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Non-iterative contextual correspondence matching

  • Bill Christmas
  • Josef Kittler
  • Maria Petrou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 801)

Abstract

In this paper, we develop a framework for the non-iterative matching of symbolic structures using contextual information. It is based on Bayesian reasoning and involves the explicit modelling of the binary relations between the objects. The difference between this and previously developed theories of the kind lies in the assumption that the binary relations used are derivable from the unary measurements that refer to individual objects. This leads to a non-iterative formula for probabilistic reasoning which is amenable to real-time implementation and produces good results. The theory is demonstrated using an application of automatic map registration.

Keywords

Binary Relation Road Segment Unary Measurement Stereo Pair Binary Measurement 
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.

References

  1. 1.
    P.J. Besl and R.C. Jain. Three-dimensional object recognition. Computing Surveys, 17:75–145, 1985.Google Scholar
  2. 2.
    B. Bhanu and O.D. Faugeras. Shape matching of two-dimensional objects. IEEE Trans. Pattern Analysis and Machine Intelligence, 6:137–156, 1984.Google Scholar
  3. 3.
    W.J. Christmas., J. Kittler, and M. Petrou. Matching in computer vision using non-iterative contextual correspondence. Submitted to Computer Vision, Graphics and Image Processing.Google Scholar
  4. 4.
    B. Gidas. A renormalization group approach to image processing problems. IEEE Trans. Pattern Analysis and Machine Intelligence, 11:164–180, 1989.Google Scholar
  5. 5.
    R.A. Hummel and S.W. Zucker. On the foundations of relaxation labeling process. IEEE Trans. Pattern Analysis and Machine Intelligence, 5(3):267–286, May 1983.Google Scholar
  6. 6.
    J. Kittler, M. Petrou, and W.J. Christmas. Probabilistic relaxation for matching problems in computer vision. In Proceedings of the Fourth International Conference on Computer Vision, pages 666–673, Berlin, 1993.Google Scholar
  7. 7.
    A. Rosenfeld, R. Hummel, and S. Zucker. Scene labeling by relaxation operations. IEEE Trans. Systems, Man, and Cybernetics, 6:420–433, June 1976.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1994

Authors and Affiliations

  • Bill Christmas
    • 1
  • Josef Kittler
    • 1
  • Maria Petrou
    • 1
  1. 1.Department of Electronic and Electrical EngineeringUniversity of SurreyUK

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