Genetic algorithms applied to binocular stereovision

  • Régis Vaillant
  • Laurent Gueguen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 801)


This paper describes an original approach to the problem of edge-based binocular stereovision. The tokens to be matched are subchains of the chains of connected pixels. Local constraints of the stereovision problem are first used in associating to each token a set of potential matches. Global constraints are embedded in a cost function and we look for the minimum of this cost function. The optimisation search is conducted using genetic algorithms.


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  1. [BB89]
    Andrew T. Brint and Michael Brady. Stereo Matching of Curves. In International Advanced Robotics Programme, October 1989.Google Scholar
  2. [DA89]
    Umesh D. Dhond and Jake K. Aggarwal. Structure from Stereo-A Review. IEEE Transactions on Systems Man and Cybernetics, 19:1489–1510, November/December 1989.Google Scholar
  3. [Der87]
    Rachid Deriche. Using Canny's Criteria to Derive an Optimal Edge Detector Recursively Implemented. In The International Journal of Computer Vision, volume 2, pages 15–20, April 1987.Google Scholar
  4. [Fau93]
    Olivier D. Faugeras. Three-dimensional Computer Vision: a geometric view-point. MIT Press, 1993.Google Scholar
  5. [Gol89]
    David E. Goldberg. Genetic Algorithms in Search Optimization & Machine Learning. Adison-Wesley, 1989.Google Scholar
  6. [Gri85]
    W.E.L. Grimson. Computational experiments with a feature based stereo algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence, 7, No 1:17–34, 1985.Google Scholar
  7. [HAL88]
    Charles Hansen, Nicolas Ayache, and Francis Lustman. Towards real-time trinocular stereo. In Second International Conference on Computer Vision, December 1988.Google Scholar
  8. [MN85]
    G. Medioni and R. Nevatia. Segment-based stereo matching. Computer Vision Graphics and Image Processing, pages 31:2–18, 1985.Google Scholar
  9. [OK85]
    Y. Ohta and T. Kanade. Stereo by Intra-and Inter-Scanline Search. IEEE Transactions on Pattern Analysis and Machine Intelligence, 7(2):139–154, 1985.Google Scholar
  10. [PMF85]
    S.B. Pollard, J.E.W. Mayhew, and J.P. Frisby. PMF: a Stereo Correspondance Algorithm using a Disparity Gradient Limit. Perception, 14:449–470, 1985.Google Scholar
  11. [RF91]
    Luc Robert and Olivier Faugeras. Curve-Based Stereo: Figurai Continuity And Curvature. In Computer Vision and Pattern Recognition, 1991.Google Scholar
  12. [VG93]
    Régis Vaillant and Laurent Gueguen. Genetic algorithms applied to binocular stereovision. Technical Report ASRF-93-2, Thomson-CSF, L.C.R., September 1993.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1994

Authors and Affiliations

  • Régis Vaillant
    • 1
  • Laurent Gueguen
    • 1
  1. 1.Thomson-CSF LCROrsay CedexFrance

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