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Parallel and deterministic algorithms for MRFs: surface reconstruction and integration.

Author: D Geiger; F Girosi; Massachusetts Institute of Technology. Artificial Intelligence Laboratory.; Whitaker College. Center for Biological Information Processing.
Publisher: 1989.
Edition/Format:   Book : English
Database:WorldCat
Summary:
In recent years many researchers have investigated the use of Markov random fields (MRFs) for computer vision. The computational complexity of the implementation has been a drawback of MRFs. In this paper we derive deterministic approximations to MRFs models. All the theoretical results are obtained in the framework of the mean field theory from statistical mechanics. Because we use MRFs models the mean field  Read more...
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Document Type: Book
All Authors / Contributors: D Geiger; F Girosi; Massachusetts Institute of Technology. Artificial Intelligence Laboratory.; Whitaker College. Center for Biological Information Processing.
OCLC Number: 123325866

Abstract:

In recent years many researchers have investigated the use of Markov random fields (MRFs) for computer vision. The computational complexity of the implementation has been a drawback of MRFs. In this paper we derive deterministic approximations to MRFs models. All the theoretical results are obtained in the framework of the mean field theory from statistical mechanics. Because we use MRFs models the mean field equations lead to parallel and iterative algorithms. One of the considered models for image reconstruction is shown to give in a natural way the graduate non-convexity algorithm proposed by Blake and Zisserman.

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