Handling uncertainty in knowledge-based computer vision
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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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- Handling uncertainty in knowledge-based computer vision
- Book Title
- Symbolic and Quantitative Approaches to Uncertainty
- Book Subtitle
- European Conference ECSQAU Marseille, France, October 15–17, 1991 Proceedings
- pp 328-332
- Print ISBN
- Online ISBN
- Series Title
- Lecture Notes in Computer Science
- Series Volume
- Series ISSN
- Springer Berlin Heidelberg
- Copyright Holder
- Additional Links
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- Author Affiliations
- 1. Department of Computing, Imperial College, 180 Queen's Gate, SW7 2BZ, London, England
- 2. Centre for Logic and Probability in Information Technology, King's College, Manresa Road, SW3 6LX, London, England
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