IEEE Trans Image Process - Minimization of monotonically levelable higher order MRF energies via graph cuts.

Tópicos

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Resumo

A feature of minimizing images of submodular binary Markov random field energies is introduced. Using this novel feature, the collection of minimizing images of levels of higher order, monotonically levelable multi label MRF energies is shown to constitute a monotone collection. This implies that these minimizing binary images can be combined to give minimizing images of the multi label MRF energies. Thanks to the graph cuts framework, the mentioned class of binary MRF energies is known to be minimized by maximum flow computations on appropriately constructed graphs. With the aid of these developments an exact and efficient algorithm to minimize monotonically levelable multi label MRF energies of any order, which is composed of a series of maximum flow computations, is proposed and an application of the proposed algorithm to image denoising is given.

Resumo Limpo

featur minim imag submodular binari markov random field energi introduc use novel featur collect minim imag level higher order monoton level multi label mrf energi shown constitut monoton collect impli minim binari imag can combin give minim imag multi label mrf energi thank graph cut framework mention class binari mrf energi known minim maximum flow comput appropri construct graph aid develop exact effici algorithm minim monoton level multi label mrf energi order compos seri maximum flow comput propos applic propos algorithm imag denois given

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