Perception as Bayesian Inference

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David C. Knill (University of Pennsylvania) Whitman Richards (Massachusetts Institute of Technology)

Perception as Bayesian Inference

David C. Knill (University of Pennsylvania), Whitman Richards (Massachusetts Institute of Technology)

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English

Cambridge University Press
07 December 1996

Physiological & neuro-psychology, biopsychology; Perception; Neurosciences; Computer vision

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.

Edited by: David C. Knill (University of Pennsylvania), Whitman Richards (Massachusetts Institute of Technology)
Imprint: Cambridge University Press
Country of Publication: United Kingdom
Dimensions: Height: 254mm, Width: 178mm, Spine: 29mm
Weight: 1.130kg
ISBN: 9780521461092
ISBN 10: 052146109X
Pages: 530
Publication Date: 07 December 1996
Audience: Professional and scholarly , Undergraduate
Replaced By: 9780521064996
Format: Hardback
Publisher's Status: Active

1. Introduction D. C. Knill, D. Kersten and A. Yuille; 2. Pattern theory: a unifying perspective D. Mumford; 3. Modal structure and reliable inference A. Jepson, W. Richards and D. C. Knill; 4. Priors, preferences and categorical percepts W. Richards, A. Jepson and J. Feldman; 5. Bayesian decision theory and psychophysics A. L. Yuille and H. H. Bulthoff; 6. Observer theory, Bayes theory, and psychophysics B. M. Bennett, D. D. Hoffman, C. Prakash and S. N. Richman; 7. Implications of a Bayesian formulation D. C. Knill, D. Kersten and P. Mamassian; 8. Shape from texture: ideal observers and human psychophysics A. Blake, H. H. Bulthoff and D. Sheinberg; 9. A computational theory for binocular stereopsis P. N. Belhumeur; 10. The generic viewpoint assumption in a Bayesian framework W. T. Freeman; 11. Experiencing and perceiving visual surfaces K. Nakayama and S. Shimojo; 12. The perception of shading and reflectance E. H. Adelson and A. P. Pentland; 13. Banishing the Homunculus H. Barlow.