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Proceedings Paper

Temporal object identification via fuzzy models
Author(s): James M. Keller; Jeffrey Osborn
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Paper Abstract

In designing systems which analyze temporal sequences of images, it is necessary to provide mechanisms which identify, track, and release objects over time. There is considerable uncertainty in the definition of objects in a natural scene as evidenced, for example, in forward looking infrared (FLIR) imagery. In this paper, we present a methodology, based on the theory of fuzzy sets, which can handle this problem. It contains a feature driven fuzzy correlator which integrates current and past information to update object histories, to detect new objects, and to determine when objects leave the scene. The intention is to use such a system in a surveillance mode, where there is reasonable time for computation. Examples are given from an automatic target recognition application.

Paper Details

Date Published: 1 February 1992
PDF: 11 pages
Proc. SPIE 1607, Intelligent Robots and Computer Vision X: Algorithms and Techniques, (1 February 1992); doi: 10.1117/12.57083
Show Author Affiliations
James M. Keller, Univ. of Missouri/Columbia (United States)
Jeffrey Osborn, Univ. of Missouri/Columbia (United States)


Published in SPIE Proceedings Vol. 1607:
Intelligent Robots and Computer Vision X: Algorithms and Techniques
David P. Casasent, Editor(s)

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