A Blackboard-Based Architecture For The Interpretation Of Image Sequences
Christine Porquet, Michel Desvignes, Marinette Revenu · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1990
In recent works about automatic target recognition, researchers try to make the most of the use of context. We have built a tool to study the use of context during the interpretation of an image sequence. This tool is written in an object-oriented language we have developed, AIRELLE and has a meta-level blackboard architecture. It runs according to a prediction-verification-propagation cycle. The strategy is both data-driven and model-driven; the focus of attention areas created thanks to the hypotheses enable a more rapid convergence towards a consistent interpretation of the scene. In this paper, AIRELLE and the blackboard architecture of the system are described in detail. Then, we show how our system for the interpretation of image sequences was designed and we describe its knowledge representation and knowledge sources.