Parallel and Deterministic Algorithms for MRFs: Surface Reconstruction and Integration

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/6025

Full metadata record

DC Field	Value	Language
dc.creator	Geiger, Davi	-
dc.creator	Girosi, Federico	-
dc.date	2004-10-04T14:36:13Z	-
dc.date	2004-10-04T14:36:13Z	-
dc.date	1989-05-01	-
dc.date.accessioned	2013-10-09T02:42:26Z	-
dc.date.available	2013-10-09T02:42:26Z	-
dc.date.issued	2013-10-09	-
dc.identifier	AIM-1114	-
dc.identifier	http://hdl.handle.net/1721.1/6025	-
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721	-
dc.description	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.	-
dc.format	37 p.	-
dc.format	3090418 bytes	-
dc.format	2411062 bytes	-
dc.format	application/postscript	-
dc.format	application/pdf	-
dc.language	en_US	-
dc.relation	AIM-1114	-
dc.subject	surface reconstruction	-
dc.subject	Markov random fields	-
dc.subject	mean field	-
dc.subject	sintegration	-
dc.subject	parameter estimation	-
dc.subject	deterministic algorithms	-
dc.title	Parallel and Deterministic Algorithms for MRFs: Surface Reconstruction and Integration	-
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