Advertisement

Application of the projected hough transform in picture processing

  • Ulrich Eckhardt
  • Gerd Maderlechner
Shone Analysis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 301)

Abstract

The main result of this paper is that a projection of the classical Hough transform for line detection onto a subspace of the parameter space (accumulator) will yield a useless trivial result if the composite operator consisting of projection and Hough transform is assumed to be linear and translation invariant.

Keywords

Binary Image Composite Operator Picture Processing Nonlinear Projector Parallel Computer Architecture 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Adams RA (1975) Sobolev Spaces. New York, San Francisco, London: Academic PressGoogle Scholar
  2. Ballard DH (1981) Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognition 13:111–122CrossRefGoogle Scholar
  3. Ballard DH, Sabbah D (1983) Viewer independent shape recognition. IEEE Trans. PAMI-5: 653–660Google Scholar
  4. Biland HP, Wahl FM (1986) Understanding Hough space for polyhedral scene decomposition. IBM Zürich Research Laboratory, RZ 1458 (# 52978) 3/25/86Google Scholar
  5. Brown CM, Sher DB (1982) Hough transformation into cache accumulators: Considerations and simulations. TR 114, Department of Computer Science, University of RochesterGoogle Scholar
  6. Deans SR (1983) The Radon Transform and Some of Its Applications. New York, Chichester, Brisbane, Toronto, Singapore: John Wiley and SonsGoogle Scholar
  7. Duda RO, Hart PE (1972) Use of the Hough transform to detect lines and curves in pictures. Communications of the ACM 15: 11–15CrossRefGoogle Scholar
  8. Eckhardt U, Maderlechner G (1987) Projections of the Hough transform. Manuscript Universität HamburgGoogle Scholar
  9. Gerig G, Klein F (1986) Fast contour identification through efficient Hough transform and simplified interpretation strategy. IAPR — afcet: Eighth International Conference on Pattern Recognition. Paris, France, October 27–31, 1986Google Scholar
  10. Gerig G (1987) Segmentierung zur symbolischen Beschreibung von Strukturen in Grauwertbildern. Zürich: Dissertation ETH Nr. 8390Google Scholar
  11. Hough PVC (1962) Method and means for recognizing complex patterns. U.S. Patent 3,069,654. Washington: United States Patent Office, December 18, 1962Google Scholar
  12. Kushnir M, Abe K, Matsumoto K (1985) Recognition of handprinted Hebrew characters using features selected in the Hough transform space. Pattern Recognition 18: 103–114CrossRefGoogle Scholar
  13. Merlin PM, Farber DJ (1975) A parallel mechanism for detecting curves in pictures. IEEE Trans. C-24: 96–98Google Scholar
  14. Neveu CF, Dyer CR, Chin RT (1986) Two-dimensional object recognition using multi-resolution models. Computer Vision, Graphics, and Image Processing 34:52–65Google Scholar
  15. O'Rourke J (1981) Dynamically quantized spaces for focusing the Hough transform. In: Proceedings of the Seventh International Joint Conference on Artificial Intelligence, 24–28 August 1981, University of British Columbia, Vancouver, B.C., Canada, pp. 737–739Google Scholar
  16. Radon J (1917) Über die Bestimmung von Funktionen durch ihre Integralwerte längs gewisser Mannigfaltigkeiten. Ber. Verh. Sächs. Akad. Wiss. Leipzig, Math.-Nat. Kl. 69: 262–277Google Scholar
  17. Silberberg TM, Davis L, Harwood D (1984) An iterative Hough procedure for three-dimensional object recognition. Pattern Recognition 17: 621–629CrossRefGoogle Scholar
  18. Sloan KR (1981) Dynamically quantized pyramids. In: Proceedings of the Seventh International Joint Conference on Artificial Intelligence, 24–28 August 1981, University of British Columbia, Vancouver, B.C., Canada, pp. 734–736Google Scholar
  19. Wallace RS (1985) A modified Hough transform for lines. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June 19–23, 1985, San Francisco, California, pp. 665–667. Silver Spring: IEEE Computer Society Press. Amsterdam: North-Holland Publishing CompanyGoogle Scholar
  20. Yalamanchili S, Aggarwal JK (1985) A system organization for parallel image processing. Pattern Recognition 18: 17–29.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1988

Authors and Affiliations

  • Ulrich Eckhardt
    • 1
  • Gerd Maderlechner
    • 2
  1. 1.Institut für Angewandte MathematikUniversität HamburgHamburg 13
  2. 2.ZT ZTI INF 122, SIEMENS AGMünchen 83

Personalised recommendations