Information Processing Using Energy Function Minimization

  • James J. Clark
  • Alan L. Yuille
Chapter
Part of the The Springer International Series in Engineering and Computer Science book series (SECS, volume 105)

Abstract

A good deal of recent research into sensory information processing algorithms has focussed on the so-called energy function minimization, or regularization, approach to inverting the world-image mapping. In this chapter we review the application of this paradigm to early vision and its incorporation within the Bayesian framework. We will concentrate in this section on using this formulation to impose smoothness constraints on the solutions; other constraints can be similarly imposed, as will be described in later sections. A large part of this chapter will be concerned with new approaches to formulating energy function based algorithms using techniques borrowed from statistical mechanics.

Keywords

Partition Function Energy Function Data Fusion Markov Random Field Reflectance Function 
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

© Springer Science+Business Media New York 1990

Authors and Affiliations

  • James J. Clark
    • 1
  • Alan L. Yuille
    • 1
  1. 1.Division of Applied SciencesHarvard UniversityCambridgeUSA

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