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Handling uncertainty in knowledge-based computer vision

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Symbolic and Quantitative Approaches to Uncertainty (ECSQARU 1991)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 548))

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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Author information

Authors and Affiliations

  1. Department of Computing, Imperial College, 180 Queen's Gate, SW7 2BZ, London, England

    L. Enrique Sucar & Duncan F. Gillies

  2. Centre for Logic and Probability in Information Technology, King's College, Manresa Road, SW3 6LX, London, England

    Donald A. Gillies

Authors
  1. L. Enrique Sucar
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  2. Duncan F. Gillies
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  3. Donald A. Gillies
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Editor information

Rudolf Kruse Pierre Siegel

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© 1991 Springer-Verlag Berlin Heidelberg

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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. https://doi.org/10.1007/3-540-54659-6_110

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  • DOI: https://doi.org/10.1007/3-540-54659-6_110

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-54659-7

  • Online ISBN: 978-3-540-46426-6

  • eBook Packages: Springer Book Archive

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