Search Space Reduction for MRF Stereo
暂无分享,去创建一个
[1] Nanning Zheng,et al. Stereo Matching Using Belief Propagation , 2002, IEEE Trans. Pattern Anal. Mach. Intell..
[2] Vladimir Kolmogorov,et al. An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision , 2001, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[3] Minglun Gong,et al. Fast Unambiguous Stereo Matching Using Reliability-Based Dynamic Programming , 2005, IEEE Trans. Pattern Anal. Mach. Intell..
[4] Christopher Joseph Pal,et al. Learning Conditional Random Fields for Stereo , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.
[5] Jitendra Malik,et al. Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[6] Dani Lischinski,et al. Colorization using optimization , 2004, ACM Trans. Graph..
[7] Zhengyou Zhang,et al. A Progressive Scheme for Stereo Matching , 2000, SMILE.
[8] Heiko Hirschmüller,et al. Evaluation of Cost Functions for Stereo Matching , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.
[9] Olga Veksler,et al. Markov random fields with efficient approximations , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).
[10] Kuk-Jin Yoon,et al. Locally adaptive support-weight approach for visual correspondence search , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[11] D. Nistér,et al. Stereo Matching with Color-Weighted Correlation, Hierarchical Belief Propagation, and Occlusion Handling , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[12] S. Birchfiled. A Pixel Dissimilarity Measure That Is Insensitive to Image Sampling , 1998 .
[13] Aaron F. Bobick,et al. Large Occlusion Stereo , 1999, International Journal of Computer Vision.
[14] William H. Press,et al. Numerical recipes in C , 2002 .
[15] Tianli Yu,et al. Efficient Message Representations for Belief Propagation , 2007, 2007 IEEE 11th International Conference on Computer Vision.
[16] Richard Szeliski,et al. A Comparative Study of Energy Minimization Methods for Markov Random Fields , 2006, ECCV.
[17] Miao Liao,et al. High-Quality Real-Time Stereo Using Adaptive Cost Aggregation and Dynamic Programming , 2006, Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06).
[18] Olga Veksler,et al. Fast Approximate Energy Minimization via Graph Cuts , 2001, IEEE Trans. Pattern Anal. Mach. Intell..
[19] Geoffrey Egnal,et al. Detecting Binocular Half-Occlusions: Empirical Comparisons of Five Approaches , 2002, IEEE Trans. Pattern Anal. Mach. Intell..
[20] Olga Veksler. Extracting dense features for visual correspondence with graph cuts , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..
[21] Radim Sára,et al. Efficient Sampling of Disparity Space for Fast And Accurate Matching , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.
[22] Olga Veksler. Reducing Search Space for Stereo Correspondence with Graph Cuts , 2006, BMVC.
[23] Radim Sára. Finding the Largest Unambiguous Component of Stereo Matching , 2002, ECCV.