Graph cut based continuous stereo matching using locally shared labels

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2014, Pages 1613-1620

  Taniai, Tatsunori ^a   Matsushita, Yasuyuki ^b   Naemura, Takeshi ^a  

^a UNIVERSITY OF TOKYO   (Japan)

^b MICROSOFT RESEARCH ASIA   (China)

Author keywords

[No Author keywords available]

Indexed keywords

IMAGE CODING; IMAGE REGISTRATION; MAGNETORHEOLOGICAL FLUIDS; PATTERN RECOGNITION; PIXELS; STEREO IMAGE PROCESSING;

BELIEF PROPAGATION; DISPARITY ESTIMATIONS; ENERGY MINIMIZATION; PARALLEL COMPUTATION; RANDOMIZED SEARCH; SPATIAL PROPAGATION; STEREO MATCHING METHOD; SUBPIXEL ACCURACY;

GRAPHIC METHODS;

EID: 84911443836     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2014.209     Document Type: Conference Paper

References (27)

1. C. Barnes, E. Shechtman, A. Finkelstein, and D. B. Goldman. Patch-Match: a randomized correspondence algorithm for structural image editing. Proc. ofSIGGRAPH (ACM Trans. on Graph.), 28(3):24:1-24:11, 2009.
2. C. Barnes, E. Shechtman, D. B. Goldman, and A. Finkelstein. The generalized patchmatch correspondence algorithm. In Proc. of European Conf. on Computer Vision (ECCV), pages 29-43, 2010.
3. F. Besse, C. Rother, A. Fitzgibbon, and J. Kautz. PMBP: PatchMatch Belief Propagation for Correspondence Field Estimation. In Proc. of British Machine Vision Conf. (BMVC), pages 132.1-132.11, 2012.
4. M. Bleyer, C. Rhemann, and C. Rother. PatchMatch Stereo-Stereo Matching with Slanted Support Windows. In Proc. of British Machine Vision Conf. (BMVC), pages 14.1-14.11, 2011.
5. Y. Boykov and V. Kolmogorov. An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Trans. Pattern Anal. Mach. Intell., 26(9):1124-1137, 2004.
6. Y. Boykov, O. Veksler, and R. Zabih. Fast approximate energy minimization via graph cuts. IEEE Trans. Pattern Anal. Mach. Intell., 23(11):1222-1239, 2001.
7. P. Felzenszwalb and D. Huttenlocher. Efficient belief propagation for early vision. In Proc. of IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), volume 1, pages 261-268, 2004.
8. S. Geman and D. Geman. Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images. IEEE Trans. Pattern Anal. Mach. Intell., 6(6):721-741, 1984.
9. P. Heise, S. Klose, B. Jensen, and A. Knoll. PM-Huber: PatchMatch with Huber Regularization for Stereo Matching. In Proc. of Int'l Conf. on Computer Vision (ICCV), pages 2360-2367, 2013.
10. L. Hong and G. Chen. Segment-based stereo matching using graph cuts. In Proc. of IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), volume 1, pages 74-81, 2004.
11. A. Klaus, M. Sormann, and K. Karner. Segment-Based Stereo Matching Using Belief Propagation and a Self-Adapting Dissimilarity Measure. In Proc. of Int'l Conf. on Pattern Recognition (ICPR), volume 3, pages 15-18, 2006.
12. V. Kolmogorov. Convergent Tree-Reweighted Message Passing for Energy Minimization. IEEE Trans. Pattern Anal. Mach. Intell., 28(10):1568-1583, 2006.
13. V. Kolmogorov and C. Rother. Minimizing Nonsubmodular Functions with Graph Cuts-A Review. IEEE Trans. Pattern Anal. Mach. Intell., 29(7):1274-1279, 2007.
14. V. Kolmogorov and R. Zabih. Computing visual correspondence with occlusions using graph cuts. In Proc. of Int'l Conf. on Computer Vision (ICCV), volume 2, pages 508-515, 2001.
15. V. Kolmogorov and R. Zabih. Multi-camera Scene Reconstruction via Graph Cuts. In Proc. of European Conf. on Computer Vision (ECCV), pages 82-96, 2002.
16. V. Kolmogorov and R. Zabin. What energy functions can be minimized via graph cuts? IEEE Trans. Pattern Anal. Mach. Intell., 26(2):147-159, 2004.
17. V. Lempitsky, C. Rother, S. Roth, and A. Blake. Fusion Moves for Markov Random Field Optimization. IEEE Trans. Pattern Anal. Mach. Intell., 32(8):1392-1405, 2010.
18. J. Lu, H. Yang, D. Min, and M. N. Do. Patch Match Filter: Efficient Edge-Aware Filtering Meets Randomized Search for Fast Correspondence Field Estimation. In Proc. of IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pages 1854-1861, 2013.
19. C. Olsson, J. Ulen, and Y. Boykov. In Defense of 3D-Label Stereo. In Proc. of IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pages 1730-1737, 2013.
20. R. Szeliski, R. Zabih, D. Scharstein, O. Veksler, V. Kolmogorov, A. Agarwala, M. Tappen, and C. Rother. A Comparative Study of Energy Mnimization Methods for Markov Random Fields with Smoothness-Based Priors. IEEE Trans. Pattern Anal. Mach. Intell., 30(6):1068-1080, 2008.
21. H. Tao, H. Sawhney, and R. Kumar. A global matching framework for stereo computation. In Proc. of Int'l Conf. on Computer Vision (ICCV), volume 1, pages 532-539, 2001.
22. L. Wang and R. Yang. Global stereo matching leveraged by sparse ground control points. In Proc. of IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pages 3033-3040, 2011.
23. Z.-F. Wang and Z.-G. Zheng. A region based stereo matching algorithm using cooperative optimization. In Proc. of IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2008.
24. Y Wei and L. Quan. Asymmetrical Occlusion Handling Using Graph Cut for Multi-View Stereo. In Proc. of IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pages 902-909, 2005.
25. O. Woodford, P. Torr, I. Reid, and A. Fitzgibbon. Global Stereo Reconstruction under Second-Order Smoothness Priors. IEEE Trans. Pattern Anal. Mach. Intell., 31(12):2115-2128, 2009.
26. J. S. Yedidia, W T. Freeman, and Y Weiss. Generalized Belief Propagation. In Advances in Neural Information Processing Systems (NIPS), volume 13, pages 689-695, 2000.
27. K.-J. Yoon and I.-S. Kweon. Locally adaptive support-weight approach for visual correspondence search. In Proc. of IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), volume 2, pages 924-931, 2005.