Slow and Smooth: A Bayesian Theory for the Combination of Local Motion Signals in Human Vision
dc.date.accessioned | 2004-10-20T21:04:17Z | |
dc.date.accessioned | 2018-11-24T10:23:32Z | |
dc.date.available | 2004-10-20T21:04:17Z | |
dc.date.available | 2018-11-24T10:23:32Z | |
dc.date.issued | 1998-02-01 | en_US |
dc.identifier.uri | http://hdl.handle.net/1721.1/7252 | |
dc.identifier.uri | http://repository.aust.edu.ng/xmlui/handle/1721.1/7252 | |
dc.description.abstract | 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. | en_US |
dc.format.extent | 7828604 bytes | |
dc.format.extent | 1388106 bytes | |
dc.language.iso | en_US | |
dc.title | Slow and Smooth: A Bayesian Theory for the Combination of Local Motion Signals in Human Vision | en_US |
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