Advertisement

Is vision a pattern recognition problem?

  • R Wilson
Vision Versus Pattern Recognition
Part of the Lecture Notes in Computer Science book series (LNCS, volume 301)

Abstract

It is argued that traditional pattern recognition methods are inadequate for the tasks confronting computer vision. In an effort to overcome their limitations, a new approach has been developed, in which the concept of pattern is replaced by the group-theoretical notions of representations and invariants. By applying these ideas to the symbolic representation of images, it is possible to derive some very general constraints on the effectiveness of symbolic descriptions from the structure of the image vector space and the transformations which act upon it. The theory is illustrated with some simple examples and then applied to a number of practical problems, including feature description, texture analysis and segmentation. The paper is concluded with a discussion of some generalisations and extensions.

Keywords

Gray Level Uncertainty Principle Symbolic Representation Intermediate Representation Pattern Recognition System 
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. 1.
    Duda, R. O., Hart, P. E., Pattern Classification and Scene Analysis, New York, Wiley, 1973.Google Scholar
  2. 2.
    Marr, D., Vision, San Francisco, Freeman, 1982.Google Scholar
  3. 3.
    Barrow, H. G., Tenenbaum, J. M., Computational Vision, Proc. IEEE, 69, 5, 572–595, 1981.Google Scholar
  4. 4.
    Rumelhart, D. E., McClelland, J. L., Parallel Distributed Processing Vol. 1, Cambridge, M.I.T., 1986.Google Scholar
  5. 5.
    —, PDP Vol. 2, Cambridge, M.I.T., 1986.Google Scholar
  6. 6.
    Granlund, G. H., In Search of a General Picture Processing Operator, Comp. Graphics and Image Proc., 8, 155–173, 1978.Google Scholar
  7. 7.
    Knutsson, H., Filtering and Reconstruction in Image Processing, Linköping Univ., Ph.D. Thesis, 1982.Google Scholar
  8. 8.
    Wilson, R., Knutsson, H., Granlund, G. H., The Operational Definition of the Position of Line and Edge, Proc. 6th Int'l Conf. on Patt. Rec., 846–849, Munich, 1982.Google Scholar
  9. 9.
    Wilson, R., Granlund, G. H., The Uncertainty Principle in Image Processing, IEEE Trans. on Patt. Anal. and Machine Intell., PAMI-6, 758–767, 1984.Google Scholar
  10. 10.
    Spann, M., Wilson, R., A Quad-tree Approach to Image Segmentation that Combines Statistical and Spatial Information, Patt. Recog., 18, 257–269, 1985.CrossRefGoogle Scholar
  11. 11.
    Wilson, R., Spann, M., The Finite Prolate Spheroidal Sequences and their Applications pt I, II. (Accepted for publication) IEEE Trans. on Patt. Anal. and Machine Intell., 1987.Google Scholar
  12. 12.
    Wilson, R., Spann, M., Image Segmentation and Uncertainty, Chichester, Research Studies Pr., 1988.Google Scholar
  13. 13.
    Wilson, R., Knutsson, H., Uncertainty and Inference in the Visual System, (Accepted for publication) IEEE Trans. on Sys. Man and Cybern., 1987.Google Scholar
  14. 14.
    Weyl, H., The Theory of Groups and Quantum Mechanics, New York, Dover, 1950.Google Scholar
  15. 15.
    Frei, W., Chen, C. C., Fast Boundary Detection: A Generalisation and a New Algorithm, IEEE Trans. Comp., C-26, 988–998, 1977.Google Scholar
  16. 16.
    Gabor, D., Theory of Communication, Proc. IEE, 93, 26, 429–441, 1946.Google Scholar
  17. 17.
    Canny, J., A Computational Approach to Edge Detection, IEEE Trans. Patt. Anal. and Machine Intell., PAMI-8, 679–698, 1986.Google Scholar
  18. 18.
    Nering, E. D., Linear Algebra and Matrix Theory (2nd Ed.), New York, Wiley, 1970.Google Scholar
  19. 19.
    Wilson, R., Knutsson, H., Uncertainty and Inference in the Visual System II: Motion and Stereopsis without Matching, To be published.Google Scholar
  20. 20.
    Adelson, E. H., Bergen, J. R., Spatiotemporal Energy Models for the Perception of Motion, J. Opt. Soc. Am. A, 2, 284–299, 1985.PubMedGoogle Scholar
  21. 21.
    Watson, A. B., Ahumada, A. J., Model of Human Visual-Motion Sensing, J. Opt. Soc. Am. A, 2, 322–342, 1985.PubMedGoogle Scholar
  22. 22.
    Knutsson, H., A Tensor Representation of 3-d Structure, IEEE ASSP Soc. Workshop on Multidimensional S.P., Noordwijkerhout, Holland, 1987.Google Scholar
  23. 23.
    Granlund, G. H., Knutsson, H., Contrast of Structural and Homogeneous Representations, in Physical and Biological Processing of Images, ed. O. Braddick, Berlin, Springer-Verlag, 282–303, 1983.Google Scholar
  24. 24.
    Jauch, J. M., Foundations of Quantum Mechanics, Reading, Addison-Wesley, 1968.Google Scholar
  25. 25.
    Calway, A. D., Hierarchical Descriptors for Nonstationary 1 and 2-d Signal Processing, Warwick Univ. Comp. Sci. Rept. no. RR108, 1987.Google Scholar
  26. 26.
    Clippingdale, S., Wilson, R., Quad-tree Image Estimation: a New Image Model and its Application to Minimum Mean Square Error Image Restoration, Proc. 5th Scand. Conf. on Image Anal., 699–706, Stockholm, 1987.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1988

Authors and Affiliations

  • R Wilson
    • 1
  1. 1.University of WarwickCoventryEngland

Personalised recommendations