Handling uncertainty in knowledge-based computer vision

  • L. Enrique Sucar
  • Duncan F. Gillies
  • Donald A. Gillies
Contributed Papers

DOI: 10.1007/3-540-54659-6_110

Part of the Lecture Notes in Computer Science book series (LNCS, volume 548)
Cite this paper as:
Sucar L.E., Gillies D.F., Gillies D.A. (1991) Handling uncertainty in knowledge-based computer vision. In: Kruse R., Siegel P. (eds) Symbolic and Quantitative Approaches to Uncertainty. ECSQARU 1991. Lecture Notes in Computer Science, vol 548. Springer, Berlin, Heidelberg

Abstract

Probability theory provides a sound theoretical foundation for handling uncertainty in computer vision. Its objective interpretation allows us to use data for improving the quantitative and qualitative structure of our KB. An important challenge in vision is to find which are the important features to recognize the different objects in the world, and a probabilistic approach provides a useful tool for advancing in this direction.

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Copyright information

© Springer-Verlag 1991

Authors and Affiliations

  • L. Enrique Sucar
    • 1
  • Duncan F. Gillies
    • 1
  • Donald A. Gillies
    • 2
  1. 1.Department of ComputingImperial CollegeLondonEngland
  2. 2.Centre for Logic and Probability in Information TechnologyKing's CollegeLondonEngland

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