Slow and Smooth: A Bayesian Theory for the Combination of Local Motion Signals in Human Vision

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/7252

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DC Field	Value	Language
dc.creator	Weiss, Yar	-
dc.creator	Adelson, Edward H.	-
dc.date	2004-10-20T21:04:17Z	-
dc.date	2004-10-20T21:04:17Z	-
dc.date	1998-02-01	-
dc.date.accessioned	2013-10-09T02:48:48Z	-
dc.date.available	2013-10-09T02:48:48Z	-
dc.date.issued	2013-10-09	-
dc.identifier	AIM-1624	-
dc.identifier	CBCL-158	-
dc.identifier	http://hdl.handle.net/1721.1/7252	-
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721	-
dc.description	In order to estimate the motion of an object, the visual system needs to combine multiple local measurements, each of which carries some degree of ambiguity. We present a model of motion perception whereby measurements from different image regions are combined according to a Bayesian estimator --- the estimated motion maximizes the posterior probability assuming a prior favoring slow and smooth velocities. In reviewing a large number of previously published phenomena we find that the Bayesian estimator predicts a wide range of psychophysical results. This suggests that the seemingly complex set of illusions arise from a single computational strategy that is optimal under reasonable assumptions.	-
dc.format	7828604 bytes	-
dc.format	1388106 bytes	-
dc.format	application/postscript	-
dc.format	application/pdf	-
dc.language	en_US	-
dc.relation	AIM-1624	-
dc.relation	CBCL-158	-
dc.title	Slow and Smooth: A Bayesian Theory for the Combination of Local Motion Signals in Human Vision	-
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