What Makes a Good Feature?

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/5963

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DC Field	Value	Language
dc.creator	Richards, W.	-
dc.creator	Jepson, A.	-
dc.date	2004-10-04T14:24:15Z	-
dc.date	2004-10-04T14:24:15Z	-
dc.date	1992-04-01	-
dc.date.accessioned	2013-10-09T02:42:07Z	-
dc.date.available	2013-10-09T02:42:07Z	-
dc.date.issued	2013-10-09	-
dc.identifier	AIM-1356	-
dc.identifier	http://hdl.handle.net/1721.1/5963	-
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721	-
dc.description	Using a Bayesian framework, we place bounds on just what features are worth computing if inferences about the world properties are to be made from image data. Previously others have proposed that useful features reflect "non-accidental'' or "suspicious'' configurations (such as parallel or colinear lines). We make these notions more precise and show them to be context sensitive.	-
dc.format	42 p.	-
dc.format	2433280 bytes	-
dc.format	1910701 bytes	-
dc.format	application/postscript	-
dc.format	application/pdf	-
dc.language	en_US	-
dc.relation	AIM-1356	-
dc.subject	computational vision	-
dc.subject	vision features	-
dc.subject	Bayesian model	-
dc.subject	svision psychophysics	-
dc.subject	color	-
dc.subject	motion	-
dc.title	What Makes a Good Feature?	-
Appears in Collections:	MIT Items

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