Recognizing 3D Ojbects of 2D Images: An Error Analysis

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/5959

Full metadata record

DC Field	Value	Language
dc.creator	Grimson, W. Eric	-
dc.creator	Huttenlocher, Daniel P.	-
dc.creator	Alter, T. D.	-
dc.date	2004-10-04T14:24:10Z	-
dc.date	2004-10-04T14:24:10Z	-
dc.date	1992-07-01	-
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-1362	-
dc.identifier	http://hdl.handle.net/1721.1/5959	-
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721	-
dc.description	Many object recognition systems use a small number of pairings of data and model features to compute the 3D transformation from a model coordinate frame into the sensor coordinate system. With perfect image data, these systems work well. With uncertain image data, however, their performance is less clear. We examine the effects of 2D sensor uncertainty on the computation of 3D model transformations. We use this analysis to bound the uncertainty in the transformation parameters, and the uncertainty associated with transforming other model features into the image. We also examine the impact of the such transformation uncertainty on recognition methods.	-
dc.format	32 p.	-
dc.format	137449 bytes	-
dc.format	547073 bytes	-
dc.format	application/octet-stream	-
dc.format	application/pdf	-
dc.language	en_US	-
dc.relation	AIM-1362	-
dc.subject	object recognition	-
dc.subject	error analysis	-
dc.title	Recognizing 3D Ojbects of 2D Images: An Error Analysis	-
Appears in Collections:	MIT Items

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