A novel Markovian formulation of the correspondence problem in stereo vision
This paper addresses the problem of finding matching points in stereo image pairs, i.e., the problem of correspondence. Even though this topic is well-known, a complete probabilistic formulation of it using psychovisual cues is still missing. We propose a novel Bayesian model based on Markov Random Fields (MRFs); the prior energy function is built in terms of the probability density function (pdf) of the disparity gradient. This pdf has never been reported in the past. The likelihood energy function is defined in terms of the pdf of the square normalized cross covariance between any two matching points. The stereo correspondence map is then obtained as the MAP estimator of the posterior field. Comparative results with methods previously reported, show the adequacy of the general model here proposed, and a good compromise between deterministic and stochastic images is attained.