Perception as Bayesian Inference

By: David C. Knill (Editor), Whitman Richards (Editor)

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Hardcover | 7 December 1996

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Hardcover
530 Pages

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25.4 x 17.78 x 2.87

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Published: 12th June 2008

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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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Non-Fiction Computing & I.T.Computer Programming & Software Development Programming & Scripting Languages Computer Science Artificial Intelligence Computer Vision Science Biology, Life Sciences Life Sciences in General Neurosciences Psychology

Physiological & Neuro-Psychology, Biopsychology Cognition & Cognitive Psychology Perception Information Technology General Issue Ethical & Social Aspects of IT

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Paperback

Published: 12th June 2008

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List of contributors
Preface
Introduction	p. 1
Pattern theory: A unifying perspective	p. 25
Modal structure and reliable inference	p. 63
Priors, preferences and categorical percepts	p. 93
Bayesian decision theory and psychophysics	p. 123
Observer theory, Bayes theory, and psychophysics	p. 163
Implications of a Bayesian formulation of visual information for processing for psychophysics	p. 239
Shape from texture: Ideal observers and human psychophysics	p. 287
A computational theory for binocular stereopsis	p. 323
The generic viewpoint assumption in a Bayesian framework	p. 365
Experiencing and perceiving visual surfaces	p. 391
The perception of shading and reflectance	p. 409
Banishing the homunculus	p. 425
Author index	p. 507
Subject index	p. 513
Table of Contents provided by Blackwell. All Rights Reserved.

Perception as Bayesian Inference

At a Glance

New Edition

Hardcover

Shipping

How to return your order

Defective items

You Can Find This Book In

Other Editions and Formats

Paperback

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