Title: Perception as Bayesian inference /
Authors: Knill, David C.; Richards, Whitman.
Publisher: Cambridge University Press,
Year: 1996
Subjects: Bayes Theorem; Perception; Perception; Bayesian statistical decision theory
Abstract: 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.
Contents: 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.