Learning Three-Dimensional Shape Models for Sketch Recognition

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/7424

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DC Field	Value	Language
dc.creator	Kaelbling, Leslie P.	-
dc.creator	Lozano-Pérez, Tomás	-
dc.date	2004-12-13T06:56:19Z	-
dc.date	2004-12-13T06:56:19Z	-
dc.date	2005-01	-
dc.date.accessioned	2013-10-09T02:49:28Z	-
dc.date.available	2013-10-09T02:49:28Z	-
dc.date.issued	2013-10-09	-
dc.identifier	http://hdl.handle.net/1721.1/7424	-
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721	-
dc.description	Artifacts made by humans, such as items of furniture and houses, exhibit an enormous amount of variability in shape. In this paper, we concentrate on models of the shapes of objects that are made up of fixed collections of sub-parts whose dimensions and spatial arrangement exhibit variation. Our goals are: to learn these models from data and to use them for recognition. Our emphasis is on learning and recognition from three-dimensional data, to test the basic shape-modeling methodology. In this paper we also demonstrate how to use models learned in three dimensions for recognition of two-dimensional sketches of objects.	-
dc.description	Singapore-MIT Alliance (SMA)	-
dc.format	408469 bytes	-
dc.format	application/pdf	-
dc.language	en	-
dc.relation	Computer Science (CS);	-
dc.subject	sketch recognition	-
dc.subject	object recognition	-
dc.subject	computer vision	-
dc.title	Learning Three-Dimensional Shape Models for Sketch Recognition	-
dc.type	Article	-
Appears in Collections:	MIT Items

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