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Relaxing Symmetric Multiple Windows Stereo Using Markov Random Fields

  • Andrea Fusiello
  • Umberto Castellani
  • Vittorio Murino
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2134)

Abstract

This paper introduces R-SMW, a new algorithm for stereo matching. The main aspect is the introduction of a Markov Random Field (MRF) model in the Symmetric Multiple Windows (SMW) stereo algorithm in order to obtain a non-deterministic relaxation. The SMW algorithm is an adaptive, multiple window scheme using left-right consistency to compute disparity. The MRF approach allows to combine in a single functional the disparity values coming from different windows, the left-right consistency constraint and regularization hypotheses. The optimal estimate of the disparity is obtained by minimizing an energy functional with simulated annealing. Results with both synthetic and real stereo pairs demonstrate the improvement over the original SMW algorithm, which was already proven to perform better than state-of-the-art algorithms.

Keywords

Markov Random Field Observation Model Stereo Match Stereo Pair Markov Random 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Andrea Fusiello
    • 1
  • Umberto Castellani
    • 1
  • Vittorio Murino
    • 1
  1. 1.Dipartimento di InformaticaUniversità di VeronaVeronaItaly

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