Perception as Bayesian Inference (Paperback)

David C. Knill

ISBN 10: 0521064996 / ISBN 13: 9780521064996

Verlag: Cambridge University Press, Cambridge, 2008

Beschreibung

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 multiple locations in the US or from the UK, depending on stock availability.

Bestandsnummer des Verkäufers 9780521064996

Bibliografische Details

• Titel: Perception as Bayesian Inference (Paperback)
• Verlag: Cambridge University Press, Cambridge
• Erscheinungsjahr: 2008
• Sprache: Englisch
• ISBN-10: 0521064996
• ISBN-13: 9780521064996
• Einband: Paperback
• Auflage: 1. Auflage
• Zustand: new

Über diesen Titel

Inhaltsangabe

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

Von der hinteren Coverseite

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. 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.