Hi,
The following paper describing a Bayesian model for human motion perception is
now available online via:
http://www-bcs.mit.edu/~yweiss/weiss.html#slowSmooth
This paper forms part of my dissertation that is also downloadable from
http://www-bcs.mit.edu/~yweiss/thesis.html
Comments are most welcome.
Yair
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Title: Slow and Smooth: a Bayesian theory for the combination of
local motion signals in human vision
Author: Yair Weiss and Edward H. Adelson
Reference: MIT AI Memo 1624, MIT CBCL Paper 158.
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.