Perception as Bayesian Inference

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David C. Knill, Whitman Richards
Cambridge University Press, Sep 13, 1996 - Computers - 516 pages
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In recent years, Bayesian probability theory has emerged not only as a powerful tool for building computational theories of vision, but also as a general paradigm for studying human visual perception. The Bayesian approach provides new and powerful metaphors for conceptualizing visual perception, suggests novel questions to ask about perceptual processing, and provides the means to formalize theories of perception that make testable predictions about human perceptual performance. This book provides an introduction to and critical analysis of the Bayesian paradigm. Chapters by leading researchers in computational theory and experimental visual science introduce new theoretical frameworks for building perceptual theories, discuss the implications of the Bayesian paradigm for psychophysical studies of human perception, and describe specific applications of the approach. The editors have created a critical dialogue of ideas through the authors' commentaries on each others' chapters, conveying to the reader a unique appreciation for the issues and ideas raised in the book.
 

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10/29/2007 Found in Reiter's.

Contents

Introduction D C Knill D Kersten A Yuille
12
A unifying perspective D Mumford
25
Modal structure and reliable inference A Jepson W Richards
63
Priors preferences and categorical percepts W Richards A Jepson
93
Bayesian decision theory and psychophysics A L Yuille
123
Observer theory Bayes theory and psychophysics B M Bennett
164
Commentaries
213
Implications of a Bayesian formulation of visual information
239
Ideal observers and human psychophysics
306
A computational theory for binocular stereopsis P N Belhumeur
323
The generic viewpoint assumption in a Bayesian framework
365
The perception of shading and reflectance E H Adelson
409
Banishing the homunculus H Barlow
425
Commentaries
451
Author index
507
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