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Fuzzy Interpretation of Image Data

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Soft Computing for Image Processing

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 42))

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Abstract

In image formation, we usually consider two things: the intensity and the location of a pixel. Because of several reasons, there could be uncertainty in the image brightness and also in the location of a pixel. We consider the cases where the observed image data or entities computed from it are inherently fuzzy. Based on this idea, we have considered shape detection methods and representation of edges using fuzzy set theory. In shape detection method, an image point is considered as a fuzzy data. By combining this concept and the Hough transform algorithm, a fuzzy Hough transform algorithm is introduced. More general shape detection paradigm is given by defining the cardinality of a shape. Edges, which is one of the basic entities of an image, is generalized using the fuzzy set theory. An edge evaluation criteria for the edges are also presented.

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References

  • E. Abdou and W. K. Pratt, “Quantitative Design and Evaluation of Enhancement/Thresholding Edge Detectors”, Proc. IEEE, 67(5) pp. 753–763, 1979.

    Article  Google Scholar 

  • Donald A. Berry and Bernard W. Lindgren, Statistics: Theory and Methods, Brooks/Cole Publishing Co., 1990 .

    MATH  Google Scholar 

  • John Canny, “A Computational Approach to Edge Detection”, IEEE Trans, on PAMI, Vol.8, No.6, pp. 679–698, 1986.

    Article  Google Scholar 

  • Kyujin Cho, Peter Meer and Javier Cabrera, “Quantitative Evaluation of Performance through Bootstrapping: Edge Detection”, Proceedings of International Symposium on Computer Vision, pp. 491–496., 1995.

    Google Scholar 

  • William S. Cleveland, “Robust Locally Weighted Regression and Smoothing B Scatterplots”, Journal of the American Statistical Association, V. 74, N. 368, pp. 829–836, 1979.

    Article  MathSciNet  MATH  Google Scholar 

  • R. S. Conker, “A Dual Variation of the Hough Transform for Detecting Nonconcentric Circles of Different Radii”, Computer Vision, Graphics and Image Processing, 43, pp. 115–132, 1988.

    Article  Google Scholar 

  • Edward J. Delp and C. Henry Chu, “Detecting Edge Segments”, IEEE Trans, on SMC, SMC-15(1), pp.144–152, 1985.

    Google Scholar 

  • E. R. Dougherty, Probability and Statistics for the Engineering, Computing and Physical Sciences, Prentice–Hall, 1990.

    MATH  Google Scholar 

  • Joon H. Han, Laszlo T. Koczy, and Timothy Poston, “Fuzzy Hough Transform”, Pattern Recognition Letters, 15, pp. 649–658, 1994.

    Article  Google Scholar 

  • Joon H. Han, and Sung Y. Kim, “Geometrical interpretation of fuzzy data and shape detection “ The 8th International conference on fuzzy systems (FUZZ IEEE’99), 1999 (To be presented)

    Google Scholar 

  • Robert M. Haralick and James S. J. Lee, “Context Dependent Edge Detection and Evaluation”, Pattern Recognition, 23(1/2), pp. 1–19, 1990.

    Article  Google Scholar 

  • Trevor Hastie and Werner Stuetzle, “Principal Curves”, Journal of the American Statistical Association, V. 84, N. 46, pp. 502–516, 1989.

    Article  MathSciNet  MATH  Google Scholar 

  • Michael D. Heath, Sudeep Sarkar, Thomas Sanocki and Kevin Bowyer, “Comparison of Edge Detectors: A Methodology and Initial Study”, Proceedings of CVPR, pp. 143–148, 1996.

    Google Scholar 

  • T. Huntsberger, C. Rangarajan, and S. Jayaramamurthy, “Representation of Uncertainty in computer Vision Using Fuzzy Sets”, IEEE Trans. Comp. vol C–53, no. 2, pp. 145–156, 1986.

    Google Scholar 

  • P. V. C. Hough, “Method and means for recognizing complex patterns”, U. S. Patent 3,069,654, 1962.

    Google Scholar 

  • J. Illingworth and J. Kittler, “A Survey of the Hough Transform”, Computer Vision, Graphics and Image Processing, 44, pp. 87–116, 1988.

    Article  Google Scholar 

  • A. Kaufmann and M. M. Gupta, Introduction to Fuzzy Arithmetic: Theory and Application, Van Nostrand Reinhold, New York, 1985.

    Google Scholar 

  • O. Kaleva and S. Seikkala , “On Fuzzy Metric Spaces”, Fuzzy Sets and Systems, 12, pp. 215–229, 1984.

    Article  MathSciNet  MATH  Google Scholar 

  • Wilfred Kaplan, Advanced Calculus, Addison–Wesley Publishing Co., 1984.

    MATH  Google Scholar 

  • Tae Y. Kim and Joon H. Han, “A Fuzzy Approach to Edge Detection and Representation”, Proceedings of IEEE International Conference on Fuzzy Systems, pp. 69–74, 1997.

    Google Scholar 

  • Tae Y. Kim and Joon H. Han, “Selection of Significant Parameters in Edge Detection Using Ambiguity Distance”, Proc. 1998 IEEE World Congress on Computational Intelligence, FUZZ–iEEE ’98, Anchorage, Alaska, May 4–7, pp. 1536–1541, 1998.

    Google Scholar 

  • Les Kitchen and Azriel Rosenfeld, “Edge Evaluation Using Local Edge Coherence”, IEEE Trans, on SMC, SMC-11(9), pp. 597–605, 1981.

    Google Scholar 

  • George J. Klir and Tina A. Folger, Fuzzy Sets, Uncertainty, And Information, Prentice Hall, 1988.

    MATH  Google Scholar 

  • George J. Klir, and Bo Yuan, Fuzzy Sets and Fuzzy Logic: Theory and applications, Prentice Hall PTR, 1995

    MATH  Google Scholar 

  • Michael Lindenbaum, “Bounds on Shape Recognition Performance”, IEEE Trans, on PAMI, 17(7), pp. 666–680, 1995.

    Article  Google Scholar 

  • Zbigniew Michalewicz, Genetic Algorithms -f Data Structures = Evolution Programs, 3rd Ed. Springer, 1996.

    Google Scholar 

  • Sankar, K. Pal, and Robert A. King, “Image Enhancement Using Smoothing with Fuzzy Sets”, IEEE Trans. Sys. Man, and Cybern., vol. SMC-11, no. 7, pp. 494–501, 1981.

    Google Scholar 

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

    Article  MATH  Google Scholar 

  • P. L. Palmer, H. Dabis and J. Kittler, “A Performance Measure for Boundary Detection Algorithms”, Computer Vision and Image Understanding, 63(3), pp. 476–494, 1996.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • J. Sklansky, “On the Hough Technique for Curve Detection”, IEEE Trans. Computers, c–27 (10), pp. 923–926, 1978.

    Article  Google Scholar 

  • Paul L. Rosin, “Edges: saliency measures and automatic thresholding”, Machine Vision and Applications, 9, pp. 139–159, 1997.

    Article  Google Scholar 

  • Jun Shen, “An Optimal Linear Operator for Step Edge Detection”, CVGIP: Graphical Models–and Image Processing, 54(2) pp. 112–133, 1992.

    Article  Google Scholar 

  • Robin N. Strickland and Dunkai K. Chang, “Adaptable edge quality metric”, Optical Engineering, 32(5), pp. 944–951, 1993.

    Article  Google Scholar 

  • L. A. Zadeh , “Fuzzy Sets”, Information and Control, 8, pp. 338–353, 1965.

    Article  MathSciNet  MATH  Google Scholar 

  • Qiuming Zhu, “Efficient evaluation of edge connectivity and width uniformity”, Image and Vision Computing, 14, pp. 21–34, 1996.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Dept. of Computer Science and Engineering, Pohang University of Science and Technology, Hyo-Ja Dong San31, Pohang, 790-784, Republic of Korea

    Joon H. Han & Tae Y. Kim

  2. Dept. of Telecommunication and Telematics, Technical University of Budapest, Sztoczek u. 2, Budapest, H-1111, Hungary

    László T. Kóczy

Authors
  1. Joon H. Han
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  2. Tae Y. Kim
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  3. László T. Kóczy
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Editor information

Editors and Affiliations

  1. Machine Intelligence Unit, Indian Statistical Institute, 203 B. T. Road, Calcutta, 700035, India

    Sankar K. Pal , Ashish Ghosh  & Malay K. Kundu ,  & 

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© 2000 Springer-Verlag Berlin Heidelberg

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Han, J.H., Kim, T.Y., Kóczy, L.T. (2000). Fuzzy Interpretation of Image Data. In: Pal, S.K., Ghosh, A., Kundu, M.K. (eds) Soft Computing for Image Processing. Studies in Fuzziness and Soft Computing, vol 42. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1858-1_11

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  • DOI: https://doi.org/10.1007/978-3-7908-1858-1_11

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-2468-1

  • Online ISBN: 978-3-7908-1858-1

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