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Graph algorithms for more efficient inference in 1st-order and higher-order MRF's

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Efficient inference is a major challenge for the MRF ’s that arise in computer vision. Most such MRF ’s are 1st-order, and are typically solved with methods like message passing or graph cuts. I will present a new preprocessing technique for 1st-order MRF ’s that makes widely used graph cut methods an order of magnitude more efficient. Higher-order MRF ’s are very powerful, but present a much more difficult challenge; I will describe techniques based on a variant of submodular flow that can perform efficient inference over some important higher-order priors.

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