Generalized Fuzzy Hough Transform for Detecting Arbitrary Shapes in a Vague and Noisy Image | Soft Computing Skip to main content
Springer Nature Link
Log in

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

  • Focus
  • Published:
Soft Computing Aims and scope Submit manuscript

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

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

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 

Download references

Author information

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
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eiji Uchino
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kanae Hirata
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Noriaki Suetake.

Rights and permissions

Reprints and permissions

About this article

Cite this article

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

Download citation

  • Published:

  • Issue Date:

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

Keywords

Search

Navigation