Parallel and distributed graph cuts by dual decomposition

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

Volumn , Issue , 2010, Pages 2085-2092

  Strandmark, Petter ^a   Kahl, Fredrik ^a  

^a LUND UNIVERSITY   (Sweden)

Author keywords

[No Author keywords available]

Indexed keywords

DATA SETS; DISTRIBUTED NETWORKS; DUAL DECOMPOSITION; EMPIRICAL EVALUATIONS; GRAPH CUT; MULTI CORE; MULTIPLE COMPUTERS; MULTIPLE PARTS; OPEN SOURCE IMPLEMENTATION; OPTIMAL SOLUTIONS; OPTIMALITY; SUB-PROBLEMS;

COMPUTER VISION;

EID: 77956008810     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2010.5539886     Document Type: Conference Paper

References (23)

1. D. P. Bertsekas. Nonlinear programming. Athena Scientific, 1999.
2. 1 norm minimization. IEEE Trans. Pattern Analysis and Machine Intelligence, 30(10):1866-1871, 2008.
3. Y. Boykov and V. Kolmogorov. An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Trans. Pattern Analysis and Machine Intelligence, 26(9):1124-1137, Sept. 2004.
4. Y. Boykov, O. Veksler, and R. Zabih. Markov random fields with efficient approximations. In Conf. Computer Vision and Pattern Recognition, 1998.
5. A. Delong and Y. Boykov. A scalable graph-cut algorithm for N-D grids. In Conf. Computer Vision and Pattern Recognition, 2008.
6. H. Everett. Generalized Lagrange multiplier method for solving problems of optimum allocation of resources. Operations Research, 11:399-417, 1963.
7. L. M. Goldschlager, R. A. Shaw, and J. Staples. The maximum flow problem is log space complete for P. Theoretical Comput. Sci., 21(1):105-111, Oct. 1982.
8. D. M. Greig, B. T. Porteous, and A. H. Seheult. Exact maximum a posteriori estimation for binary images. Journal of the Royal Statistical Society, 1989.
9. M. Klodt, T. Schoenemann, K. Kolev, M. Schikora, and D. Cremers. An experimental comparison of discrete and continuous shape optimization methods. In European Conf. Computer Vision, 2008.
10. P. Kohli and P. H. S. Torr. Efficiently solving dynamic markov random fields using graph cuts. In Int. Conf. Computer Vision, 2005.
11. N. Komodakis, N. Paragios, and G. Tziritas. MRF optimization via dual decomposition: Message-passing revisited. In Int. Conf. Computer Vision. IEEE, 2007.
12. V. Lempitsky and Y. Boykov. Global optimization for shape fitting. In Conf. Computer Vision and Pattern Recognition, Minneapolis, USA, June 2007.
13. V. Lempitsky, C. Rother, S. Roth, and A. Blake. Fusion moves for markov random field optimization. IEEE Trans. Pattern Analysis and Machine Intelligence, 2009. To appear.
14. J. Liu and J. Sun. Parallel graph-cuts by adaptive bottom-up merging. In Conf. Computer Vision and Pattern Recognition, 2010.
15. J. Liu, J. Sun, and H.-Y. Shum. Paint selection. ACM Transactions on Graphics, 28(3):1-7, 2009.
16. D. Martin, C. Fowlkes, D. Tal, and J. Malik. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In Int. Conf. Computer Vision, 2001.
17. U. of Western Ontario. Max-flow problem instances in vision. http://vision.csd.uwo.ca/maxflow-data.
18. C. H. Papadimitriou and K. Steiglitz. Combinatorial Optimization; Algorithms and Complexity. Dover Publications, 1998.
19. A. Rantzer. Dynamic dual decomposition for distributed control. In American Control Conference, 2009.
20. C. Rother, V. Kolmogorov, and A. Blake. Grabcut -interactive foreground extraction using iterated graph cuts. ACM Transactions on Graphics, 23(3):309-314, 2004.
21. M. Snir and S. Otto. MPI - The Complete Reference: The MPI Core. MIT Press, Cambridge, MA, USA, 1998.
22. V. Vineet and P. Narayanan. CUDA cuts: Fast graph cuts on the GPU. In Computer Vision and Pattern Recognition Workshops, CVPRW, June 2008.
23. Wikipedia. Max-flow min-cut theorem - Wikipedia, the Free Encyclopedia. [Online; accessed 9-January-2010].