Motion estimation with non-local total variation regularization

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

Volumn , Issue , 2010, Pages 2464-2471

  Werlberger, Manuel ^a   Pock, Thomas ^a   Bischof, Horst ^a  

^a Institute for Computer Graphics and Vision   (Austria)

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARK DATA; GESTALT PRINCIPLES; IMAGE SEGMENTATION PROCESS; IMAGE STRUCTURES; LOW LEVEL; MOTION ESTIMATION ALGORITHM; NONLOCAL; NUMERICAL RESULTS; SMALL SCALE; TEXTURED REGIONS; TOTAL VARIATION REGULARIZATION; VARIATIONAL FRAMEWORK;

COMPUTER VISION; ESTIMATION; MOTION ESTIMATION; NUMERICAL ANALYSIS; OPTICAL FLOWS;

IMAGE SEGMENTATION;

EID: 77955994781     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2010.5539945     Document Type: Conference Paper

References (29)

1. A.Bruhn, J.Weickert, and C.Schnörr. Lucas/Kanade meets Horn/Schunck: Combining local and global optic flow methods. Int'l J. Computer Vision, 61(3):211-231, 2005. 2
2. G. Aubert, R. Deriche, and P. Kornprobst. Computing optical flow via variational techniques. SIAM J. Appl. Math., 60(1):156-182, 1999. 2
3. S. Baker, D. Scharstein, J. P. Lewis, S. Roth, M. J. Black, and R. Szeliski. A database and evaluation methodology for optical flow. In ICCV, 2007. 1, 5
4. M. J. Black and P. Anandan. Robust dynamic motion estimation over time. pages 296-302, June 1991. 1
5. T. Brox, A. Bruhn, N. Papenberg, and J. Weickert. High accuracy optical flow estimation based on a theory for warping. In ECCV, pages 25-36, 2004. 1, 2
6. T. Brox, O. Kleinschmidt, and D. Cremers. Efficient nonlocal means for denoising of textural patterns. IEEE Trans. on Image Processing, 17(7):1083-1092, July 2008. 2
7. A. Buades, B. Coll, and J.-M. Morel. Nonlocal image and movie denoising. International Journal of Computer Vision, 76(2):123-139, 2007. 2
8. D. J. Fleet and Y. Weiss. Optical flow estimation. In N. Paragios, Y. Chen, and O. Faugeras, editors, Handbook of Mathematical Models in Computer Vision, chapter 15, pages 239-258. Springer, 2006. 2
9. G. Gilboa and S. J. Osher. Nonlocal operators with applications to image processing. Technical report, UCLA CAM Report 07-23, July 2007. 2
10. B. K. P. Horn and B. G. Schunck. Determining optical flow. Artificial Intelligence, 17:185-203, 1981. 1
11. H.-H. Nagel and W. Enkelmann. An investigation of smoothness constraints for the estimation of displacement vector fields from image sequences. IEEE Trans. on Pattern Analysis and Machine Intelligence, 8(5):565-593, 1986. 2
12. M. Nikolova. Model distortions in bayesian map reconstruction. Inverse Problems and Imaging, (2):399-422, 2007. 2
13. T. Pock, D. Cremers, H. Bischof, and A. Chambolle. An algorithm for minimizing the mumford-shah functional. In ICCV, 2009. 4
14. R. T. Rockafellar. Convex analysis. Princeton Landmarks in Mathematics. Princeton University Press, Princeton, NJ, 1997. Reprint of the 1970 original, Princeton Paperbacks. 4
15. S. Roth and M. J. Black. On the spatial statistics of optical flow. IJCV, 74(1):33-50, August 2007. 2
16. L. Rudin, S. J. Osher, and E. Fatemi. Nonlinear total variation based noise removal algorithms. Physica D, 60:259-268, 1992. 2
17. D. Shulman and J.-Y. Hervé. Regularization of discontinuous flow fields. In Proceedings Workshop on Visual Motion, pages 81-86, 1989. 1
18. F. Steinbruecker, T. Pock, and D. Cremers. Advanced data terms for variational optic flow estimation. In Vision, Modeling, and Visualization Workshop, Braunschweig, Germany, 2009. 1, 3
19. D. Q. Sun, S. Roth, J. P. Lewis, and M. J. Black. Learning optical flow. In ECCV, volume III, pages 83-97, 2008. 2
20. C. Tomasi and R. Manduchi. Bilateral filtering for gray and color images. In ICCV, pages 839-846, 1998. 2
21. W. Trobin, T. Pock, D. Cremers, and H. Bischof. An unbiased second-order prior for high-accuracy motion estimation. In Pattern Recognition, DAGM 2008, 2008. 2
22. C. Vogel and M. Oman. Iteration methods for total variation denoising. SIAM J. Sci. Comp., 17:227-238, 1996. 4
23. A. Wedel, D. Cremers, T. Pock, and H. Bischof. Structure- and motion-adaptive regularization for high accuracy optic flow. In ICCV, Kyoto, Japan, 2009. 2
24. A. Wedel, T. Pock, C. Zach, D. Cremers, and H. Bischof. An improved algorithm for TV-L1 optical flow. In Proceedings of the Dagstuhl Motion Workshop, September 2008. 3
25. M. Werlberger, W. Trobin, T. Pock, A. Wedel, D. Cremers, and H. Bischof. Anisotropic Huber-L1 optical flow. In BMVC, London, UK, September 2009. 2, 3
26. J. Xiao, H. Cheng, H. Sawhney, C. Rao, and M. Isnardi. Bilateral filtering-based optical flow estimation with occlusion detection. In ECCV, volume I, pages 211-224, 2006. 2
27. L. P. Yaroslavsky. Digital Picture Processing - an Introduction. Springer, Berlin, 1985. 2
28. K.-J. Yoon and I. S. Kweon. Adaptive support-weight approach for correspondence search. IEEE Trans. on Pattern Analysis and Machine Intelligence, 28(4):650-656, 2006. 2, 3
29. C. Zach, T. Pock, and H. Bischof. A duality based approach for realtime TV-L1 optical flow. In Pattern Recognition, DAGM, pages 214-223, 2007. 1, 2