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A Markov Chain Monte Carlo Approach to Stereovision

  • Julien Sénégas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2352)

Abstract

We propose Markov chain Monte Carlo sampling methods to address uncertainty estimation in disparity computation. We consider this problem at a postprocessing stage, i.e. once the disparity map has been computed, and suppose that the only information available is the stereoscopic pair. The method, which consists of sampling from the posterior distribution given the stereoscopic pair, allows the prediction of large errors which occur with low probability, and accounts for spatial correlations. The model we use is oriented towards an application to mid-resolution stereo systems, but we give insights on how it can be extended. Moreover, we propose a new sampling algorithm relying on Markov chain theory and the use of importance sampling to speed up the computation. The efficiency of the algorithm is demonstrated, and we illustrate our method with the computation of confidence intervals and probability maps of large errors, which may be applied to optimize a trajectory in a three dimensional environment.

Keywords

stereoscopic vision digital terrain models disparity uncertainty estimation sampling algorithms Bayesian computation inverse problems 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Julien Sénégas
    • 1
    • 2
  1. 1.Centre de GéostatistiqueEcole des Mines de ParisFontainebleau CedexFrance
  2. 2.IstarSophia-Antipolis CedexFrance

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