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Generalized Fuzzy Hough Transform for Detecting Arbitrary Shapes in a Vague and Noisy Image

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Abstract

A generalized Hough transform is an effective method for an arbitrary shape detection in a contour image. However, the conventional generalized Hough transform is not suitable for a noisy and blurred image. This paper describes a generalized fuzzy Hough transform which is derived by fuzzifying the vote process in the Hough transform. The present generalized fuzzy Hough transform enables a detection of an arbitrary shape in a very noisy, blurred, and even distorted image. The effectiveness of the present method has been confirmed by some preliminary experiments for artificially produced images and for actual digital images taken by an ordinary digital camera

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References

  1. Rosenfeld A, Kak AC (1976) Digital picture processing. Academic, London

    Google Scholar 

  2. Ballard DH, Brown CM (1982) Computer vision. Prentice-Hall, New Jersey

    Google Scholar 

  3. Ballard DH (1981) Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognit 13(2):111–122

    Article  MATH  Google Scholar 

  4. Lamdan Y, Wolfson HJ (1988) Geometric hashing: a general and efficient model-based recognition scheme. In: Proceedings of ICCV, pp 238–249

  5. Dufresne TE, Dhawan AP (1995) Chord–tangent transformation for object recognition. Pattern Recognit 29(9):1321–1331

    Article  Google Scholar 

  6. Kimura A, Watanabe T (1998) Fast generalized Hough transform: rotation, scale and translation invariant detection of arbitrary shapes (in Japanese). Trans IEICE J81-D-II(4):726–734

  7. Chatzis V, Pitas I (1997) Fuzzy cell Hough transform for curve detection. Pattern Recognit 30(12):2031–2042

    Article  Google Scholar 

  8. Han JH, Koczy LT, Poston T (1994) Fuzzy Hough transform. Pattern Recognit Lett 15:649–658

    Article  Google Scholar 

  9. Bhandarkar SM (1994) A fuzzy probabilistic model for the generalized Hough transform. IEEE Trans Syst Man Cybern 24(5):745–759

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Physics, Biology and Informatics, Yamaguchi University, 1677-1 Yoshida, Yamaguchi, 753-8512, JAPAN

    Noriaki Suetake, Eiji Uchino & Kanae Hirata

Authors
  1. Noriaki Suetake
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  2. Eiji Uchino
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  3. Kanae Hirata
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Correspondence to Noriaki Suetake.

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Suetake, N., Uchino, E. & Hirata, K. Generalized Fuzzy Hough Transform for Detecting Arbitrary Shapes in a Vague and Noisy Image. Soft Comput 10, 1161–1168 (2006). https://doi.org/10.1007/s00500-005-0038-2

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  • DOI: https://doi.org/10.1007/s00500-005-0038-2

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