Abstract—This paper introduces a new rigorous theoretical framework to address discrete MRF-based optimization in computer vision. Such a framework exploits ...
May 20, 2010 · This paper introduces a new rigorous theoretical framework to address discrete MRF-based optimization in computer vision.
Aug 19, 2016 · This paper introduces a new rigorous theoretical framework to address discrete MRF-based optimization in computer vision.
It is shown that by appropriately choosing what subproblems to use, one can design novel and very powerful MRF optimization algorithms, which are able to ...
This paper introduces a new rigorous theoretical framework to address discrete MRF-based optimization in computer vision. Such a framework exploits the ...
A new message-passing scheme for MRF optimization that inherits better theoretical properties than all other state-of-the-art message passing methods and in ...
Dual-decomposition methods for optimization have emerged as an extremely powerful tool for solving combinatorial problems in graphical models. These techniques ...
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In particular, for dual decomposition of energy minimization (MAP inference), using cycle subproblems leads to a much tighter relaxation than using trees, but ...
Abstract. Markov Random Fields (MRF) minimization is a well-known problem in computer vision. We consider the augmented dual of the MRF.
In dual decomposition a problem is broken into smaller subproblems and a solution to the relaxed problem is found. This method can be employed for MRF ...
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