Region-Based Feature Interpretation for Recognizing 3D Models in 2D Images

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/7039

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DC Field	Value	Language
dc.creator	Clemens, David T.	-
dc.date	2004-10-20T20:23:24Z	-
dc.date	2004-10-20T20:23:24Z	-
dc.date	1991-06-01	-
dc.date.accessioned	2013-10-09T02:48:05Z	-
dc.date.available	2013-10-09T02:48:05Z	-
dc.date.issued	2013-10-09	-
dc.identifier	AITR-1307	-
dc.identifier	http://hdl.handle.net/1721.1/7039	-
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721	-
dc.description	In model-based vision, there are a huge number of possible ways to match model features to image features. In addition to model shape constraints, there are important match-independent constraints that can efficiently reduce the search without the combinatorics of matching. I demonstrate two specific modules in the context of a complete recognition system, Reggie. The first is a region-based grouping mechanism to find groups of image features that are likely to come from a single object. The second is an interpretive matching scheme to make explicit hypotheses about occlusion and instabilities in the image features.	-
dc.format	22413823 bytes	-
dc.format	8247283 bytes	-
dc.format	application/postscript	-
dc.format	application/pdf	-
dc.language	en_US	-
dc.relation	AITR-1307	-
dc.title	Region-Based Feature Interpretation for Recognizing 3D Models in 2D Images	-
Appears in Collections:	MIT Items

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