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...

Полное описание

Библиографические подробности
Главные авторы:	Kohli, P, Torr, PHS
Формат:	Journal article
Язык:	English
Опубликовано:	IEEE 2007

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