Perception as Bayesian Inference

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

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Paperback | 12 June 2008

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532 Pages

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25 x 17.5 x 3.5

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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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Hardcover

Published: 7th December 1996

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Introduction
Pattern theory: a unifying perspective
Modal structure and reliable inference
Priors, preferences and categorical percepts
Bayesian decision theory and psychophysics
Observer theory, Bayes theory, and psychophysics
Implications of a Bayesian formulation
Shape from texture: ideal observers and human psychophysics
A computational theory for binocular stereopsis
The generic viewpoint assumption in a Bayesian framework
Experiencing and perceiving visual surfaces
The perception of shading and reflectance
Banishing the Homunculus
Table of Contents provided by Publisher. All Rights Reserved.

Perception as Bayesian Inference

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