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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Dettagli Bibliografici
Autori principali:	Kohli, P, Torr, PHS
Natura:	Journal article
Lingua:	English
Pubblicazione:	IEEE 2007

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