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http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/7252| Title: | Slow and Smooth: A Bayesian Theory for the Combination of Local Motion Signals in Human Vision |
| Issue Date: | 9-Oct-2013 |
| 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. |
| URI: | http://koha.mediu.edu.my:8181/xmlui/handle/1721 |
| Other Identifiers: | AIM-1624 CBCL-158 http://hdl.handle.net/1721.1/7252 |
| Appears in Collections: | MIT Items |
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