PT Journal
AU Mikhail Mozerov
   Joost Van de Weijer
TI One-view occlusion detection for stereo matching with a fully connected CRF model
SO IEEE Transactions on Image Processing
JI TIP
PY 2019
BP 2936
EP 2947
VL 28
IS 6
DI 10.1109/TIP.2019.2892668
DE Stereo matching; energy minimization; fully connected MRF model; geodesic distance filter
AB In this paper, we extend the standard belief propagation (BP) sequential technique proposed in the tree-reweighted sequential method [15] 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 onlyone 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-ofthe-art especially for median, average and mean squared error metrics.
ER