Slow and Smooth: A Bayesian theory for the combination of local motion signals in human vision
Year: 1998
Type: article
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 dierent 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. 1 Introduction Estimating motion in scenes containing multiple, complex motions remains a dicult problem for computer vision systems, yet is performed eortlessly by human observers. Motion analysis in such scenes imposes conicting demands on the design of a vision system (Braddick, 1993)....
Authors: Yair Weiss, Edward H. Adelson
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W1519344860