Perception as Bayesian Inference

David C. Knill

ISBN 10: 052146109X / ISBN 13: 9780521461092

Editore: Cambridge University Press, 1996

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Druck auf Anfrage Neuware - Printed after ordering - 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.

Codice articolo 9780521461092

Dati bibliografici

• Titolo: Perception as Bayesian Inference
• Editore: Cambridge University Press
• Data di pubblicazione: 1996
• Lingua: Inglese
• ISBN 10: 052146109X
• ISBN 13: 9780521461092
• Rilegatura: Buch
• Condizione: Neu
• Dimensioni: 260 L x 183 A x 33 P (millimetri) 260 millimetri di larghezza per 183 millimetri di altezza per 33 millimetri di profondità
• Peso: 1181 grammi

Descrizione articolo

Riassunto

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

Descrizione del libro

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.