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Abstract

As we have seen in the previous chapter, prior models play an essential role in the formulation of Bayesian estimators. A prior model can be as simple as the prior probabilities of different terrain types used in our remote sensing example of Section 3.1, or as complicated as the initial state (position, orientation and velocity) estimate of a satellite in a Kaiman filter on-line estimation system. When applied to low-level vision, prior models encode the smoothness or coherence of the two-dimensional fields that are being estimated from the image. In this chapter, we will examine the spectral characteristics of our prior models, develop algorithms for efficiently generating random samples, develop a relative representation using a frequency domain approach, and compare our probabilistic models to deterministic (mechanical) models. Let us start by previewing how these four ideas fit together.

Keywords

Thin Plate Bayesian Modeling Gibbs Sampler Finite Impulse Response Fine Level 
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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