Browse > Article
http://dx.doi.org/10.3745/KIPSTB.2010.17B.2.115

A Stereo Matching Technique using Multi-directional Scan-line Optimization and Reliability-based Hole-filling  

Baek, Seung-Hae (경북대학교 컴퓨터 및 로봇비전 연구실)
Park, Soon-Young (경북대학교 컴퓨터공학과)
Abstract
Stereo matching techniques are categorized in two major schemes, local and global matching techniques. In global matching schemes, several investigations are introduced, where cost accumulation is performed in multiple matching lines. In this paper, we introduce a new multi-line stereo matching techniques which expands a conventional single-line matching scheme to multiple one. Matching cost is based on simple normalized cross correlation. We expand the scan-line optimization technique to a multi-line scan-line optimization technique. The proposed technique first generates a reliability image, which is iteratively updated based on the previous reliability measure. After some number of iterations, the reliability image is completed by a hole-filling algorithm. The hole-filling algorithm introduces a disparity score table which records the disparity score of the current pixel. The disparity of an empty pixel is determined by comparing the scores of the neighboring pixels. The proposed technique is tested using the Middlebury and CMU stereo images. The error analysis shows that the proposed matching technique yields better performance than using conventional global matching algorithm.
Keywords
Stereo Matching; Scan-line Optimization; Hole filling; Multi-directional;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 P. Mordohai and G. Medioni, “Stereo using monocular cues within the tensor vot-ing framework,” PAMI Vol.28, No.6, pp.968-982, 2006.   DOI   ScienceOn
2 H. Hirschmuuller, “Stereo vision in structured environments by consistent semi-global matching,” CVPR pp.2386-2393, 2006.   DOI
3 S. Birchfield and C. Tomasi, “Depth Discontinuities by Pixel-to-pixel Stereo,” ICCV, pp. 1073-1080, 1998.
4 Y. Deng and X. Lin, “A Fast Line Segment Based Dense Stereo Algorithm Using Tree Dynamic Programming,” ECCV, Vol.3, pp.201-212, 2006.   DOI   ScienceOn
5 F. Felzenszwalb and P. Huttenlocher, “Efficient Belief Propagation for Early Vision,” Computer Vision and Pattern Recognition, Vol.1, pp.261-268, 2004.   DOI
6 M. Gong and Y. Yang, “Near real-time reliable stereo matching using programmable graphics hardware,” CVPR pp.924-931, 2005.   DOI
7 M. Heinrichs, V. Rodehorst, and O. Hellwich, “Efficient Semi-Global Matching for Trinocular Stereo,” Photogrammetric Image Analysis, pp.185-190, 2007.
8 H. Hirschmüuller, “Accurate and efficient stereo processing by semi-global match-ing and mutual information,” CVPR, pp.807-814, 2005.   DOI
9 V. Kolmogorov and R. Zabih, “Computing Visual Correspondence with Occlusions Using Graph Cuts,” ICCV, pp.508-515, 2001.   DOI
10 S. Larsen, P. Mordohai, M. Pollefeys, and H. Fuchs, “Temporally consistent re-construction from multiple video streams using enhanced belief propagation,” ICCV, pp.1-8, 2007.   DOI
11 S. Mattoccia, F. Tombari, and L. Di Stefano, “Stereo vision enabling precise border localization within a scanline optimization framework,” ACCV, pp.517-527, 2007.
12 O. Veksler, “Stereo Correspondence by Dynamic Programming on a Tree,” CVPR, pp.384-390, 2005.   DOI
13 Q. Yang, L. Wang, R. Yang, H. Stewenius, and D. Nister, “Stereo Matching with Color-weighted Correlation, Hierarchical Belief Propagation and Occlusion Handling,” CVPR, Vol.31, No.3, pp.492-504, 2006.   DOI
14 Q. Yang, R. Yang, J. Davis, and D. Nist, “Spatial-depth super resolution for range images,” CVPR pp.1-8, 2007.   DOI
15 T. Kanade and M. Okutomi, “A Stereo Matching Algorithm with an Adaptive Window: Theory and Experiment,” IEEE transactions on pattern analysis and machine intelligence Vol.16, No.9, pp.920-932, 1994.   DOI   ScienceOn
16 C. Kim, K. M. Lee, B. T. Choi, and S. U. Lee, “A Dense Stereo Matching Using Two-pass Dynamic Programming with Generalized Ground Control Points,” CVPR, pp.1075-1082, 2005.   DOI
17 J. Sun, Y. Li, S.B. Kang, and H.-Y. Shum, “Symmetric stereo matching for occlu-sion handling,” CVPR Vol.2, pp.399-406, 2005.   DOI
18 A. Klaus, M. Sormann and K. Karner, “Segment-based stereo matching using belief propagation and a self-adapting dissimilarity measure,” ICPR Vol.3, pp.15-18, 2006.   DOI
19 http://vision.middlebury.edu/stereo. Middlebury Stereo Evaluation
20 D. Scharstein and R. Szeliski, “A taxonomy and evaluation of dense two-frame stereo correspondence algorithms,” IJCV, Vol.47, pp.7-42. 2002.   DOI
21 M. C. Sung, S. H. Lee, and N. I. Cho, “Stereo Matching Using Multi-directional Dynamic Programming and Edge Orientations,” ICIP, pp.233-236, 2007.   DOI
22 L. Zitnick and S. B. Kang, “Stereo for Image-based Rendering Using Image Over-segmentation,” IJCV, Vol.75, No.1, pp.49-65, 2007.   DOI
23 C. Ye, C. Moon, and J. Jeon, “방향성 특징벡터를 이용한 스테레오 정합 기법,” 제어 자동화 시스템공학 논문지, 제13권 제1호, pp.52-57, 2007.   과학기술학회마을