Stereo matching based on color image segmentation and cross adaptive window

A method of stereo matching which is based on color image segmentation and cross adaptive window has been used to solve the problems of matching imprecision in depth discontinuity regions and low texture regions. This article first produces the matching cost based on color segmentation region by segmenting color image and the one based on cross adaptive window which makes use of color similarity, and then integrates the two to form the combined matching cost. At last it uses the fast searching method of optical parallax to narrow the matching range and then raise the efficiency rate. The result of this experiment can improve the matching accuracy in depth discontinuity regions and low texture regions and raise the matching speed.

[1]  Rafael Cabeza,et al.  Stereo matching using gradient similarity and locally adaptive support-weight , 2011, Pattern Recognit. Lett..

[2]  Ieee Xplore,et al.  IEEE Transactions on Pattern Analysis and Machine Intelligence Information for Authors , 2022, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Yo-Sung Ho,et al.  Advances in Image and Video Technology , 2011, Lecture Notes in Computer Science.

[4]  Gauthier Lafruit,et al.  Cross-Based Local Stereo Matching Using Orthogonal Integral Images , 2009, IEEE Transactions on Circuits and Systems for Video Technology.

[5]  Narendra Ahuja,et al.  A constant-space belief propagation algorithm for stereo matching , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

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

[7]  Marc Pollefeys,et al.  Temporally Consistent Reconstruction from Multiple Video Streams Using Enhanced Belief Propagation , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[8]  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).

[9]  Olga Veksler,et al.  Fast variable window for stereo correspondence using integral images , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[10]  Cyril Cassisa,et al.  Local vs global energy minimization methods: Application to stereo matching , 2010, 2010 IEEE International Conference on Progress in Informatics and Computing.

[11]  Luis Rueda,et al.  Advances in Image and Video Technology, Second Pacific Rim Symposium, PSIVT 2007, Santiago, Chile, December 17-19, 2007, Proceedings , 2007, PSIVT.

[12]  Emanuele Trucco,et al.  Efficient stereo with multiple windowing , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[13]  In-So Kweon,et al.  Adaptive Support-Weight Approach for Correspondence Search , 2006, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Federico Tombari,et al.  Segmentation-Based Adaptive Support for Accurate Stereo Correspondence , 2007, PSIVT.

[15]  Pramode K. Verma,et al.  Dense image registration using sparse coding and belief propagation , 2011, ICSPCS.

[16]  Wu Chun.  A Stereo Matching Method Based on K-means Segmentation and Neighborhood Constraints Relaxation , 2011 .

[17]  Federico Tombari,et al.  Near real-time stereo based on effective cost aggregation , 2008, 2008 19th International Conference on Pattern Recognition.