Slow and Smooth: A Bayesian theory for the combination of local motion signals in human vision

Authors:
Yair Weiss;Edward H. Adelson
Affiliations:
-;-
Venue:
Slow and Smooth: A Bayesian theory for the combination of local motion signals in human vision
Year:
1998

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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.