Slow and Smooth: a Bayesian Theory for the Combination of Local Motion Signals in Human Vision
نویسنده
چکیده
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 diierent 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 nd 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.
منابع مشابه
Slow and Smooth: a Bayesian theory for the ombination of of lo al motion signals in human vision
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تاریخ انتشار 1998