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...

書誌詳細
主要な著者:	Kohli, P, Torr, PHS
フォーマット:	Journal article
言語:	English
出版事項:	IEEE 2007

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