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http://dx.doi.org/10.6109/jkiice.2010.14.5.1217

A Stereo Matching Method Based on the Dynamic Programming to Reduce the Streaking Phenomena  

Park, Jang-Ho (광운대학교 실감미디어 연구소)
Choi, Hyun-Jun (안양대학교 정보통신공학과)
Seo, Young-Ho (광운대학교 실감미디어 연구소)
Kim, Dong-Wook (광운대학교 실감미디어 연구소)
Abstract
The dynamic programming based methods, a kind of globally optimizing stereo matching methods, has the inherent advantage that the occlusion regions can be found during the process. But it also has a serious drawback of streaking phenomena. This paper focuses on reducing the streaking phenomena by adjusting the penalties in calculating the cost matrix and re-establishing the optimal path in the back-tracing process using the boundary information of the image. Especially we use a pixel expansion method in re-establishing the path, which is the results from expanding the pixel information of the ones just left the boundaries. Experiments with the four image pairs provided by the Middlebury site showed the results that the proposed method has the disparity error ratio of 6.33% and the rank is 29, which is competitive to the best method among the previously published dynamic programming based methods.
Keywords
Dynamic Programming; Stereo Matching; Disparity; Stereo Image;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 H. Hirschumuller, "Stereo Processing by Semiglobal Matching and Mutual Information," IEEE Trans. on Pattern Analysis and Machine Intelligence. Vol. 30, No.2, pp. 328-341, Feb. 2008.   DOI   ScienceOn
2 http://vision.middlebury.edu/stereo/
3 R. C. Gonzalez and R. E. Woods, Digital Image Processing. Pearson Prentice Hall, NJ, 2008.
4 O. Veksler, "Stereo Correspondence by Dynamic Programming on a Tree," IEEE Conf. on CVRP, pp. 20-25, June 2005.
5 M. Bleyer and M. Gelautz, "Simple but Effective Tree Structure for Dynamic Programming-Based Stereo Matching," Intl. Conf. on Computer Vision Theory and Applications. pp. 415-422, 2008.
6 C. Leung, B. Appleton, and C. Sun, "Iterated Dynamic Programming and Quadtree Subregioning for Fast Stereo Matching," J. of Image and Vision Computing, Vol. 26, pp. 1371-1383, 2008.   DOI   ScienceOn
7 C. Lei, S.elzer, J. and Y. H. Yang, "Region-Tree Based Stereo Using Dynamic Programming Optimization," IEEE Conf. on Computer Vision and Pattern Recognition, Vol. 2, pp.2378-2385, 2006.
8 Y. Taguchi, B. Wilburn, and C. L. Zitnick, "Stereo Reconstruction with Mixed Pixels using Adaptive Over-Segmentation," IEEE Conf. on Computer Vision and Pattern Recognition, June 2008.
9 M. Gong, Y. H. Yang, "Fast Unambiguous Stereo Matching Using Reliability-Based Dynamic Programming," IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 27, No 6, pp. 998-1003, June 2005.   DOI   ScienceOn
10 D. SchSarstein and R. Szeliski, "A Taxonomy and Evaluation of Dense Two-frame Stereo Correspondence Algorithms," International Journal of Computer Vision, Vol. 47, Issue 1-3, pp. 7-42, April 2002.   DOI
11 M. Gong, Y. H. Yang, "Near Real-time Reliable Stereo Matching using Programmable Graphics Husiwuse," IEEE Conf. on Computer Vision and Pattern Recognition, Vol. 1, pp.20-25, June, 2005.
12 M. Gong, Y. H. Yang, "Real-Time Stereo Matching Using Orthogonal Reliability-Based Dynamic Programming," IEEE Transactions on Image processing, Vol. 16, No 3, pp. 879-884, Mar 2007.   DOI   ScienceOn
13 주재흠, 오종규, 설성욱, 이철훈, 남기곤, "에지 정보를 강조한 동적계획법에 의한 스테레오 정합," 대한전자공학회논문지, 제36권, 제10호, pp. 123-131, 1999.
14 Q. Yang, et al, "Stereo Matching with Color-Weighted Correlation, Hierarchical Belief Propagation, and Occlusion Handling", IEEE Trans. on Pattern Analysis and Machine Intelligence. Vol. 31, No. 3, pp. 495-504, March 2009.
15 V. Kolmogorov and R. Zabih, "Computing Visual Correspondence with Occlusions via Graph Cuts," ICCV2001, Vol. 2, pp. 508-515, 2001.
16 H. Chen, "Stereo Matching by Dynamic Programming Based on Occlusion Detection," IEEE Intl. Conf. on Mechatronics and Automation, pp. 2445-2449, 2007.
17 ISO/IEC MPEG & ITU-T VCEG, "Multiview Video plus Depth (MVD) Format for Advanced 3D Video Systems," JVT-W100, April 2007.
18 Federico Tombari, Stefano Mattoccia, and Luigi Di Stefano, "Segmentation-Based Adaptive Support for Accurate Stereo Correspondence," LNCS 4872, pp.427-438, 2007.
19 Z. F. Wang and Z. G. Zheng, "A Region Based Stereo Matching Algorithm Using Cooperative Optimization," IEEE Conf. on Computer Vision and Pattern Recognition. pp. 1-8, June 2008.
20 Tae-june Kim, Ji-sang Yoo, "Hierarchical Stereo Matching with Color Information", 한국통신학회논문지, 제34권, 제3호, pp.279-287, 2009.   과학기술학회마을
21 Q. Yang, et al., "Spatial-Depth Super Resolution for Range Images," IEEE Conf. on Computer Vision and Pattern Recognition. pp. 17-22, June 2007.
22 J. Zhas and J. Katupitiya, "A Dymanic Programming Approach Based Stereo Vision Algorithm Improving Object Boarder Performance," IEEE/ERJ Intl. Conf. on Intelligent Robotics and Systems, pp. 5315-5320, Oct. 2006.
23 Y. Wei, L. Quan, "Region-based Progressive Stereo Matching," IEEE Proceeding, Vol. 1, No. 27, pp. 106-113, July 2004.