On the Sensitivity of the Hough Transform for Object Recognition

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/6039

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DC Field	Value	Language
dc.creator	Grimson, W. Eric L.	-
dc.creator	Huttenlocher, David	-
dc.date	2004-10-04T14:36:40Z	-
dc.date	2004-10-04T14:36:40Z	-
dc.date	1988-05-01	-
dc.date.accessioned	2013-10-09T02:42:30Z	-
dc.date.available	2013-10-09T02:42:30Z	-
dc.date.issued	2013-10-09	-
dc.identifier	AIM-1044	-
dc.identifier	http://hdl.handle.net/1721.1/6039	-
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721	-
dc.description	A common method for finding an object's pose is the generalized Hough transform, which accumulates evidence for possible coordinate transformations in a parameter space and takes large clusters of similar transformations as evidence of a correct solution. We analyze this approach by deriving theoretical bounds on the set of transformations consistent with each data-model feature pairing, and by deriving bounds on the likelihood of false peaks in the parameter space, as a function of noise, occlusion, and tessellation effects. We argue that blithely applying such methods to complex recognition tasks is a risky proposition, as the probability of false positives can be very high.	-
dc.format	40 p.	-
dc.format	5359682 bytes	-
dc.format	2031093 bytes	-
dc.format	application/postscript	-
dc.format	application/pdf	-
dc.language	en_US	-
dc.relation	AIM-1044	-
dc.subject	Hough transform	-
dc.subject	object recognition	-
dc.title	On the Sensitivity of the Hough Transform for Object Recognition	-
Appears in Collections:	MIT Items

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