Perception as Bayesian Inference (Paperback)

David C. Knill

ISBN 10: 0521064996 / ISBN 13: 9780521064996

Editore: Cambridge University Press, Cambridge, 2008

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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 UK warehouse or from our Australian or US warehouses, depending on stock availability.

Codice articolo 9780521064996

Dati bibliografici

• Titolo: Perception as Bayesian Inference (Paperback)
• Editore: Cambridge University Press, Cambridge
• Data di pubblicazione: 2008
• Lingua: Inglese
• ISBN 10: 0521064996
• ISBN 13: 9780521064996
• Rilegatura: Paperback
• Edizione: prima edizione
• Condizione: new

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.