Robust and Efficient 3D Recognition by Alignment

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/6799

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DC Field	Value	Language
dc.creator	Alter, Tao Daniel	-
dc.date	2004-10-20T19:55:26Z	-
dc.date	2004-10-20T19:55:26Z	-
dc.date	1992-09-01	-
dc.date.accessioned	2013-10-09T02:46:58Z	-
dc.date.available	2013-10-09T02:46:58Z	-
dc.date.issued	2013-10-09	-
dc.identifier	AITR-1410	-
dc.identifier	http://hdl.handle.net/1721.1/6799	-
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721	-
dc.description	Alignment is a prevalent approach for recognizing 3D objects in 2D images. A major problem with current implementations is how to robustly handle errors that propagate from uncertainties in the locations of image features. This thesis gives a technique for bounding these errors. The technique makes use of a new solution to the problem of recovering 3D pose from three matching point pairs under weak-perspective projection. Furthermore, the error bounds are used to demonstrate that using line segments for features instead of points significantly reduces the false positive rate, to the extent that alignment can remain reliable even in cluttered scenes.	-
dc.format	113 p.	-
dc.format	903052 bytes	-
dc.format	1830006 bytes	-
dc.format	application/octet-stream	-
dc.format	application/pdf	-
dc.language	en_US	-
dc.relation	AITR-1410	-
dc.subject	computer vision	-
dc.subject	object recognition	-
dc.subject	error models	-
dc.subject	salignment	-
dc.subject	weak perspective	-
dc.subject	pose estimation	-
dc.title	Robust and Efficient 3D Recognition by Alignment	-
Appears in Collections:	MIT Items

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