  In this paper, we extend the standard belief propagation (BP) sequential
technique proposed in the tree-reweighted sequential method to the fully
connected CRF models with the geodesic distance affinity. The proposed method
has been applied to the stereo matching problem. Also a new approach to the BP
marginal solution is proposed that we call one-view occlusion detection (OVOD).
In contrast to the standard winner takes all (WTA) estimation, the proposed
OVOD solution allows to find occluded regions in the disparity map and
simultaneously improve the matching result. As a result we can perform only one
energy minimization process and avoid the cost calculation for the second view
and the left-right check procedure. We show that the OVOD approach considerably
improves results for cost augmentation and energy minimization techniques in
comparison with the standard one-view affinity space implementation. We apply
our method to the Middlebury data set and reach state-of-the-art especially for
median, average and mean squared error metrics.
