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" Perception as Bayesian inference / "
edited by David C. Knill, Whitman Richards.
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BL
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| Record Number
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630950
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dltt
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| Title & Author
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Perception as Bayesian inference /\ edited by David C. Knill, Whitman Richards.
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| Publication Statement
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Cambridge, U.K. ;New York :: Cambridge University Press,, 1996.
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| Page. NO
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xi, 516 p. :: ill. ;; 27 cm.
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| ISBN
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052146109X (hardcover)
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: 9780521461092 (hardcover)
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| Notes
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Papers originally presented at a meeting held at Chatham Bars Inn, Chatham, Mass., in Jan. 1993.
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| Bibliographies/Indexes
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Includes bibliographical references and indexes.
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| Contents
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Introduction / D.C. Knill, D. Kersten and A. Yuille -- 1. Pattern theory: A unifying perspective / D. Mumford -- 2. Modal structure and reliable inference / A. Jepson, W. Richards and D.C. Knill -- 3. Priors, preferences and categorical percepts / W. Richards, A. Jepson and J. Feldman -- 4. Bayesian decision theory and psychophysics / A.L. Yuille and H.H. Bulthoff -- 5. Observer theory, Bayes theory, and psychophysics / B.M. Bennett, D.D. Hoffman, C. Prakash and S.N. Richman -- 6. Implications of a Bayesian formulation of visual information for processing for psychophysics / D.C. Knill, D. Kersten and P. Mamassian -- 7. Shape from texture: Ideal observers and human psychophysics / A. Blake, H.H. Bulthoff and D. Sheinberg -- 8. A computational theory for binocular stereopsis / P.N. Belhumeur -- 9. The generic viewpoint assumption in a Bayesian framework / W.T. Freeman -- 10. Experiencing and perceiving visual surfaces / K. Nakayama and S. Shimojo.
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| Abstract
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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. The Bayesian approach provides new and powerful metaphors for conceptualizing visual perception, suggests novel questions to ask about perceptual processing, and provides the means to formalize theories of perception that make testable predictions about human perceptual performance.
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This book provides an introduction to and critical analysis of the Bayesian paradigm. Chapters by leading researchers in computational theory and experimental visual science introduce new theoretical frameworks for building perceptual theories, discuss the implications of the Bayesian paradigm for psychophysical studies of human perception, and describe specific applications of the approach. The editors have created a critical dialogue of ideas through the authors' commentaries on each others' chapters, conveying to the reader a unique appreciation for the issues and ideas raised in the book.
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| Subject
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Perception, Congresses.
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Bayesian statistical decision theory, Congresses.
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Perception.
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Bayes Theorem.
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| LC Classification
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BF311.P3534 1996
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| Added Entry
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Knill, David C.
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Richards, Whitman.
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