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Parallel and Deterministic Algorithms for MRFs: Surface Reconstruction and Integration

dc.date.accessioned2004-10-04T14:36:13Z
dc.date.accessioned2018-11-24T10:11:34Z
dc.date.available2004-10-04T14:36:13Z
dc.date.available2018-11-24T10:11:34Z
dc.date.issued1989-05-01en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/6025
dc.identifier.urihttp://repository.aust.edu.ng/xmlui/handle/1721.1/6025
dc.description.abstractIn 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.en_US
dc.format.extent37 p.en_US
dc.format.extent3090418 bytes
dc.format.extent2411062 bytes
dc.language.isoen_US
dc.subjectsurface reconstructionen_US
dc.subjectMarkov random fieldsen_US
dc.subjectmean fielden_US
dc.subjectsintegrationen_US
dc.subjectparameter estimationen_US
dc.subjectdeterministic algorithmsen_US
dc.titleParallel and Deterministic Algorithms for MRFs: Surface Reconstruction and Integrationen_US


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