Perception as Bayesian inference

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 1996 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.

Title from publisher's bibliographic system (viewed on 05 Oct 2015).

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.