Solving stereo matching problems using interior point methods

4th International Symposium on 3D Data Processing, Visualization and Transmission, 3DPVT 2008 - Proceedings

Volumn , Issue , 2008, Pages 321-328

  Bhusnurmath, Arvind ^a   Taylor, Camillo J ^a  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COST FUNCTIONS; DATA HANDLING; DATA VISUALIZATION; FUNCTIONS; LINEAR SYSTEMS; MATRIX ALGEBRA; PIECEWISE LINEAR TECHNIQUES; PIXELS; THREE DIMENSIONAL COMPUTER GRAPHICS;

CONVEX FUNCTIONS; INTERIOR POINT METHODS; LINEAR PROGRAMS; PIECEWISE LINEAR; REGULARIZATION TERMS; SECOND DERIVATIVES; STEREO MATCHING;

LINEAR PROGRAMMING;

EID: 70350595584     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (26)

1. M. Ben-Ezra, S. Peleg, and M. Werman. Real-time motion analysis with linear programming. In ICCV, 1999.
2. M. Bleyer and M. Gelautz. A layered stereo matching algorithm using image segmentation and global visibility constraints. In ICIP, 2004.
3. S. Boyd and L. Vanden Berghe. Convex Optimization. Cambridge University Press, 2004.
4. Y. Boykov, O. Veksler, and R. Zabih. Fast approximate energy minimization via graph cuts. PAMI, 23(11), 2001.
5. C. Chekuri, S. Khanna, J. Naor, and L. Zosin. Approximation algorithms for the metric labeling problem via a new linear programming formulation. In SODA, 2001.
6. P. Concus, G. Golub, and G. Meurant. Block preconditioning for the conjugate gradient method. SIAM Journal of Scientific Computing, 6(1), 1985.
7. P. Felzenszwalb and D. Huttenlocher. Efficient belief propagation for early vision. In CVPR, 2004.
8. G. Golub and C. V. Loan. Matrix Computations. The Johns Hopkins University Press, 1996.
9. H. Hirschmuller. Accurate and efficient stereo processing by semi-global matching and mutual information. In CVPR, 2005.
10. H. Hirschmuller. Stereo vision in structured environments by consistent semi-global matching. In CVPR, 2006.
11. H. Jiang, M. Drew, and Z.-N. Li. Matching by linear programming and successive convexification. PAMI, 29(6), 2007.
12. A. Klaus, M. Sormann, and K. Karner. Segment-based stereo matching using belief propagation and a self-adapting dissimilarity measure. In ICPR, 2006.
13. V. Kolmogorov. Convergent tree-reweighted message passing for energy minimization. PAMI, 28(10), 2006.
14. V. Kolmogorov and R. Zabih. What energy functions can be minimized via graph cuts. PAMI, 26(2), 2004.
15. N. Komodakis and G. Tziritas. A new framework for approximate labeling via graph cuts. In ICCV, 2005.
16. D. Scharstein and R. Szeliski. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. IJCV, 47:7-42, 2002.
17. D. Scharstein and R. Szeliski. High-accuracy stereo depth maps using structured light. In CVPR, 2003.
18. C. Strecha, R. Fransens, and L. V. Gool. Combined depth and outlier estimation in multi-view stereo. In CVPR, 2006.
19. J. Sun, Y. Li, S. B. Kang, and H.-Y. Shum. Symmetric stereo matching for occlusion handling. In CVPR, 2005.
20. O. Woodford, P. Torr, I. Reid, and A. Fitzgibbon. Global stereo reconstruction under second order smoothness priors. In CVPR, 2008.
21. Q. Yang, L. Wang, R. Yang, H. Stewenius, and D. Nister. Stereo matching with color-weighted correlation, hierarchical belief propagation and occlusion handling. In CVPR, 2006.
22. Q. Yang, R. Yang, J. Davis, and D. Nister. Spatial-depth super resolution for range images. In CVPR, 2007.
23. C. Yanover, T. Meltzer, and Y. Weiss. Linear programming relaxations and belief propagation - an empirical study. Journal of Machine Learning Research, 7:1887-1907, 2006.
24. K.-J. Yoon and I. S. Kweon. Adaptive support-weight approach for correspondence search. PAMI, 28(4), 2006.
25. K.-J. Yoon and I. S. Kweon. Stereo matching with the distinctive similarity measure. In ICCV, 2007.
26. C. L. Zitnick and S. B. Kang. Stereo for image-based rendering using image over-segmentation. IJCV, 75(1), 2007.