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1998
IEEE

Markov Random Fields with Efficient Approximations

9 years 11 months ago
Markov Random Fields with Efficient Approximations
Markov Random Fields (MRF's) can be used for a wide variety of vision problems. In this paper we focus on MRF's with two-valued clique potentials, which form a generalized Potts model. We show that the maximum a posteriori estimate of such an MRF can be obtained by solving a multiway minimum cut problem on a graph. We develop efficient algorithms for computing good approximations to the minimum multiway cut. The visual correspondence problem can be formulated as an MRF in our framework; this yields quite promising results on real data with ground truth. We also apply our techniques to MRF's with linear clique potentials.
Yuri Boykov, Olga Veksler, Ramin Zabih
Added 12 Oct 2009
Updated 30 Oct 2009
Type Conference
Year 1998
Where CVPR
Authors Yuri Boykov, Olga Veksler, Ramin Zabih
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