Document Detail


Bayesian models of object perception.
MedLine Citation:
PMID:  12744967     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
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.
Authors:
Daniel Kersten; Alan Yuille
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Publication Detail:
Type:  Journal Article; Research Support, U.S. Gov't, Non-P.H.S.; Research Support, U.S. Gov't, P.H.S.; Review    
Journal Detail:
Title:  Current opinion in neurobiology     Volume:  13     ISSN:  0959-4388     ISO Abbreviation:  Curr. Opin. Neurobiol.     Publication Date:  2003 Apr 
Date Detail:
Created Date:  2003-05-14     Completed Date:  2003-07-08     Revised Date:  2007-11-14    
Medline Journal Info:
Nlm Unique ID:  9111376     Medline TA:  Curr Opin Neurobiol     Country:  England    
Other Details:
Languages:  eng     Pagination:  150-8     Citation Subset:  IM    
Affiliation:
Department of Psychology, University of Minnesota, 75 East River Road, Minneapolis, MN 55455, USA. kersten@umn.edu
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MeSH Terms
Descriptor/Qualifier:
Animals
Bayes Theorem*
Cues
Humans
Image Processing, Computer-Assisted / methods*
Models, Biological*
Neural Networks (Computer)*
Visual Pathways / physiology
Visual Perception / physiology*
Grant Support
ID/Acronym/Agency:
EY02857/EY/NEI NIH HHS; EY12691/EY/NEI NIH HHS; R01 EY11507-001/EY/NEI NIH HHS

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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