First Int'l Workshop on
     Statistical and Computational Theories of Vision        

-- Modeling, Learning, Computing, and Sampling

Selected papers are published in two special issues of

Int'l Journal of Computer Vision

1st Issue in October 2000 and 2nd Issue in January 2001.

Scope, Program Committee, and contact information

Proc. of IEEE workshop on Statistical and Computational Theories of Vision, Fort Collins, Co, June, 1999

Proceedings of the Workshop

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Session 1. Statistics of Natural Imagery and Modeling:

  • Session 2. Markov random field theory and Low level Vision

  • Meaningful Aligments
    A. Desolneux, L. Moisan and J. M. Morel
  • Markov networks for low level vision
    Bill Freeman and Egon Pasztor
  • Session 3: Learning in Computer Vision:

  • Graded learning for object detection
    Francois Fleuret and Donald Geman
  • Gradient-Based learning for object detection, segmentation and recognition
    Yann LeCun, Patrick Haffner, Leon Bottou, and Yoshua Bengio

    Session 4: Fundamental Bounds in Computer Vision

  • Session 5: Bayes Statistics and Psychophysics.

  • The statistics of visual correspondence: insights into the visual system
    Cornelia Fermuller, Robert Pless and Yiannis Aloimonos
  • Session 6: Object detection by MCMC sampling and Pattern Theory

  • The joy of sampling
    David Forsyth, S. Loffe, and J. Haddon