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. This book provides an introduction to and critical analysis of the Bayesian paradigm. Leading researchers in computer vision and experimental vision science describe general theoretical frameworks for modeling vision, detailed applications to specific problems and implications for experimental studies of human perception. The book provides a dialogue between different perspectives both within chapters, which draw on insights from experimental and computational work, and between chapters, through commentaries written by the contributors on each other's work. Students and researchers in cognitive and visual science will find much to interest them in this thought-provoking collection.
  

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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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