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

dc.date.accessioned2004-10-20T21:04:17Z
dc.date.accessioned2018-11-24T10:23:32Z
dc.date.available2004-10-20T21:04:17Z
dc.date.available2018-11-24T10:23:32Z
dc.date.issued1998-02-01en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/7252
dc.identifier.urihttp://repository.aust.edu.ng/xmlui/handle/1721.1/7252
dc.description.abstractIn 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.en_US
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dc.format.extent1388106 bytes
dc.language.isoen_US
dc.titleSlow and Smooth: A Bayesian Theory for the Combination of Local Motion Signals in Human Visionen_US


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