Recognition and Localization of Overlapping Parts from Sparse Data

dc.creator	Grimson, W. Eric L.
dc.creator	Lozano-Perez, Tomas
dc.date	2004-10-01T20:10:47Z
dc.date	2004-10-01T20:10:47Z
dc.date	1985-06-01
dc.date.accessioned	2013-10-09T02:40:17Z
dc.date.available	2013-10-09T02:40:17Z
dc.date.issued	2013-10-09
dc.identifier	AIM-841
dc.identifier	http://hdl.handle.net/1721.1/5611
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721
dc.description	This paper discusses how sparse local measurements of positions and surface normals may be used to identify and locate overlapping objects. The objects are modeled as polyhedra (or polygons) having up to six degreed of positional freedom relative to the sensors. The approach operated by examining all hypotheses about pairings between sensed data and object surfaces and efficiently discarding inconsistent ones by using local constraints on: distances between faces, angles between face normals, and angles (relative to the surface normals) of vectors between sensed points. The method described here is an extension of a method for recognition and localization of non-overlapping parts previously described in [Grimson and Lozano-Perez 84] and [Gaston and Lozano-Perez 84].
dc.format	41 p.
dc.format	8294299 bytes
dc.format	6516094 bytes
dc.format	application/postscript
dc.format	application/pdf
dc.language	en_US
dc.relation	AIM-841
dc.subject	object recognition
dc.subject	sensor interpretations
dc.title	Recognition and Localization of Overlapping Parts from Sparse Data

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