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Author (up) Mikhail Mozerov; Joost Van de Weijer edit   pdf
doi  openurl
  Title One-view occlusion detection for stereo matching with a fully connected CRF model Type Journal Article
  Year 2019 Publication IEEE Transactions on Image Processing Abbreviated Journal TIP  
  Volume 28 Issue 6 Pages 2936-2947  
  Keywords Stereo matching; energy minimization; fully connected MRF model; geodesic distance filter  
  Abstract 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 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-ofthe-art especially for median, average and mean squared error metrics.		  
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  Notes LAMP; 600.098; 600.109; 602.133; 600.120 Approved no  
  Call Number Admin @ si @ MoW2019 Serial 3221  
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