Statistical Object Recognition

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/7046

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DC Field	Value	Language
dc.creator	Wells, William M. III	-
dc.date	2004-10-20T20:23:39Z	-
dc.date	2004-10-20T20:23:39Z	-
dc.date	1993-01-01	-
dc.date.accessioned	2013-10-09T02:48:06Z	-
dc.date.available	2013-10-09T02:48:06Z	-
dc.date.issued	2013-10-09	-
dc.identifier	AITR-1398	-
dc.identifier	http://hdl.handle.net/1721.1/7046	-
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721	-
dc.description	Two formulations of model-based object recognition are described. MAP Model Matching evaluates joint hypotheses of match and pose, while Posterior Marginal Pose Estimation evaluates the pose only. Local search in pose space is carried out with the Expectation--Maximization (EM) algorithm. Recognition experiments are described where the EM algorithm is used to refine and evaluate pose hypotheses in 2D and 3D. Initial hypotheses for the 2D experiments were generated by a simple indexing method: Angle Pair Indexing. The Linear Combination of Views method of Ullman and Basri is employed as the projection model in the 3D experiments.	-
dc.format	11809727 bytes	-
dc.format	6702525 bytes	-
dc.format	application/postscript	-
dc.format	application/pdf	-
dc.language	en_US	-
dc.relation	AITR-1398	-
dc.title	Statistical Object Recognition	-
Appears in Collections:	MIT Items

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