Automatic target detection using entropy optimized shared-weight neural networks

Author

Khabou, Mohamed A. ; Gader, Paul D.

Author_Institution

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

Volume

11

Issue

1

fYear

2000

fDate

1/1/2000 12:00:00 AM

Firstpage

186

Lastpage

193

Abstract

Standard shared-weight neural networks previously demonstrated inferior performance to that of morphological shared-weight neural networks for automatic target detection. Empirical analysis showed that entropy measures of the features generated by the standard shared-weight neural networks were consistently lower than those generated by the morphological shared-weight neural networks. Based on this observation, an entropy maximization term was added to the standard shared-weight network objective function. In this paper, we present automatic target detection results for standard shared-weight neural networks trained with and without the added entropy term

Keywords

feature extraction; learning (artificial intelligence); mathematical morphology; maximum entropy methods; neural nets; object recognition; automatic target detection; feature extraction; learning algorithm; mathematical morphology; maximum entropy; objective function; shared-weight neural networks; Convolution; Entropy; Feature extraction; Kernel; Measurement standards; Morphology; Neural networks; Object detection; Target recognition; Testing;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/72.822520

Filename

822520