Probabilistic Solution of Ill-Posed Problems in Computational Vision

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/6449

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DC Field	Value	Language
dc.creator	Marroquin, J.	-
dc.creator	Mitter, S.	-
dc.creator	Poggio, T.	-
dc.date	2004-10-04T14:56:53Z	-
dc.date	2004-10-04T14:56:53Z	-
dc.date	1987-03-01	-
dc.date.accessioned	2013-10-09T02:45:28Z	-
dc.date.available	2013-10-09T02:45:28Z	-
dc.date.issued	2013-10-09	-
dc.identifier	AIM-897	-
dc.identifier	http://hdl.handle.net/1721.1/6449	-
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721	-
dc.description	We formulate several problems in early vision as inverse problems. Among the solution methods we review standard regularization theory, discuss its limitations, and present new stochastic (in particular, Bayesian) techniques based on Markov Random Field models for their solution. We derive efficient algorithms and describe parallel implementations on digital parallel SIMD architectures, as well as a new class of parallel hybrid computers that mix digital with analog components.	-
dc.format	5330897 bytes	-
dc.format	2064608 bytes	-
dc.format	application/postscript	-
dc.format	application/pdf	-
dc.language	en_US	-
dc.relation	AIM-897	-
dc.title	Probabilistic Solution of Ill-Posed Problems in Computational Vision	-
Appears in Collections:	MIT Items

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