Temporal Surface Reconstruction

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

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