Perception as Bayesian InferenceDavid C. Knill, Whitman Richards Cambridge University Press, 13.09.1996 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. |
Inhalt
| 1 | |
Bayesian frameworks | 23 |
Commentaries | 213 |
Implications and applications | 237 |
Commentaries | 451 |
| 507 | |
| 513 | |
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algorithms analysis assume ball Barlow Bayes Bayesian approach Bayesian framework Bayesian inference Bülthoff cast shadow chapter coding competence observer compression Computer Vision configuration consider constraints context contours corresponding defined depth derived disparity domain domain theories elongation estimate example Figure geodesic geodesic curvature given homunculus human visual system hypothesis ideal observer image data Jepson kernel Kersten Knill light source likelihood function loss function measure modal modes noise orientation pattern theory perceived perception perceptual inference planar plane possible posterior distribution posterior probability prior assumptions prior distribution prior model prior probability probabilistic probability distribution problem psychophysical random reflectance function regularities reliable representation result scene interpretations scene parameters scene properties sensory shape from shading signal smooth space specific statistical stereo stereogram stereopsis structure surface normal surface shape task texel Theorem transparent variables velocity viewpoint visual perception visual system Yuille zero