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Erschienen in:
1989 | OriginalPaper | Buchkapitel
Introduction
verfasst von : Richard Szeliski
Erschienen in: Bayesian Modeling of Uncertainty in Low-Level Vision
Verlag: Springer US
Enthalten in: Professional Book Archive
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This book examines the application of Bayesian modeling to low-level vision. Bayesian modeling is a probabilistic estimation framework that consists of three separate models. The prior model describes the world or its properties which we are trying to estimate. The sensor model describes how any one instance of this world is related to the observations (such as images) which we make. The posterior model, which is obtained by combining the prior and sensor models using Bayes’ Rule, describes our current estimate of the world given the data which we have observed.