Contextual Priming for Object Detection

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/7239

Full metadata record

DC Field	Value	Language
dc.creator	Torralba, Antonio	-
dc.creator	Sinha, Pawan	-
dc.date	2004-10-20T21:03:49Z	-
dc.date	2004-10-20T21:03:49Z	-
dc.date	2001-09-01	-
dc.date.accessioned	2013-10-09T02:48:38Z	-
dc.date.available	2013-10-09T02:48:38Z	-
dc.date.issued	2013-10-09	-
dc.identifier	AIM-2001-020	-
dc.identifier	CBCL-205	-
dc.identifier	http://hdl.handle.net/1721.1/7239	-
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721	-
dc.description	There is general consensus that context can be a rich source of information about an object's identity, location and scale. In fact, the structure of many real-world scenes is governed by strong configurational rules akin to those that apply to a single object. Here we introduce a simple probabilistic framework for modeling the relationship between context and object properties based on the correlation between the statistics of low-level features across the entire scene and the objects that it contains. The resulting scheme serves as an effective procedure for object priming, context driven focus of attention and automatic scale-selection on real-world scenes.	-
dc.format	27 p.	-
dc.format	40187890 bytes	-
dc.format	5238575 bytes	-
dc.format	application/postscript	-
dc.format	application/pdf	-
dc.language	en_US	-
dc.relation	AIM-2001-020	-
dc.relation	CBCL-205	-
dc.subject	AI	-
dc.subject	context	-
dc.subject	image statistics	-
dc.subject	Bayesian reasoning	-
dc.subject	recognition	-
dc.subject	focus of attention	-
dc.title	Contextual Priming for Object Detection	-
Appears in Collections:	MIT Items

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