Bayesian models of object perception.
The human visual system is the most complex pattern recognition device known. In ways that are yet to be fully understood, the visual cortex arrives at a simple and unambiguous interpretation of data from the retinal image that is useful for the decisions and actions of everyday life. Recent advances in Bayesian models of computer vision and in the measurement and modeling of natural image statistics are providing the tools to test and constrain theories of human object perception. In turn, these theories are having an impact on the interpretation of cortical function.
Department of Psychology, University of Minnesota, 75 East River Road, Minneapolis, MN 55455, USA. kersten@umn.edu
Daniel
D
Kersten
Alan
A
Yuille
D000818:Animals; D001499:Bayes Theorem; D003463:Cues; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008954:Models, Biological; D016571:Neural Networks, Computer; D014795:Visual Pathways / Q000502:physiology; D014796:Visual Perception / Q000502:physiology*