Reasoning driven blackboard system for image understanding

Authors:
Alicia Paul
Affiliations:
Clark Atlanta University, Atlanta, GA
Venue:
Proceedings of the 44th annual Southeast regional conference
Year:
2006

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Abstract

Understanding objects in an image is a current problem in computer vision. This paper propose an image understanding system that recognizes complex objects based on geometric shapes and color. This system is based on a blackboard architecture that uses image processing algorithms as Knowledge Sources (KSs) to extract features in the image, and then infer the presence of certain objects based on the these features. The management of these KSs will be driven by a reasoning system like a belief network or a rule-based system.