A Markov Random Field Model and Method to Image Matching

Mohammed Ouali1, Holger Lange2, and Kheireddine Bouazza1
1Département d’Informatique, Faculté des Sciences, Université d’Oran, Algérie
2R and D Group, General Dynamics Canada, Canada

 

Abstract: In this paper, the correspondence problem is solved by minimizing an energy functional using a stochastic approach. Our procedure generally follows Geman and Geman’s Gibbs sampler for Markov Random Fields (MRF). We propose a transition generator to generate and explore states. The generator allows constraints such as epipolar, uniqueness, and order to be imposed. We also propose to embed occlusions in the model. The energy functional is designed to take into account resemblance, continuity, and number of occlusions.  The disparity and occlusion maps as modeled by their energy functional, i.e., as a Gibbs-Boltzmann distribution, are viewed as a MRF where the matching solution is an optimal state.

  

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