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Bayesian models and Markov Random Fields

  • Richard Szeliski
Part of the The Kluwer International in Engineering and Computer Science book series (SECS, volume 79)

Abstract

In the early days of computer vision, Bayesian modeling was a popular technique for formulating estimation and pattern classification problems (Duda and Hart 1973). This probabilistic approach fell into disuse, however, as computer vision shifted its attention to the understanding of the physics of image formation and the solution of inverse problems. Bayesian modeling has had a recent resurgence, due in part to the increased sophistication available from Markov Random Field models, and due to a realization of the importance of sensor and error modeling. In this chapter, we will briefly review the general Bayesian modeling framework. This will be followed by an introduction to Markov Random Fields and their implementation. We will then discuss the utility of probabilistic models in later stages of vision and preview the use of Bayesian modeling in the remainder of the book.

Keywords

Posterior Distribution Bayesian Modeling Prior Model Sensor Model High Order Statistic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Kluwer Academic Publishers 1989

Authors and Affiliations

  • Richard Szeliski
    • 1
  1. 1.Carnegie Mellon UniversityUSA

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