Tight convex relaxations for vector-valued labeling problems

Proceedings of the IEEE International Conference on Computer Vision

Volumn , Issue , 2011, Pages 2328-2335

  Strekalovskiy, Evgeny ^a   Goldluecke, Bastian ^a   Cremers, Daniel ^a  

^a TECHNICAL UNIVERSITY OF MUNICH   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

CONVEX RELAXATION; DATA TERMS; GLOBAL MINIMA; GLOBAL OPTIMUM; LABEL SPACE; MEMORY EFFICIENT; MULTI-LABEL; OPTIMALITY; VECTOR VALUED;

IMAGE PROCESSING; RELAXATION PROCESSES;

PROBLEM SOLVING;

EID: 84856650181     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2011.6126514     Document Type: Conference Paper

References (19)

1. G. Albert, G. Bouchitt, and G. D. Maso. The calibration method for the Mumford-Shah functional and freediscontinuity problems. Calculus of Variations and Partial Differential Equations, 16(3):299-333, 2002. 3, 5, 7
2. H. Attouch, G. Buttazzo, and G. Michaille. Variational Analysis in Sobolev and BV Spaces. MPS-SIAM Series on Optimization. Society for Industrial and Applied Mathematics, 2006. 2
3. Y. Boykov, O. Veksler, and R. Zabih. Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Intelligence, 23(11):1222-1239, 2001. 2
4. T. Brox and D. Cremers. On local region models and a statistical interpretation of the piecewise smooth Mumford-Shah functional. International Journal of Computer Vision, 84:184-193, 2009. 7
5. A. Chambolle, D. Cremers, and T. Pock. A convex approach for computing minimal partitions. Technical Report TR-2008-05, Dept. of Computer Science, University of Bonn, 2008. 1, 2, 5, 7
6. A. Chambolle and T. Pock. A first-order primal-dual algorithm for convex problems with applications to imaging. preprint, 2010. 6
7. D. Cremers and K. Kolev. Multiview stereo and silhouette consistency via convex functionals over convex domains. IEEE Transactions on Pattern Analysis and Intelligence, 2010. 1
8. B. Glocker, N. Paragios, N. Komodakis, G. Tziritas, and N. Navab. Optical flow estimation with uncertainties through dynamic mrfs. In Proc. International Conference on Computer Vision and Pattern Recognition, 2008. 2
9. B. Goldluecke and D. Cremers. Convex relaxation for multilabel problems with product label spaces. In Proc. European Conference on Computer Vision, 2010. 1, 2, 3, 4, 5, 6, 7
10. D. Greig, B. Porteous, and A. Seheult. Exact maximum a posteriori estimation for binary images. J. Royal Statistics Soc., 51(Series B):271-279, 1989. 1
11. H. Ishikawa. Exact optimization for Markov random fields with convex priors. IEEE Transactions on Pattern Analysis and Intelligence, 25(10):1333-1336, 2003. 1
12. M. Klodt, T. Schoenemann, K. Kolev, M. Schikora, and D. Cremers. An experimental comparison of discrete and continuous shape optimization methods. In Proc. European Conference on Computer Vision, pages 332-345, 2008. 2
13. V. Kolmogorov and C. Rother. Minimizing non-submodular functions with graph cuts-a review. IEEE Transactions on Pattern Analysis and Intelligence, 29(7), 2007. 2
14. J. Lellmann, , F. Becker, and C. Schnörr. Convex optimization for multi-class image labeling with a novel family of total variation based regularizers. In Proc. International Conference on Computer Vision, 2009. 1, 2, 3, 4, 5, 6
15. M. Nikolova, S. Esedoglu, and T.Chan. Algorithms for finding global minimizers of image segmentation and denoising models. SIAM Journal of Applied Mathematics, 66(5):1632-1648, 2006. 1, 2
16. T. Pock, D. Cremers, H. Bischof, and A. Chambolle. An algorithm for minimizing the piecewise smooth Mumford-Shah functional. In Proc. International Conference on Computer Vision, 2009. 5
17. T. Pock, D. Cremers, H. Bischof, and A. Chambolle. Global solutions of variational models with convex regularization. SIAM Journal on Imaging Sciences, 2010. 1, 2, 5
18. M. Wainwright, T. Jaakkola, and A. Willsky. Map estimation via agreement on trees: message-passing and linear programming. IEEE Trans. Inf. Theory, 51(11):3697-3717, 2005. 2
19. C. Zach, D. Gallup, J. Frahm, and M. Niethammer. Fast global labeling for real-time stereo using multiple plane sweeps. In Vision, Modeling and Visualization, pages 243-252, 2009. 2