Occlusion detection and stereo matching in a stochastic method

This paper proposes a stochastic approach to estimate the occlusion and disparity fields of stereoscopic images. The fields are estimated by the Bayesian maximum a posteriori (MAP) framework and Markov random field (MRF) models. The occlusion field model is based on the stochastic observation that the probability distribution in MAP estimator is relatively unstable and uniform at occluded regions. The occlusion is explicitly modelled as MRF, and is estimated in an energy optimization method called stochastic diffusion. The detected occluded region is compensated for the re-estimated disparity field in the same stochastic diffusion and the dynamic programming approach. Experimental results show good occlusion detection and disparity estimation. These results show the novel stochastic approach is suitable for occlusion detection and disparity estimation.