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Proceedings Paper

CPN-based multisensor data fusion for target classification
Author(s): LiHong Niu; GuoQiang Ni; Mingqi Liu
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Paper Abstract

A counter-propagation network (CPN) based system of multi-sensor data fusion at feature level for target classification is proposed in this paper. This presentation mainly describes the use ofthe CPN in the data fusion system for target classification, as well as the algorithm used for training the CPN. As a demonstration of the advantages of the CPN, a popular back-propagation network (BPN) and a standard counter-propagation network (SCPN) are investigated at the same time. Finally, to illustrate the effectiveness ofthe CPN with the modified training algorithm for data fusion at feature level, we present the experiments for the application system based on FUR and TV camera. The experimental results for the system using the real-world database show that the CPN with the proposed algorithm provides the best overall performance. The classification accuracy, robustness and learning process are significantly improved.

Paper Details

Date Published: 31 July 2002
PDF: 6 pages
Proc. SPIE 4875, Second International Conference on Image and Graphics, (31 July 2002); doi: 10.1117/12.477051
Show Author Affiliations
LiHong Niu, Beijing Institute of Technology (China)
GuoQiang Ni, Beijing Institute of Technology (China)
Mingqi Liu, Beijing Institute of Technology (China)

Published in SPIE Proceedings Vol. 4875:
Second International Conference on Image and Graphics
Wei Sui, Editor(s)

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