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Content Provider | IEEE Xplore Digital Library |
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Author | Veksler, O. |
Copyright Year | 2007 |
Description | Author affiliation: Univ. of Western Ontario, London (Veksler, O.) |
Abstract | Optimization with graph cuts became very popular in recent years. Progress in problems such as stereo correspondence, image segmentation, etc., can be attributed, in part, to the development of efficient graph cut based optimization. Recent evaluation of optimization techniques shows that the popular expansion and swap graph cut algorithms perform extremely well for energies where the underlying MRF has the Potts prior, which corresponds to the assumption that the true labeling is piecewise constant. For more general priors, however, such as corresponding to piece-wise smoothness assumption, both swap and expansion algorithms do not perform as well. We develop several optimization algorithms for truncated convex priors, which imply piecewise smoothness assumption. Both expansion and swap algorithms are based on moves that give each pixel a choice of only two labels. Our insight is that to obtain a good approximation under piecewise smoothness assumption, a pixel should have a choice among more than two labels. We develop new "range" moves which act on a larger set of labels than the expansion and swap algorithms. We evaluate our method on problems of image restoration, in-painting, and stereo correspondence. Our results show that we are able to get more accurate answers, both in terms of the energy, which is the direct goal, and in terms of accuracy, which is an indirect, but more important goal. |
Starting Page | 1 |
Ending Page | 8 |
File Size | 324631 |
Page Count | 8 |
File Format | |
ISBN | 1424411793 |
ISSN | 10636919 |
DOI | 10.1109/CVPR.2007.383249 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2007-06-17 |
Publisher Place | USA |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Labeling Image segmentation Image restoration Stereo vision Performance evaluation Computational modeling Simulated annealing Computer vision Computer graphics Stereo image processing |
Content Type | Text |
Resource Type | Article |
Subject | Computer Vision and Pattern Recognition Software |
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