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Proceedings Paper

Automatic FLIR target recognition using a hierarchical neural system
Author(s): Adam R. Nolan; William G. Wee; James D. Leonard
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Paper Abstract

This paper describes an approach to areas of FLIR target recognition: (1) target isolation, and (2) target classification. The method utilized for the isolation of potential target regions is based on localized texture information. The modality of the local gradient histogram is used to define both target regions and to segment these regions into subcomponents corresponding to the vehicle morphology (wheels, engine, armor, etc.). After the target regions are isolated, each region is fit with a metric (parallelogram). Each subcomponent in this region is then classified based on its shape and location within this metric. The classification is made using several neural networks with each corresponding to a specific vehicular subcomponent. The classifications of these neural networks are then used as input to another network responsible for vehicle type classification. This construct allows for azimuth and depression angle robustness of the target region, the limitations of which are discussed.

Paper Details

Date Published: 21 May 1993
PDF: 10 pages
Proc. SPIE 1902, Nonlinear Image Processing IV, (21 May 1993); doi: 10.1117/12.144766
Show Author Affiliations
Adam R. Nolan, Univ. of Cincinnati (United States)
William G. Wee, Univ. of Cincinnati (United States)
James D. Leonard, Air Force Wright Lab. (United States)

Published in SPIE Proceedings Vol. 1902:
Nonlinear Image Processing IV
Edward R. Dougherty; Jaakko T. Astola; Harold G. Longbotham, Editor(s)

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