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...

书目详细资料
Main Authors:	Kohli, P, Torr, PHS
格式:	Journal article
语言:	English
出版:	IEEE 2007

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