Handling uncertainty in knowledge-based computer vision
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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
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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