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| Research article summary (published 30 Mar 2003): |
Bayesian models of object perception.
Full Abstract
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.
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Author information
Author/s: Kersten, Daniel (D); Yuille, Alan (A);
Affiliation: Department of Psychology, University of Minnesota, 75 East River Road, Minneapolis, MN 55455, USA. kersten@umn.edu
Grants: EY02857 (Agency:United States NEI) ; EY12691 (Agency:United States NEI) ; R01 EY11507-001 (Agency:United States NEI)
Journal and publication information
Publication Type: Journal Article; Research Support, U.S. Gov't, Non-P.H.S.; Research Support, U.S. Gov't, P.H.S.; Review
Journal: Current opinion in neurobiology (Curr Opin Neurobiol), published in England. (Language: eng)
Reference: 2003-Apr; vol 13 (issue 2) : pp 150-8
Dates: Created 2003/05/14; Completed 2003/07/08; Revised 2007/11/14;
PMID: 12744967, status: MEDLINE (last retrieval date: 11/6/2008)
Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.
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