Perception as Bayesian Inference [Paperback]

This 1996 book describes an exciting theoretical paradigm for visual perception based on experimental and computational insights.The book describes an exciting new paradigm for building and testing theories of human visual perception based on Bayesian probablity theory. Leading researchers in computer vision and experimental vision science describe theoretical frameworks for modeling vision, applications to specific problems, and implications for experimental studies of human perception. All chapters draw on insights from experimental and computational work. Commentaries by the contributors on each others' work provide a dialogue among the different perspectives.The book describes an exciting new paradigm for building and testing theories of human visual perception based on Bayesian probablity theory. Leading researchers in computer vision and experimental vision science describe theoretical frameworks for modeling vision, applications to specific problems, and implications for experimental studies of human perception. All chapters draw on insights from experimental and computational work. Commentaries by the contributors on each others' work provide a dialogue among the different perspectives.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. This 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 modeling 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 other's work. Students andlãJ