Dynamic graph cuts for efficient inference in Markov random fields

Dynamic graph cuts for efficient inference in Markov random fields

In this paper, we present a fast new fully dynamic algorithm for the st-mincut/max-flow problem. We show how this algorithm can be used to efficiently compute MAP solutions for certain dynamically changing MRF models in computer vision such as image segmentation. Specifically, given the solution of...

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Dades bibliogràfiques
Autors principals:	Kohli, P, Torr, PHS
Format:	Journal article
Idioma:	English
Publicat:	IEEE 2007

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