovlev , S. V. 2008 . Set coverings and tolerance relations . Cybernetics and Sys. Anal. , 44 ( 3 ) : 333 – 340 .
   Google Scholar
• Gope , C. and Kehtarnavaz , N. 2007 . Affine invariant comparison of point-sets using convex hulls and hausdorff distances . Pattern Recogn. , 40 ( 1 ) : 309 – 320 .
   Web of Science ®Google Scholar
• Hassanien , A. E. , Abraham , A. , Peters , J. F. , Schaefer , G. and Henry , C. in press . Rough sets and near sets in medical imaging: A review . IEEE Transactions on Information Technology in Biomedicine (2009) digital object identifier: 10.1109/TITB.2009.2017017 , http://ieeexplore.ieee.org/xpl/ tocpreprint.jsp?isnumber=4358869&punumber= 4233
   Google Scholar
• Hausdorff , F. 1919 . Dimension und äusseres mass . Math. Ann. , 79 : 157 – 179 .
   Google Scholar
• Henry , C. and Peters , J. Near set evaluation and recognition (near) system, Tech. rep., Computationa Intelligence Laboratory, University of Manitoba UM CI LABORATORY TECHNICAL REPORT No. TR-2009-015 (2000) http://wren.ee.umanitoba.ca/images/ci\ _reports/reportci-2009-015.pdf
   Google Scholar
• Henry , C. and Peters , J. Perception-based image analysis . Int. J. of Bio-Inspired Computation , 2 ( 2 ) in press
   Google Scholar
• Henry , C. and Peters , J. F. Image pattern recognition using approximation spaces and near sets . : Proc. of the Eleventh International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computer (RSFDGrC 2007), Joint Rough Set Symposium (JRS07), Lecture Notes in Artificial Intelligence . vol. 4482 ,
   Google Scholar
• Henry , C. and Peters , J. F. 2008 . “ Near set index in an objective image segmentation evaluation framework ” . In GEOgraphic Object Based Image Analysis: Pixels, Objects, Intelligence , Alberta : University of Calgary .
   Google Scholar
• Henry , C. and Peters , J. F. Near sets , Wikipedia . http://en.wikipedia.org/wiki/Near_sets
   Google Scholar
• Ilonen , J. 2007 . Supervised local image feature detection , Lappeenranta : Lappeenrannan teknillinen yliopisto . Phd diss
   Google Scholar
• Jänich , K. 1984 . Topology , Berlin : Springer-Verlag .
   Google Scholar
• Kämäräinen , J. 2003 . Feature extraction using gabor filters , Lappeenranta : Lappeenrannan teknillinen yliopisto . Phd diss
   Google Scholar
• Mahalanobis , P. 1930 . On tests and measures of group divergence i. theoretical formulae . J. and Proc. Asiat. Soc. of Bengal , 26 : 541 – 588 .
   Google Scholar
• Mahalanobis , P. On the generalized distance in statistics . Proc. Nat. Institute of Science (Calcutta) . Vol. 2 , pp. 49 – 55 .
   Google Scholar
• Mallat , S. G. 1999 . A Wavelet Tour of Signal Processing , Academic Press .
   Google Scholar
• Meghdadi , A. , Peters , J. and Ramanna , S. 2009 . Tolerance classes in measuring image resemblance . Intelligent Analysis of Images & Videos , in press
   Google Scholar
• Orłowska , E. 1982 . Semantics of vague concepts. applications of rough sets, Tech. Rep. 469 , Institute for Computer Science, Polish Academy of Sciences .
   Google Scholar
• Orłowska , E. 1985 . “ Semantics of vague concepts ” . In Foundations of Logic and Linguistics , Edited by: Dorn , G. and P.Weingartner . 465 – 482 . London/NY : Problems and Solutions, Plenum Pres .
   Google Scholar
• Pavel , M. 1993 . Fundamentals of Pattern Recognition , 2nd ed. , N.Y. , , U.S.A. : Marcel Dekker, Inc .
   Google Scholar
• Pawlak , Z. 1981 . Classification of objects by means of attributes, Tech. Rep. PAS 429 , Institute for Computer Science, Polish Academy of Sciences .
   Google Scholar
• Pawlak , Z. 1982 . Rough sets, International Journal of Computer and Information Sciences , 11 : 341 – 356 .
   Web of Science ®Google Scholar
• Pawlak , Z. and Peters , J. 2002, 2007 . Jak blisko (how near), Systemy Wspomagania Decyzji I Vol. 57 , 109
   Google Scholar
• Pawlak , Z. and Skowron , A. 2007 . Rudiments of rough sets, Information Sciences , 177 : 3 – 27 .
   Web of Science ®Google Scholar
• Peters , J. 2007 . Classification of objects by means of features . Proceedings of the IEEE Symposium Series on Foundations of Computational Intelligence (IEEE SCCI 2007) . 2007 , Honolulu , Hawaii .
   Google Scholar
• Peters , J. 2007 . Near sets. general theory about nearness of objects . Applied Mathematical Sciences , 1 ( 53 ) : 2609 – 2629 .
   Google Scholar
• Peters , J. 2007 . Near sets. special theory about nearness of objects . Fundamenta Informaticae , 76 : 1 – 27 .
   Google Scholar
• Peters , J. 2008 . Classification of perceptual objects by means of features . Int. J. of Info. Technology & Intell. Computing , 3 ( 2 ) : 1 – 35 .
   Google Scholar
• Peters , J. 2009 . “ Discovery of perceptually near information granules ” . In Novel Developements in Granular Computing: Applications of Advanced Human Reasoning and Soft Computation, Information Science Reference Edited by: Yao , J. T. Hersey, N.Y , , USA p. in press
   Google Scholar
• Peters , J. 2009 . Tolerance near sets and image correspondence . Int. J. of Bio-Inspired Computation , 4 ( 1 ) : 239 – 445 .
   Google Scholar
• Peters , J. and Puzio , L. 2009 . Measuring nearness of rehabilitation hand images with finely-tuned anisotropic wavelets . 1st Int. Conf. on Image Processing & Communications . 2009 , Bydgoszcz , Poland.
   Google Scholar
• Peters , J. F. 2009 . “ Fuzzy sets, near sets, and rough sets for your computational intelligence toolbox ” . In Foundations of Computational Intelligence, Volume 2 , Approximate Reasoning Series: Studies in Computational Intelligence Edited by: Hassanien , A.-E. , Abraham , A. and Herrera , F. Vol. 202 , Berlin : Springer-Verlag . iSBN: 978-3-642-01532-8
   Google Scholar
• Peters , J. F. , Puzio , L. and Szturm , T. 2009 . Measuring nearness of rehabilitation hand images with finely-tuned anisotropic wavelets . Int. Conf. on Image Processing & Communication . 2009 . submitted
   Google Scholar
• Peters , J. F. and Ramanna , S. 2008 . “ Affinities between perceptual granules: Foundations and perspectives ” . In Human-Centric Information Processing Through Granular Modelling , Edited by: Bargiela , A. and Pedrycz , W. 49 – 66 . Berlin : Springer-Verlag .
   Google Scholar
• Peters , J. F. and Wasilewski , P. in press . Foundations of near sets, Information Sciences . An International Journal (2009) digital object identifier: doi:10.1016/j.ins.2009.04.018 , http://dx.doi.org/10.1016/j.ins.2009.04.018
   Google Scholar
• Poincaré , H. 1965 . “ The topology of the brain and the visual perception ” . In Topology of 3-manifolds and Selected Topics , Edited by: Fort , K. M. 240 – 256 . New Jersey : Prentice Hall .
   Google Scholar
• Puzio , L. 2008 . Adaptive edge extraction method for images , Military University of Technology in Warsaw . Ph.D. thesis
   Google Scholar
• Puzio , L. and Walczak , A. 2005 . “ 2-d wavelet with position controlled resolution ” . In Medical Imaging , Vol. 5959 , Warsaw : SPIE .
   Google Scholar
• Puzio , L. and Walczak , A. 2008 . Adaptive edge detection method for images . Opto-Electronics Review , 16 ( 1 ) : 60 – 67 .
   Web of Science ®Google Scholar
• Rogers , C. 1970 . Hausdorff Measures , Cambridge , , UK : Cambridge U Press .
   Google Scholar
• Rucklidge , W. 1996 . Efficient Visual Recognition Using the Hausdorff Distance , Berlin : Springer-Verlag .
   Google Scholar
• Sanderson , C. and Paliwal , K. 2002 . Fast feature extraction method for robust face verification . Electronics Letters , 38 ( 25 ) : 1648 – 1650 .
   Web of Science ®Google Scholar
• Schroeder , M. and Wright , M. 1992 . Tolerance and weak tolerance relations . Journal of Combinatorial Mathematics and Combinatorial Computing , 11 : 123 – 160 .
   Google Scholar
• Shreider , Y. A. 1970 . Tolerance spaces, Cybernetics and Systems Analysis , 6 ( 12 ) : 153 – 758 .
   Google Scholar
• Skowron , A. and Stepaniuk , J. 1996 . Tolerance Approximation Spaces, Fundamenta Informaticae , 27 ( 2/3 ) : 245 – 253 .
   Google Scholar
• Sossinsky , A. 1986 . Tolerance space theory and some applications . Acta Applicandae Mathematicae: An International Survey Journal on Applying Mathematics and Mathematical Applications , 2 : 137 – 167 .
   Google Scholar
• Velisavljevic , V. , Beferull-Lozano , B. , Vetterli , M. and Dragotti , P. L. 2006 . Directionlets: Anisotropic Multidirectional representation with separable filtering . IEEE Transactions on Image Processing , 15 ( 7 ) : 1916 – 1933 .
   PubMed Web of Science ®Google Scholar
• itte , V. D. W. , Schulte , S. and Kerre , E. 2008 . New vector ordering in the redgreenblue colour model with application to morphological image magnification . International Journal of Computational Intelligence Systems , 1 ( 2 ) : 103 – 115 .
   Web of Science ®Google Scholar
• Zeeman , E. 1965 . “ The topology of the brain and the visual perception ” . In Topology of 3-manifolds and Selected Topics , Edited by: Fort , K. M. 240 – 256 . New Jersey : Prentice Hall .
   Google Scholar
• Zheng , Z. , Hu , H. and Shi , Z. 2005 . Tolerance Relation Based Granular Space . Lecture Notes in Computer Science , 3641 : 682
   Google Scholar