Slow and Smooth: A Bayesian theory for the combination of local motion signals in human vision
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 di erent 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. Copyright c Massachusetts Institute of Technology, 1998 This report describes research done at the Center for Biological and Computational Learning and the Department of Brain and Cognitive Sciences of the Massachusetts Institute of Technology. Support for the Center is provided in part by a grant from the National Science Foundation under contract ASC{9217041. The work was also supported by NEI R01 EY11005 to E. H. Adelson