Learning object boundary detection from motion data

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/3686

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DC Field	Value	Language
dc.creator	Ross, Michael G.	-
dc.creator	Kaelbling, Leslie P.	-
dc.date	2003-11-16T19:34:34Z	-
dc.date	2003-11-16T19:34:34Z	-
dc.date	2003-01	-
dc.date.accessioned	2013-10-09T02:31:56Z	-
dc.date.available	2013-10-09T02:31:56Z	-
dc.date.issued	2013-10-09	-
dc.identifier	http://hdl.handle.net/1721.1/3686	-
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721	-
dc.description	A significant barrier to applying the techniques of machine learning to the domain of object boundary detection is the need to obtain a large database of correctly labeled examples. Inspired by developmental psychology, this paper proposes that boundary detection can be learned from the output of a motion tracking algorithm that separates moving objects from their static surroundings. Motion segmentation solves the database problem by providing cheap, unlimited, labeled training data. A probabilistic model of the textural and shape properties of object boundaries can be trained from this data and then used to efficiently detect boundaries in novel images via loopy belief propagation.	-
dc.description	Singapore-MIT Alliance (SMA)	-
dc.format	1220156 bytes	-
dc.format	application/pdf	-
dc.language	en_US	-
dc.relation	Computer Science (CS);	-
dc.subject	machine learning	-
dc.subject	boundary detection	-
dc.subject	motion segmentation	-
dc.subject	loopy belief propagation	-
dc.subject	motion tracking algorithm	-
dc.title	Learning object boundary detection from motion data	-
dc.type	Article	-
Appears in Collections:	MIT Items

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