Efficient Parallel Estimation for Markov Random Fields

We present a new, deterministic, distributed MAP estimation algorithm for Markov Random Fields called Local Highest Confidence First (Local HCF). The algorithm has been applied to segmentation problems in computer vision and its performance compared with stochastic algorithms. The experiments show t...

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Bibliographic Details
Published inarXiv.org
Main Authors Swain, Michael J, Wixson, Lambert E, Chou, Paul B
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 27.03.2013
Subjects
Algorithms
Computer vision
Fields (mathematics)
Markov analysis
Markov processes
Segmentation
Stochastic processes
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Summary:We present a new, deterministic, distributed MAP estimation algorithm for Markov Random Fields called Local Highest Confidence First (Local HCF). The algorithm has been applied to segmentation problems in computer vision and its performance compared with stochastic algorithms. The experiments show that Local HCF finds better estimates than stochastic algorithms with much less computation.
ISSN:2331-8422