Bayesian models of object perception.

Daniel Kersten; Alan Yuille

doi:10.1016/s0959-4388(03)00042-4

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Bayesian models of object perception.

Daniel Kersten·Alan Yuille

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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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Animals Bayes Theorem Cues Humans Image Processing, Computer-Assisted Models, Biological Neural Networks, Computer Visual Pathways Visual Perception Journal Article Research Support, U.S. Gov't, Non-P.H.S.Research Support, U.S. Gov't, P.H.S.Review

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Daniel Kersten, Alan Yuille. 2003. Bayesian models of object perception.. https://doi.org/10.1016/s0959-4388(03)00042-4

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