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Proceedings Paper

Hierarchical multifeature integration for automatic target recognition
Author(s): Shishir Shah; Jake K. Aggarwal
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Paper Abstract

This paper presents a methodology for object recognition in complex scenes by learning multiple feature object representation in second generation Forward Looking InfraRed (FLIR) images. A hierarchical recognition framework is developed which solves the recognition task by performing classification using decisions at the lower levels and the input features. The system uses new algorithms for detection and segmentation of objects and a Bayesian formulation for combining multiple object features for improved discrimination. Experimental results on a large database of FLIR images is presented to validate the robustness of the system, and its applicability to FLIR imagery obtained from real scenes.

Paper Details

Date Published: 27 July 1999
PDF: 9 pages
Proc. SPIE 3720, Signal Processing, Sensor Fusion, and Target Recognition VIII, (27 July 1999); doi: 10.1117/12.357176
Show Author Affiliations
Shishir Shah, Wayne State Univ. (United States)
Jake K. Aggarwal, Univ. of Texas/Austin (United States)

Published in SPIE Proceedings Vol. 3720:
Signal Processing, Sensor Fusion, and Target Recognition VIII
Ivan Kadar, Editor(s)

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