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Proceedings Paper

A Blackboard-Based Architecture For The Interpretation Of Image Sequences
Author(s): Christine Porquet; M. Desvignes; M. Revenu
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Paper Abstract

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.

Paper Details

Date Published: 1 March 1990
PDF: 12 pages
Proc. SPIE 1192, Intelligent Robots and Computer Vision VIII: Algorithms and Techniques, (1 March 1990); doi: 10.1117/12.969785
Show Author Affiliations
Christine Porquet, GERSIC - ISMRa - ENSI de CAEN (France)
M. Desvignes, GERSIC - ISMRa - ENSI de CAEN (France)
M. Revenu, GERSIC - ISMRa - ENSI de CAEN (France)

Published in SPIE Proceedings Vol. 1192:
Intelligent Robots and Computer Vision VIII: Algorithms and Techniques
David P. Casasent, Editor(s)

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