A large variety of computer vision tasks can be formulated using
Markov Random Fields (MRF). Except in certain special cases,
optimizing an MRF is intractable, due to a large number of
variables and complex dependencies between them. In this thesis,
we present new algorithms to perform inference in MRFs, that are
either more efficient (in terms of running time and/or memory
usage) or more effective (in terms of solution quality), than the
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