Temporal Surface Reconstruction

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/6808

Full metadata record

DC Field	Value	Language
dc.creator	Heel, Joachim	-
dc.date	2004-10-20T19:57:38Z	-
dc.date	2004-10-20T19:57:38Z	-
dc.date	1991-05-01	-
dc.date.accessioned	2013-10-09T02:47:00Z	-
dc.date.available	2013-10-09T02:47:00Z	-
dc.date.issued	2013-10-09	-
dc.identifier	AITR-1296	-
dc.identifier	http://hdl.handle.net/1721.1/6808	-
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721	-
dc.description	This thesis investigates the problem of estimating the three-dimensional structure of a scene from a sequence of images. Structure information is recovered from images continuously using shading, motion or other visual mechanisms. A Kalman filter represents structure in a dense depth map. With each new image, the filter first updates the current depth map by a minimum variance estimate that best fits the new image data and the previous estimate. Then the structure estimate is predicted for the next time step by a transformation that accounts for relative camera motion. Experimental evaluation shows the significant improvement in quality and computation time that can be achieved using this technique.	-
dc.format	149 p.	-
dc.format	23730458 bytes	-
dc.format	8484961 bytes	-
dc.format	application/postscript	-
dc.format	application/pdf	-
dc.language	en_US	-
dc.relation	AITR-1296	-
dc.subject	3D reconstruction	-
dc.subject	Kalman Filter	-
dc.subject	temporal vision	-
dc.subject	structuresestimation	-
dc.subject	surface reconstruction	-
dc.title	Temporal Surface Reconstruction	-
Appears in Collections:	MIT Items

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