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Computational Intelligence: Soft Computing and Fuzzy-Neuro Integration with Applications pp 434–449Cite as

Fuzzy Sets and the Management of Uncertainty in Computer Vision

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Part of the book series: NATO ASI Series ((NATO ASI F,volume 162))

Abstract

Visual perception is a difficult task to automate. This process, known as computer vision, has received a considerable amount of attention for the last three or four decades. Even with all of the research and development efforts, relatively few real computer vision systems have been put into routine use - these being primarily in controlled environments. Yet, the potential of general purpose vision systems which can effectively operate in varying scenarios is so great that much research continues to be devoted to the components of a computer vision system. These components include tasks such as noise removal, smoothing, and sharpening of contrast (low-level vision); segmentation of images to isolate objects and regions and description and recognition of the segmented regions (intermediate-level vision); and finally interpretation of the scene (high-level vision). Uncertainty exists in every phase of computer vision. Some of the sources of this uncertainty include: additive and non-additive noise of various sorts and distributions in low-level vision, imprecisions in computations and vagueness in class definitions in intermediate-level vision, and ambiguities in interpretations and ill-posed questions in high-level vision. The use of multiple image sources can aid in making better judgements about scene content, but the use of more than one source of information poses new questions of how the complementary and supplementary information should be combined, how redundant information should be treated and how conflicts should be resolved.

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References

  • D.Man, Vision, W. H. Freeman and Company, San Francisco California, 1982.

    Google Scholar 

  • J. Keller and R. Krishnapuram, “Fuzzy Set Methods in Computer Vision” in An Introduction to Fuzzy Logic Applications in Intelligent Systems, R. Yager and L. Zadeh (eds), 1992, pp. 121–146.

    Chapter  Google Scholar 

  • R. Krishnapuram and J. Keller, “Fuzzy Set theoretic Approach to Computer Vision: An Overview”, Invited Paper, Proceedings, IEEE International Conference on Fuzzy Systems, San Diego, CA, 1992, pp. 135–142.

    Google Scholar 

  • J. Keller, “Fuzzy Logic and Neural Networks in Computer Vision”, Video Tutorial, IEEE Press, 1992.

    Google Scholar 

  • R. Krishnapuram and J. Keller, “Fuzzy and Possibilistic Clustering Methods for Computer Vision”, in Neural and Fuzzy Systems, S. Mitra, M. Gupta and W. Kraske (eds), SPIE Press, 1994, pp. 133–159.

    Google Scholar 

  • J. Keller and R. Krishnapuram, “Fuzzy Decision Models in Computer Vision” in Fuzzy Sets, Neural Networks, and Soft Computing, R. Yager and L. Zadeh (eds), Van Nostrand, 1994, pp. 213–232.

    Google Scholar 

  • J. Keller, R. Krishnapuram, P. Gader, and Y.-S. Choi, “Fuzzy Rule-Based Models in Computer Vision,” in Fuzzy Modelling: Paradigms and Practice, W. Pedrycz, Ed.: Kluwer Academic Publishers, 1996, pp. 353–371.

    Chapter  Google Scholar 

  • S. K. Pal, “Fuzzy Sets in Image Processing and Recognition,” Proceedings,First IEEE International Conference on Fuzzy Systems, San Diego, pp. 119–126, March 8–12, 1992.

    Google Scholar 

  • S.K. Pal, and R.A. King. “Image enhancement using smoothing with fuzzy sets,” IEEE Transactions on System, Man, and Cybernetics, Vol. SMC-11, 1981, pp. 494–501.

    Google Scholar 

  • S.K. Pal, “A Note on the Quantitative measure of image enhancement through fuzziness”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-4, no. 2, 1982, pp. 204–208.

    Article  Google Scholar 

  • T.K. De and B.N. Chatterji, “An Approach to a generalized technique for image contrast enhancement using the concept of fuzzy set”, Fuzzy Sets and Systems, vol. 25, 1988, pp. 145–158.

    Article  Google Scholar 

  • S.K. Pal, and R.A. King. “Histogram equalization with S and • functions in detecting x-ray edges”, Electronics Letters, Vol. 17, 1981, pp. 302–304.

    Article  Google Scholar 

  • O. AlShaykh, S. Ramaswamy, and H. Hung, “Fuzzy Techniques for Image Enhancement and Reconstruction”, Proceedings,Second IEEE International Conference on Fuzzy Systems, San Francisco, CA, 1993, pp. 582–587.

    Google Scholar 

  • F. Russo, “A New Class of Fuzzy Operators for Image Processing: Design and Implementation”, Proceedings, Second IEEE International Conference on Fuzzy Systems, San Francisco, CA, 1993, pp. 815–820.

    Google Scholar 

  • F. Russo and G. Ramponi, “Combined FIRE Filters for Image Enhancement,” Proceedings, Third IEEE International Conference on Fuzzy Systems, Orlando, FL, pp. 260–264, June, 1994.

    Google Scholar 

  • F. Russo and G. Ramponi, “Edge Extraction by FIRE Operators,” Proceedings, Third IEEE International Conference on Fuzzy Systems, Orlando, pp. 249–253, June, 1994.

    Google Scholar 

  • M. Mancuso, R. Poluzzi, and G. Rizzotto, “A Fuzzy Filter for Dynamic Range Reduction and Contrast Enhancement,” Proceedings, Third IEEE International Conference on Fuzzy Systems, Orlando, FL, pp. 264–267, June, 1994.

    Google Scholar 

  • S. Peng and L. Lucke, “Fuzzy Filtering for Mixed Noise Removal During Image Processing,” Proceedings,Third IEEE International Conference on Fuzzy Systems, Orlando, FL, pp. 89–93, June, 1994.

    Google Scholar 

  • B. Chen, Y. Chen, and W. Hsu, “Image Processing and Understanding Based on the Fuzzy Inference Approach,” Proceedings, Third IEEE International Conference on Fuzzy Systems, Orlando, FL, pp. 254–259, June, 1994.

    Chapter  Google Scholar 

  • F. Russo and G. Ramponi, “Fuzzy Operator for Sharpening of Noisy Images,” IEE Electronics Letters, vol. 28, no. 18, pp. 1715–1717, 1992.

    Article  Google Scholar 

  • C. Tyan and P. Wang, “Image Processing - Enhancement, Filtering and Edge Detection Using the Fuzzy Logic Approach,” Proceedings, Second IEEE International Conference on Fuzzy Systems, San Francisco, pp. 600–605, March, 1993.

    Google Scholar 

  • M. Grabisch, “Fuzzy Integrals as a General Class of Order Filters”, Proceedings of the European Symposium on Satellite Remote Sensing, Rome, Italy, 1994.

    Google Scholar 

  • M. Grabisch and M. Schmitt, “Mathematical Morphology, Order Filters, and Fuzzy Logic”, Proceedings, Fourth IEEE International Conference on Fuzzy Systems, Yokohama, Japan, pp. 2103–2108, March, 1995.

    Google Scholar 

  • H. Shi, P. Gader, and J. Keller, “An 0(K)-Time Implementation of Fuzzy Integral Filters on an Enhanced Mesh Processor Array”, Proceedings,Fifth IEEE International Congress on Fuzzy Systems, New Orleans, LA, September, 1996, pp. 1086–1091.

    Google Scholar 

  • J. Keller, H. Qiu, and H. Tahani, “The fuzzy integral in image segmentation”, Proceedings of NAFIPS Workshop,New Orleans, June 1986, pp. 324–338.

    Google Scholar 

  • R Sankar, “Improvements in image enhancement using fuzzy sets”, Proceedings of NAFIPS Workshop, New Orleans, June 2–4, 1986, pp. 502–515.

    Google Scholar 

  • A. Rosenfeld, “Fuzzy digital topology”, Information and Control, 40, 1979, pp. 76–87.

    Article  MathSciNet  MATH  Google Scholar 

  • A. Rosenfeld, “On connectivity properties of gray scale pictures”, Pattern Recognition, 16, 1983, pp. 47–50.

    Article  Google Scholar 

  • A. Rosenfeld, “The fuzzy geometry of image subsets”, Pattern Recognition Letters, 2, 1984, pp. 311–317.

    Article  Google Scholar 

  • D. Dubois and M.C. Jaulent, “A general approach to parameter evaluations in fuzzy digital pictures”, Pattern Recognition Letters, Vol. 6, 1987, pp. 251–259.

    Article  MATH  Google Scholar 

  • S.K. Pal, R.A. King and A.A. Hishim, “Automatic grey level thresholding through index of fuzziness and entropy”, Pattern Recognition Letters, vol. 1, 1983, pp. 141–146.

    Article  Google Scholar 

  • S.K. Pal, “A measure of edge ambiguity using fuzzy sets”, Pattern Recog Letters, vol. 4, 1986, pp. 51–56.

    Article  Google Scholar 

  • S.K. Pal and A. Ghosh, “Index of area coverage of fuzzy image subsets and object extraction”, Pattern Recognition Letters, vol. 11, 1990, pp. 831–841.

    Article  MATH  Google Scholar 

  • Y. Nakagawa, and A. Rosenfeld, “A note on the use of local min and max operators in digital picture processing,” IEEE Transactions on System, Man and Cybernetics, Vol. SMC-8, 1978, pp. 632–635.

    Google Scholar 

  • S.K. Pal, and R.A. King. On edge detection of x-ray images using fuzzy sets,“ IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-5, 1983, pp. 69–77.

    Article  Google Scholar 

  • M.M. Gupta, G.K. Knopf, and P.N. Mikiforuk, “Edge Perception Using Fuzzy Logic”, in Fuzzy Computing: Theory Hardware and Applications, North-Holland, 1988.

    Google Scholar 

  • T. Huntsberger, and M. Desclazi, “Color edge detection”, Pattern Recognition Letters, 3, 1985, 205.

    Article  Google Scholar 

  • R. Krishnapuram and L. Chen, “Implementation of Parallel Thinning Algorithms Using Iterative Neural Networks”, to appear in the IEEE Transactions on Neural Networks, 1992.

    Google Scholar 

  • D. Sinha and E. Dougherty, “An Intrinsically Fuzzy Approach to Mathematical Morphology”, SPIE Image Algebra and Morphological Image Processing III, San Diego CA, July 1992.

    Book  Google Scholar 

  • P. Gader, “Fuzzy Morphological Networks”, Proc. First Midwest Electro-Technology Conference, Ames I, April 1992, pp. 70–74.

    Google Scholar 

  • J.M. Prewitt, “Object enhancement and extraction”, in Picture Processing and Psychopictorics, B.S. Lipkin and A. Rosenfeld (Eds.), Academic Press, New York, 1970, pp. 75–149.

    Google Scholar 

  • C.A. Murthy and S.K. Pal, “Fuzzy thresholding: mathematical framework, bound functions and weighted moving average technique”, Pattern Recognition Letters, vol. 11, 1990, pp. 197–206.

    Article  MATH  Google Scholar 

  • S.K. Pal and A. Rosenfeld, “Image enhancement and thresholding by optimization of fuzzy compactness”, Pattern Recognition Letters, vol. 7, 1988, pp. 77–86.

    Article  MATH  Google Scholar 

  • J. T. Kent and K.V. Mardia, “Spatial classification using fuzzy membership models”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 10, no. 5, 1988, pp. 659–671.

    Article  MATH  Google Scholar 

  • J.C. Dunn, “A fuzzy relative of the Isodata process and its use in detecting compact well-separated clusters”, Journal Cybernet 31(3), 1974, pp. 32–57.

    MathSciNet  Google Scholar 

  • R. R. Yager and L. A. Zadeh (Eds.), An Introduction to fuzzy logic applications in intelligent systems, Kluwer Academic, Norwell, MA, 1992.

    Google Scholar 

  • J. C. Bezdek and S. K. Pal (Eds.), Fuzzy Models for Pattern Recognition, IEEE Press, New York, 1992.

    Google Scholar 

  • J. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York, 1981.

    Book  MATH  Google Scholar 

  • T. Huntsberger, C. Jacobs, and R. Cannon, “Iterative fuzzy image segmentation”, Pattern Recognition, vol. 18, 1985, pp. 131–138.

    Article  Google Scholar 

  • R. Cannon, J. Dave and J. Bezdek, “Efficient implementation of the fuzzy c-means clustering algorithm,” IEEE Transactions on Pattern Analysis Machine Intelligence, Vol. 8, No. 2, 1986, pp. 248–255.

    Article  MATH  Google Scholar 

  • T. Huntsberger., “Representation of uncertainty in low level vision”, IEEE Transactions on Computers, Vol. 235, No. 2, 145, 1986, p. 145.

    Article  Google Scholar 

  • R. Cannon, J. Dave, J.C. Bezdek, and M. Trivedi, “Segmentation of a thematic mapper image using the fuzzy c-means clustering algorithm,” IEEE Transactions on Geographical Science and Remote Sensing, Vol. 24, No. 3, 1986, pp. 400–408.

    Article  Google Scholar 

  • S. Araki, H. Nomura, and N. Wakami, “Segmentation of Thermal Images Using the Fuzzy C-Means Algorithm”, Proceedings,Second IEEE International Conference on Fuzzy Systems, San Francisco, CA, 1993, pp. 719–724.

    Google Scholar 

  • R. Krishnapuram and J. Keller, “A Possibilistic Approach to Clustering”, IEEE Transactions on Fuzzy Systems, Vol. 1, No. 2, 1993, pp. 98–110.

    Article  Google Scholar 

  • J. Keller and C. Carpenter, “Image Segmentation in the Presence of Uncertainty,” International Journal of Intelligent Systems, vol. 5, 1990, pp. 193–208.

    Article  MATH  Google Scholar 

  • M.M. Trivedi and J. Bezdek, “Low-Level segmentation of aerial images with fuzzy clustering”, IEEE Transactions on Systems, Man,and Cybernetics, vol. SMC-16, No. 4, 1986, pp. 589–598.

    Article  Google Scholar 

  • I. Gath and A.B. Geva, “Unsupervised Optimal Fuzzy Clustering”, IEEE Transactions on Pattern Analysis Machine Intelligence, vol. PAMI-11, no. 7, July 1989, pp. 773–781.

    Article  Google Scholar 

  • R. Krishnapuram and A. Munshi, “Cluster-Based Segmentation of Range Images Using Differential-Geometric Features”, Optical Engineering, Vol. 30, No. 10, October 1991, pp. 1468–1478.

    Article  Google Scholar 

  • J. Jantzen, P. Ring, and P. Christiansen, “Image Segmentation Based on Scaled Fuzzy Membership Functions”, Proceedings,Second IEEE International Conference on Fuzzy Systems, San Francisco, CA, 1993, pp. 714–724.

    Google Scholar 

  • O. Strauss and M. Alden, “Segmentation of Cross Sectional Images Using Fuzzy Logic”, Proceedings,Second IEEE International Conference on Fuzzy Systems, San Francisco, CA, 1993, pp. 731–738.

    Google Scholar 

  • J. Keller and Y. Seo, “Local fractal geometric features for image segmentation”, International Journal of Imaging Systems and Technology, Vol. 2, 1990, pp. 267–284.

    Article  Google Scholar 

  • M.P. Windham, “Cluster validity for the fuzzy c-means clustering algorithm”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-4, no. 4, 1982, pp. 357–363.

    Google Scholar 

  • E. Backer and A.K. Jain, “A clustering performance measure based on fuzzy set decomposition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-3, no. 1, 1981, pp. 66–75.

    Article  Google Scholar 

  • S. Peleg and A. Rosenfeld, “A mini-max medial axis transformation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-3, 1981, pp. 208–210.

    Google Scholar 

  • C.R. Dyer and A. Rosenfeld, “Thinning operations on grayscale pictures,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-1, 1979, pp. 88–89.

    Google Scholar 

  • S.K. Pal “Fuzzy sketetonization of an image”, Pattern Recognition Letters, vol. 10, 1989, pp. 17–23.

    Article  MATH  Google Scholar 

  • S. K. Pal and L. Wang, “Fuzzy Medial Axis Transformation (FMAT): Redundancy, Approximation and Computational Aspects”, Proceedings of the International Fuzzy Systems Association Congress, Brussels, 1991, volume on Engineering, pp. 167–170.

    Google Scholar 

  • S.K. Pal, R.A. King, and A.A. Hashim, “Image description and primitive extraction using fuzzy sets”, IEEE Transaction on Systems, Man, and Cybernetics, vol. SMC-13, No. 1, 1983, pp. 94–100.

    Google Scholar 

  • J. Bezdek and I.M. Anderson, “An Application of the c-varieties clustering algorithms to polygonal curve fitting”, IEEE Transactions on Systems, Man,and Cybernetics, vol. SMC-15, No. 5, 1985, pp. 637–641.

    Article  Google Scholar 

  • R. Dave, “Use of the adaptive fuzzy clustering algorithm to detect lines in digital images”, Proceedings of the Intelligent Robots and Computer Vision VIII, vol. 1192, no. 2, 1989, pp. 600–611.

    Google Scholar 

  • J. Bezdek, C. Cordy, R. Gunderson and J. Watson, “Detection and characterization of cluster substructure”, SIAM Journal Applied Mathematics, Vol. 40, 1981, pp. 339–372.

    Article  MATH  Google Scholar 

  • M. Windham, “Geometrical fuzzy clustering algorithms”, Fuzzy Sets and Systems, vol. 10, 1983, pp. 271–279.

    Article  MathSciNet  MATH  Google Scholar 

  • R. Krishnapuram and C.-P. Freg, “Algorithms to detect linear and planar clusters and their applications”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Hawaii, June 1991, pp. 426–431.

    Google Scholar 

  • R. Krishnapuram and C.-P. Freg, “Fitting an unknown number of lines and planes to image data through compatible cluster merging”, Pattern Recognition, Vol. 25, No. 4, April 1992, pp. 385–400.

    Article  Google Scholar 

  • R. Dave, “Fuzzy Shell-Clustering and applications to circle detection in digital images”, International Journal of General Systems, vol. 16, No. 4, 1990, pp. 343–355.

    Article  MathSciNet  Google Scholar 

  • R. Krishnapuram, H. Frigui and O. Nasraoui, “New Fuzzy Shell Clustering Algorithms for Boundary Detection and Pattern Recognition”, Proc. of the SPIE Conf. on Intelligent Robots and Computer Vision, Vol. 1607, Boston, Nov. 1991, pp. 458–465.

    Google Scholar 

  • R. Krishnapuram, O. Nasraoui, and H. Frigui, “Fuzzy C Spherical Shells Algorithm: A New Approach”, IEEE Transactions on Neural Networks, Vol. 3, No. 5, September 1992, pp. 663–671.

    Article  Google Scholar 

  • R. Krishnapuram, O. Nasraoui, and H. Frigui, “The Surface Density Criterion and Its Application to Linear/Circular Boundary Detection and Planar/Spherical Surface Approximation”, Proceedings, Second IEEE International Conference on Fuzzy Systems, San Francisco, CA, 1993, pp. 725–730.

    Google Scholar 

  • J. Keller and R. Krishnapuram, “Possibilistic Clustering for Shape Description”, Plenary Paper, Proceedings, Third International Workshop on Neural Networks and Fuzzy Logic, NASA/JSC, Houston, TX, 1992, pp. 227–236.

    Google Scholar 

  • R. Krishnapuram and S. Gupta, “Morphological Methods for Detection and Classification of Edges in Range Images”, to appear in the Journal of Mathematical Imaging and Vision, 1993.

    Google Scholar 

  • R. N. Dave, “Adaptive C-shells clustering”, Proceedings of the North American Fuzzy Information Processing Society Workshop, Columbia, Missouri, 1991, pp. 195–199.

    Google Scholar 

  • J. Han, L. Koczy, and T. Poston, “Fuzzy Hough Transform”, Proceedings, Second IEEE International Conference on Fuzzy Systems, San Francisco, CA, 1993, pp. 803–808.

    Google Scholar 

  • C. Tao, W. Thompson, and J. Taur, “A Fuzzy If-Then Approach to Edge Detection”, Proceedings,Second IEEE International Conference on Fuzzy Systems, San Francisco, CA, 1993, pp. 1356–1360.

    Google Scholar 

  • R. Krishnapuram and J. Lee, “Fuzzy-Connective-Based Hierarchical Aggregation Networks for Decision Making”, Fuzzy Sets and Systems, 46, 1992, pp. 1–17.

    Article  MathSciNet  Google Scholar 

  • H.J. Zimmermann and P. Zysno “Decisions and evaluations by hierarchical aggregation of information”, Fuzzy Sets and Systems, vol. 10, no. 3, 1983 pp. 243–260.

    Article  MATH  Google Scholar 

  • R. Krishnapuram and J. Lee, “Fuzzy-Compensative-Connective-Based Hierarchical Networks and Their Application to Computer Vision”, Neural Networks, 5, 1992, pp. 335–350.

    Article  Google Scholar 

  • R. Krishnapuram and J. Lee, “Fuzzy-Connective-Based Hierarchical Aggregation Networks for Decision Making”, Fuzzy Sets and Systems, 46, 1992, pp. 1–17.

    Article  MathSciNet  Google Scholar 

  • J. Keller, R. Krishnapuram, Z. Chen, and O. Nasraoui, “Fuzzy Additive Hybrid Operators for Network-Based Decision Making”, Intl J of Intelligent Systems, 9, 1994, pp 1001–1023.

    Article  Google Scholar 

  • J. Keller, Y. Hayashi, and Z. Chen, “Interpretation of Nodes in Neural Networks for Fuzzy Logic”, Proceedings,Second IEEE International Conference on Fuzzy Systems, San Francisco, CA, 1993, pp. 334–338.

    Google Scholar 

  • J. Keller and Z. Chen, “Learning in Fuzzy Neural Networks Utilizing Additive Hybrid Operators”, Proceedings,Second International Conference on Fuzzy Logic and Neural Networks, Iizuka, Japan, 1992 pp. 85–87.

    Google Scholar 

  • J. Keller and R. Krishnapuram, Second Quarter Report: Fuzzy Set Methods For Object Recognition in Space Applications, NASA/JSC through a subcontract NO. 088 Under Cooperative Agreement No. NCC9–16 (Project No. SE. 42), University of Houston-Clear Lake, 1991.

    Google Scholar 

  • H. Qiu and J. Keller, “Multispectral segmentation using fuzzy techniques,” Proceedings of the NAFIPS Workshop, Purdue University, May 1987, pp. 374–387.

    Google Scholar 

  • H. Tahani and J. Keller, “Information fusion in computer vision using the fuzzy integral”, IEEE Transactions on System, Man and Cybernetics, vol. 20, no. 3, 1990, pp. 733741. Reprinted in G. Klir and Z. Wang, Fuzzy Measure Theory,Plenum Press, 1992.

    Google Scholar 

  • J. Keller and B. Yan, “Possibility Expectation and Its Decision Making Algorithm”, Proceedings of the First IEEE Conference on Fuzzy Systems, San Diego, CA, March, 1992, pp. 661–668.

    Google Scholar 

  • J. Keller and H. Tahani, “The Fusion of Information Via Fuzzy Integration”, Proceedings,NAFIPS’92, Puerto Vallarta, Mexico, December, 1992, pp 468–477.

    Google Scholar 

  • M. Sugeno, “Fuzzy measures and fuzzy integrals: A survey”, in Fuzzy Automatic and Decision Processes, North Holland, Amsterdam, 1977, pp. 89–102.

    Google Scholar 

  • J. Keller, and J. Osborn, “Training the Fuzzy Integral”, International Journal of Approximate Reasoning, Vol. 15, No. 1, 1996, pp. 1–24.

    Article  MATH  Google Scholar 

  • J. Keller, G. Hobson, J. Wootton, A. Nafarieh, and K. Luetkemeyer, “Fuzzy confidence measures in midlevel vision,” IEEE Transactions on System, Man and Cybernetics, Vol. SMC-17, No. 4, 1987, pp. 676–683.

    Article  Google Scholar 

  • J. Wootton, J. Keller, C. Carpenter, and G. Hobson, “A Multiple Hypothesis Rule Based Automatic Target Recognizer”, in Pattern Recognition, Lecture Notes in Computer Science, Vol. 301, J. Kittler (ed.), Springer-Verlag, 1988, pp. 315–324.

    Google Scholar 

  • J. Keller and D. Jeffreys, “Linguistic computations in computer vision”, Proceedings of the NAFIPS Workshop, Vol. 2, Toronto, 1990, pp. 432–435.

    Google Scholar 

  • R. Krishnapuram and F. Rhee, “Compact Fuzzy Rule Base Generation Methods for Computer Vision”, Proceedings,Second IEEE International Conference on Fuzzy Systems, San Francisco, CA, 1993, pp. 809–814.

    Google Scholar 

  • W. Dong, H. Shaw and F. Wang, “Fuzzy computations in risk and decision analysis”, Civil Engineering Systems, vol. 2, 1985, pp. 201–208.

    Article  Google Scholar 

  • J. Keller and D. Hunt, D., “Incorporating Fuzzy Membership Functions into the Perceptron Algorithm,” IEEE Transactions on Pattern Analysis Machine Intelligence, Vol. PAMI7, No. 6, November, 1985, pp. 693–699,(Reprinted in Fuzzy Models for Pattern Recognition, J. C. Bezdek, and S. K. Pal (eds), IEEE Press, Piscataway, N.J., 1992).

    Google Scholar 

  • J. Keller, M. Gray, and J. Givens, “A fuzzy k-nearest neighbor algorithm,” IEEE Transactions on System, Man, and Cybernetics, vol. 15, 1985, pp. 580–585.

    Article  Google Scholar 

  • J. Keller, D. Subhanghasen, K. Unklesbay, and N. Unklesbay, “An approximate reasoning technique for recognition in color images of beef steaks”, International Journal General Systems, Vol. 16, 1990, pp. 331–342.

    Article  Google Scholar 

  • P. Gader, and J. Keller, “Fuzzy Logic in Handwritten Word Recognition”, Proceedings,Third IEEE International Congress on Fuzzy Systems, Orlando, FL, June, 1994 (invited paper), pp. 910–917.

    Google Scholar 

  • P. D. Gader, Mohamed M., and Chiang, J-H., “Fuzzy and Crisp Handwritten Alphabetic Character Recognition Using Neural Networks”, Proceedings of Artificial Neural Networks in Engineering, St. Louis MO, November 1992.

    Google Scholar 

  • P. Gader, J. Keller, and J. Cai, “A Fuzzy Logic System for the Detection and Recognition of Street Number Fields on Handwritten Postal Addresses,” IEEE Transactions on Fuzzy Systems, vol. 3, no. 1, 1995, pp. 83–95.

    Article  Google Scholar 

  • J. Keller, P. Gader, O. Sjahputera, C. W. Caldwell, and H.-M. Huang, “A Fuzzy Logic Rule-Based System for Chromosome Recognition,” Proceedings,Eigth IEEE Symposium on Computer-Based Medical Systems, Lubbock, TX, pp. 125–132 (invited paper), June 9–11, 1995.

    Google Scholar 

  • M. Man and C. Poon, “A Fuzzy-Attributed Graph Approach to Handwritten Character Recognition”, Proceedings,Second IEEE International Conference on Fuzzy Systems, San Francisco, CA, 1993, pp. 570–575.

    Google Scholar 

  • P. Gader, M. Mohamed, and J. Keller, “Dynamic-Programming-Based Handwritten Word Recognition Using the Choquet Integral as the Match Function”, Journal of Electronic Imaging, Special Issue on Document Image Analysis, Vol. 5, No. 1, 1996, pp. 15–24.

    Google Scholar 

  • H. Ushida, T. Takagi, and T. Yamaguchi, “Recognition of Facial Expressions Using Conceptual Fuzzy Sets”, Proceedings,Second IEEE International Conference on Fuzzy Systems, San Francisco, CA, 1993, pp. 594–599.

    Google Scholar 

  • H. Takagi, “Fusion Technology of Fuzzy Theory and Neural Networks - Survey and Future Directions”, Proc. International Conf. on Fuzzy Logic and Neural Networks, Iizuka Japan, July 1990, pp. 13–26.

    Google Scholar 

  • A. Nafarieh and J. Keller, “A Fuzzy Logic Rule-Based Automatic Target Recognizer”, International Journal of Intelligent Systems, Vol. 6, 1990, pp. 295–312.

    Article  Google Scholar 

  • C. Perneel, M. deMathelin, and M. Acheroy, “Automatic Target Recognition Fuzzy System for Thermal Infrared Images”, Proceedings,Second IEEE International Conference on Fuzzy Systems, San Francisco, CA, 1993, pp. 576–581.

    Google Scholar 

  • A. Mogre, R. McLaren, J. Keller, and R. Krishnapuram,“Uncertainty Management in Rule Based Systems With Applications to Image Analysis”, IEEE Transactions, Systems, Man, and Cybernetics, Vol. 24, No. 3, 1994, pp. 470–481.

    Article  Google Scholar 

  • L. Zadeh, “The concept of a linguistic variable and its application to approximate reasoning”, Information Sciences, Part 1, Vol. 8, pp. 199–249; Part 2, Vol. 8, pp. 301357; Part 3, Vol. 9, pp. 43–80, 1975.

    Google Scholar 

  • I. B. Türken and Z. Zhong, “An approximate analogical reasoning approach based on similarity measures”, IEEE Transactions on Systems, Man and Cybernetics, vol. 18, 1988, pp. 1044–1056.

    Google Scholar 

  • A. Nafarieh and J. Keller, “A new approach to inference in approximate reasoning”, Fuzzy Sets and Systems, vol. 41, 1991, pp. 17–37.

    Article  MathSciNet  MATH  Google Scholar 

  • J. Keller and H. Tahani, “Backpropagation neural networks for fuzzy logic”, Information Sciences, Vol. 62, No. 3, 1992, pp. 205–221.

    Article  Google Scholar 

  • J. Keller and R. Yager, “Fuzzy logic inference neural networks”, Proceedings of the SPIE Symposium on Intelligent Robots and Computer Vision VIII, 1989, pp. 582–591.

    Google Scholar 

  • J. Keller and H. Tahani, “Implementation of conjunctive and disjunctive fuzzy logic rules with neural networks”, International Journal of Approximate Reasoning, Vol. 6, No. 2, 1992, pp. 221–240. Reprinted in Fuzzy Models for Pattern Recognition, J. C. Bezdek and S. K. Pal (Eds.), IEEE Press, New York, 1992.

    Google Scholar 

  • W. Zhuang and M. Sugeno, “A Fuzzy Approach to Scene Understanding”, Proceedings,Second IEEE International Conference on Fuzzy Systems, San Francisco, CA, 1993, pp. 564–569.

    Google Scholar 

  • E. Walker, “Fuzzy Relations for Feature-Model Correspondence in 3D Object Recognition”, Proceedings,NAFIPS’96, Berkeley, CA, 1996, pp. 28–32.

    Google Scholar 

  • H-B. Kang and E. Walker, “Characterizing and Controlling Approximation in Hierarchical Perceptual Grouping”, Fuzzy Sets and Systems, Vol. 65, 1994, pp. 187–223.

    Article  Google Scholar 

  • J. Keller, “Computational Intelligence in High Level Computer Vision: Determining Spatial Relationships”, Computational Intelligence: Imitating Life, J. Zurada, R. Marks II, C. Robinson (eds.), IEEE Press, 1994, pp. 81–91. Presented at the IEEE World Congress on Computational Intelligence, Orlando, FL, June, 1994 (invited paper).

    Google Scholar 

  • J. Keller and L. Sztandera, “Spatial relations among fuzzy subsets of an image”, Proceedings of the First International Symposium on Uncertainty Modeling and Analysis, College Park, MD, 1990, pp. 207–211.

    Google Scholar 

  • R. Krishnapuram, J. Keller, and Y. Ma, “Quantitative analysis of properties and spatial relations of fuzzy image regions”, Proceedings, NAF!PS’92,Puerto Vallarta, Mexico, December, 1992, pp. 468–477.

    Google Scholar 

  • R. Krishnapuram, J. Keller, and Y. Ma, “Quantitative Analysis of Properties and Spatial Relations of Fuzzy Image Regions”, IEEE Transactions on Fuzzy Systems,Vol. 1, No. 3, 1993, pp. 222–233.

    Article  Google Scholar 

  • K. Miyajima and A. Ralescu, “Spatial Organization in 2D Segmented Images,” Proceedings,Third IEEE International Conference on Fuzzy Systems, Orlando, FL, pp. 100–105,, 1994.

    Google Scholar 

  • K. Miyajima and A. Ralescu, “Spatial Organization in 2D Segmented Images: Representation and Recognition of Primitive Spatial Relations,” Fuzzy Sets and Systems, vol. 65, pp. 225–236, 1994.

    Article  Google Scholar 

  • J. Keller and X. Wang, “Comparison of Spatial Relation Definitions in Computer Vision”, Proceedings,ISUMA/NAFIPS’95, College Park, MD, September, 1995, pp. 679–684.

    Google Scholar 

  • S. Dutta, “Approximate spatial reasoning: Integrating qualitative and quantitative constraints”, International Journal of Approximate Reasoning“, Vol. 5, 1991, pp. 307–331.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Engineering and Computer Science, University of Missouri-Columbia, Columbia, Missouri, 65211, USA

    James M. Keller

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  1. James M. Keller
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Editors and Affiliations

  1. Faculty of Engineering, Bogaziçi University, Istanbul, TR-80815, Turkey

    Okyay Kaynak

  2. Computer Science Division, University of California at Berkeley, Berkeley, CA, 94720-1776, USA

    Lotfi A. Zadeh

  3. University of Toronto, Ontario, M5S 3G8, Canada

    Burhan Türkşen

  4. Bánki Donát Polytechnic, Népszinház u. 8, Budapest, H-1081, Hungary

    Imre J. Rudas

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© 1998 Springer-Verlag Berlin Heidelberg

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Keller, J.M. (1998). Fuzzy Sets and the Management of Uncertainty in Computer Vision. In: Kaynak, O., Zadeh, L.A., Türkşen, B., Rudas, I.J. (eds) Computational Intelligence: Soft Computing and Fuzzy-Neuro Integration with Applications. NATO ASI Series, vol 162. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-58930-0_21

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  • DOI: https://doi.org/10.1007/978-3-642-58930-0_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-63796-4

  • Online ISBN: 978-3-642-58930-0

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