Stereo matching based on guided filter and segmentation

Abstract For improving the accuracy of stereo matching and maintaining discontinuity of object edge and continuity of non-edge area in the matching result, a stereo matching method based on guided filter and mean shift is proposed in this paper. Matching cost function based on Markov random field (MRF) and guided filter are established, and the initial disparity value of each pixel is calculated by minimizing the corresponding matching cost function. Mean shift algorithm is used to segment the stereo images into independent areas, and improve the final disparity map. Results of the proposed method are tested on the international standard Middlebury stereo benchmark and compared with other methods. Comparative results show high accuracy of the proposed algorithm and its superiority to some prevailing algorithms.

[1]  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.

[2]  Olga Veksler,et al.  Fast Approximate Energy Minimization via Graph Cuts , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Andreas Klaus,et al.  Segment-Based Stereo Matching Using Belief Propagation and a Self-Adapting Dissimilarity Measure , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[4]  Daniel P. Huttenlocher,et al.  Efficient Belief Propagation for Early Vision , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[5]  Nanning Zheng,et al.  Stereo Matching Using Belief Propagation , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Roberto Manduchi,et al.  Bilateral filtering for gray and color images , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[7]  Jian Sun,et al.  Guided Image Filtering , 2010, ECCV.

[8]  Olga Veksler,et al.  Stereo correspondence by dynamic programming on a tree , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[9]  Stan Z. Li,et al.  Markov Random Field Modeling in Image Analysis , 2001, Computer Science Workbench.

[10]  William T. Freeman,et al.  Learning Low-Level Vision , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[11]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Aaron F. Bobick,et al.  Large Occlusion Stereo , 1999, International Journal of Computer Vision.

[13]  Qingxiong Yang,et al.  A non-local cost aggregation method for stereo matching , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Richard Szeliski,et al.  A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms , 2001, International Journal of Computer Vision.

[15]  Ruigang Yang,et al.  Stereo Matching with Color-Weighted Correlation, Hierarchical Belief Propagation and Occlusion Handling , 2006, CVPR.