MACE prefiltering for neural network based automatic target recognition

Author

Gader, Paul ; Keller, James M. ; Jones, Thomas ; Miramonti, Joseph ; Hobson, Greg

Author_Institution

Dept. of Electr. & Comput. Eng., Missouri Univ., Columbia, MO, USA

Volume

6

fYear

1994

fDate

27 Jun- 2 Jul 1994

Firstpage

4006

Abstract

A method for achieving improved target recognition rates with low false alarm rates is presented. The method combines frequency domain advantages of the minimum average correlation energy (MACE) filter with the superior classification abilities of multi-layer, feedforward networks. Results are provided on forward looking infrared (FLIR) images of real targets. A new evaluation metric is proposed that measures the quality of the results

Keywords

correlation methods; feedforward neural nets; filtering theory; image classification; multilayer perceptrons; object recognition; MACE prefiltering; classification abilities; evaluation metric; false alarm rates; forward looking infrared images; frequency domain advantages; minimum average correlation energy filter; multilayer feedforward networks; neural network based automatic target recognition; target recognition rates; Feedforward neural networks; Frequency domain analysis; Infrared imaging; Matched filters; Mean square error methods; Multi-layer neural network; Neural networks; Nonlinear filters; Power engineering and energy; Target recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-1901-X

Type

conf

DOI

10.1109/ICNN.1994.374854

Filename

374854