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Perception as Bayesian Inference

Perception as Bayesian Inference

Perception as Bayesian Inference

Editors:
David C. Knill, University of Pennsylvania
Whitman Richards, Massachusetts Institute of Technology
E. H. Adelson, H. Barlow, P. N. Belhumeur, B. M. Bennett, A. Blake, H. H. Bulthoff, J. Feldman, W. T. Freeman, D. D. Hoffman, A. Jepson, D. Kersten, D. C. Knill, P. Mamassian, D. Mumford, K. Nakayama, A. P. Pentland, C. Prakash, W. Richards, S. N. Richman, D. Sheinberg, S. Shimojo, A. Yuille.
Published:
September 1996
Format:
Hardback
ISBN:
9780521461092

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    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.

    • Unusual degree of cross-referencing and dialogue between contributors of different disciplines
    • Integrated coverage

    Product details

    September 1996
    Hardback
    9780521461092
    530 pages
    254 × 178 × 29 mm
    1.13kg
    140 b/w illus.
    Available

    Table of Contents

    • 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.
      Contributors
    • E. H. Adelson, H. Barlow, P. N. Belhumeur, B. M. Bennett, A. Blake, H. H. Bulthoff, J. Feldman, W. T. Freeman, D. D. Hoffman, A. Jepson, D. Kersten, D. C. Knill, P. Mamassian, D. Mumford, K. Nakayama, A. P. Pentland, C. Prakash, W. Richards, S. N. Richman, D. Sheinberg, S. Shimojo, A. Yuille.

    • Editors
    • David C. Knill , University of Pennsylvania
    • Whitman Richards , Massachusetts Institute of Technology