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Author 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 (down) 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.		  
  Address  
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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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Author Mikhail Mozerov; Joost Van de Weijer edit   pdf
doi  openurl
  Title Improved Recursive Geodesic Distance Computation for Edge Preserving Filter Type Journal Article
  Year (down) 2017 Publication IEEE Transactions on Image Processing Abbreviated Journal TIP  
  Volume 26 Issue 8 Pages 3696 - 3706  
  Keywords Geodesic distance filter; color image filtering; image enhancement  
  Abstract All known recursive filters based on the geodesic distance affinity are realized by two 1D recursions applied in two orthogonal directions of the image plane. The 2D extension of the filter is not valid and has theoretically drawbacks, which lead to known artifacts. In this paper, a maximum influence propagation method is proposed to approximate the 2D extension for the
geodesic distance-based recursive filter. The method allows to partially overcome the drawbacks of the 1D recursion approach. We show that our improved recursion better approximates the true geodesic distance filter, and the application of this improved filter for image denoising outperforms the existing recursive implementation of the geodesic distance. As an application,
we consider a geodesic distance-based filter for image denoising.
Experimental evaluation of our denoising method demonstrates comparable and for several test images better results, than stateof-the-art approaches, while our algorithm is considerably fasterwith computational complexity O(8P).		  
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  Corporate Author Thesis  
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  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes LAMP; ISE; 600.120; 600.098; 600.119 Approved no  
  Call Number Admin @ si @ Moz2017 Serial 2921  
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