Perception as Bayesian Inference (Paperback)

David C. Knill

ISBN 10: 0521064996 / ISBN 13: 9780521064996

Edité par Cambridge University Press, Cambridge, 2008

A propos de cet article

Paperback. 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. This 1996 book describes an exciting paradigm for building and testing theories of human visual perception based on Bayesian probability theory. Leading researchers in computer vision and experimental vision science describe theoretical frameworks, applications to specific problems, and implications for experimental studies. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. N° de réf. du vendeur 9780521064996

Détails bibliographiques

Titre : Perception as Bayesian Inference (Paperback)

Éditeur : Cambridge University Press, Cambridge

Date d'édition : 2008

Reliure : Paperback

Etat : new

Edition : Edition originale

A propos de ce titre

Synopsis :

This 1996 book describes an exciting theoretical paradigm for visual perception based on experimental and computational insights.

Présentation de l'éditeur:

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.