Image Processing and Computer Vision

Digital image processing is the study of theories, models and algorithms for the manipulation of images (usually by computer). It spans a wide variety of topics such as digitization, histogram manipulation, warping, filtering, segmentation, restoration and compression. Computer vision deals with theories and algorithms for automating the process of visual perception, and involves tasks such as noise removal, smoothing, and sharpening of edges (low-level vision); segmentation of images to isolate object regions, and description of the segmented regions (intermediate-level vision); and finally, interpretation of the scene (high-level vision). Thus, there is much overlap between these two fields. In this chapter, we concentrate on some of the aspects of image processing and computer vision in which a fuzzy approach has had an impact. We begin with some notation and definitions used throughout the chapter.

[1]  Michio Sugeno,et al.  A study on subjective evaluations of printed color images , 1991, Int. J. Approx. Reason..

[2]  G.B. Coleman,et al.  Image segmentation by clustering , 1979, Proceedings of the IEEE.

[3]  Julius T. Tou,et al.  Pattern Recognition Principles , 1974 .

[4]  Nicolaos B. Karayiannis,et al.  Compression of digital mammograms using wavelets and learning vector quantization , 1997, Electronic Imaging.

[5]  Cezary Z. Janikow,et al.  Exemplar learning in fuzzy decision trees , 1996, Proceedings of IEEE 5th International Fuzzy Systems.

[6]  Peter J. Rousseeuw,et al.  Fuzzy clustering algorithms based on the maximum likelihood principle , 1991 .

[7]  John W. Tukey,et al.  Exploratory Data Analysis , 1980, ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems.

[8]  Sudeep Sarkar,et al.  Robust Visual Method for Assessing the Relative Performance of Edge-Detection Algorithms , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Pietro Laface,et al.  Use of Fuzzy Algorithms for Phonetic and Phonemic Labeling of Continuous Speech , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Edward R. Dougherty,et al.  Design and analysis of fuzzy morphological algorithms for image processing , 1997, IEEE Trans. Fuzzy Syst..

[11]  Ronald L. Iman,et al.  On a method for detecting clusters of possible uranium deposits , 1979 .

[12]  J Frank,et al.  Fuzzy sets‐based classification of electron microscopy images of biological macromolecules with an application to ribosomal particles , 1990, Journal of microscopy.

[13]  Hong Yan,et al.  Fuzzy Algorithms: With Applications to Image Processing and Pattern Recognition , 1996, Advances in Fuzzy Systems - Applications and Theory.

[14]  Cezary Z. Janikow,et al.  Fuzzy decision trees: issues and methods , 1998, IEEE Trans. Syst. Man Cybern. Part B.

[15]  James M. Keller,et al.  Fuzzy patch label relaxation in bone marrow cell segmentation , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[16]  Sankar K. Pal,et al.  A review on image segmentation techniques , 1993, Pattern Recognit..

[17]  Rajesh N. Dave,et al.  Fuzzy ellipsoidal shell clustering algorithm and detection of elliptical shapes , 1991, Other Conferences.

[18]  R.N. Dave,et al.  Robust fuzzy clustering algorithms , 1993, [Proceedings 1993] Second IEEE International Conference on Fuzzy Systems.

[19]  J.-S.R. Jang,et al.  Structure determination in fuzzy modeling: a fuzzy CART approach , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

[20]  Atsuko Mutoh,et al.  Fuzzy reasoning for image compression using adaptive triangular plane patches , 1999, Fuzzy Sets Syst..

[21]  Cezary Z. Janikow,et al.  A genetic algorithm method for optimizing fuzzy decision trees , 1996 .

[22]  King-Sun Fu,et al.  Error-Correcting Isomorphisms of Attributed Relational Graphs for Pattern Analysis , 1979, IEEE Transactions on Systems, Man, and Cybernetics.

[23]  R.N. Dave.  Boundary detection through fuzzy clustering , 1992, [1992 Proceedings] IEEE International Conference on Fuzzy Systems.

[24]  J. L. Johnson.  Pulse-coupled neural nets: translation, rotation, scale, distortion, and intensity signal invariance for images. , 1994, Applied optics.

[25]  A. F. Smith,et al.  Statistical analysis of finite mixture distributions , 1986 .

[26]  Anil K. Jain,et al.  Algorithms for Clustering Data , 1988 .

[27]  Nicolaos B. Karayiannis,et al.  Fuzzy algorithms for learning vector quantization , 1996, IEEE Trans. Neural Networks.

[28]  Divyendu Sinha,et al.  Fuzzy mathematical morphology , 1992, J. Vis. Commun. Image Represent..

[29]  E. Trauwaert.  On the meaning of Dunn's partition coefficient for fuzzy clusters , 1988 .

[30]  Thomas A. Kent,et al.  A two-stage human brain MRI segmentation scheme using fuzzy logic , 1995, Proceedings of 1995 IEEE International Conference on Fuzzy Systems..

[31]  M.C. de Oliveira,et al.  Texture analysis for discrimination of tissues in MRI data , 1991, [1991] Proceedings Computers in Cardiology.

[32]  Rangachar Kasturi,et al.  Machine vision , 1995 .

[33]  Josef Kittler,et al.  The Adaptive Hough Transform , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[34]  J. B. Jordan,et al.  On the optimal choice of parameters in a fuzzy c-means algorithm , 1992, [1992 Proceedings] IEEE International Conference on Fuzzy Systems.

[35]  R. L. Thorndike.  Who belongs in the family? , 1953 .

[36]  Thomas A. Kent,et al.  Segmentation of rat brain MR images using a hybrid fuzzy system , 1994, NAFIPS/IFIS/NASA '94. Proceedings of the First International Joint Conference of The North American Fuzzy Information Processing Society Biannual Conference. The Industrial Fuzzy Control and Intellige.

[37]  Paul D. Gader,et al.  Dynamic-programming-based handwritten word recognition using the Choquet fuzzy integral as the match function , 1996, J. Electronic Imaging.

[38]  Michael G. Thomason,et al.  Finite fuzzy automata, regular fuzzy languages, and pattern recognition , 1973, Pattern Recognit..

[39]  Bart Kosko,et al.  Neural networks and fuzzy systems , 1998 .

[40]  Mohan M. Trivedi,et al.  Low-Level Segmentation of Aerial Images with Fuzzy Clustering , 1986, IEEE Transactions on Systems, Man, and Cybernetics.

[41]  Nicolaos B. Karayiannis,et al.  Entropy-constrained learning vector quantization algorithms and their application in image compression , 1997, Electronic Imaging.

[42]  Godfried T. Toussaint,et al.  Bibliography on estimation of misclassification , 1974, IEEE Trans. Inf. Theory.

[43]  Edward R. Dougherty,et al.  A general axiomatic theory of intrinsically fuzzy mathematical morphologies , 1995, IEEE Trans. Fuzzy Syst..

[44]  Yannis A. Tolias,et al.  Image segmentation by a fuzzy clustering algorithm using adaptive spatially constrained membership functions , 1998, IEEE Trans. Syst. Man Cybern. Part A.