Contributed Papers

Symbolic and Quantitative Approaches to Uncertainty

Volume 548 of the series Lecture Notes in Computer Science pp 328-332

Date:

Handling uncertainty in knowledge-based computer vision

  • L. Enrique SucarAffiliated withDepartment of Computing, Imperial College
  • , Duncan F. GilliesAffiliated withDepartment of Computing, Imperial College
  • , Donald A. GilliesAffiliated withCentre for Logic and Probability in Information Technology, King's College

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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.