Advertisement

Data Fusion Applied to Feature Based Stereo Algorithms

  • 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

In this chapter we describe a theoretical formulation for stereo (this was first proposed by Yuille, Geiger and Bülthoff in [169]) in terms of the Bayesian approach to vision outlined in chapters 2 and 3, in particular in terms of coupled Markov Random Fields. We show that this formalism is rich enough to contain most of the elements used in standard stereo theories.

Keywords

Partition Function Data Fusion Level Theory Epipolar Line Matching Element 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

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

Personalised recommendations