Perception as Bayesian Inference

by Edited by David C. Knill , Whitman Richards

• ISBN13: 9780521064996
• ISBN10: 0521064996
• Edition: 1st
• Format: Paperback
• Copyright: 2008-06-12
• Publisher: Cambridge University Press

Summary

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 modelling 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 others' work. Students and researchers in cognitive and visual science will find much to interest them in this thought-provoking collection.

Table of Contents

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

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