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Proceedings Paper

Automatic edge and target extraction based on pulse-couple neuron networks wavelet theory (PCNNW)
Author(s): Kya Berthe Abraham; Yang Yang
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Paper Abstract

Recent developments in Pulse-Coupled Neural Networks (PCNN) techniques provide efficiency in edge and target extraction. The detection of targets is facilitated by PCNN multi-scale image factorization. But noise is still the enemy of PCNN. An efficient new Pulse-Coupled Neural Networks technique has been proposed in combination with the wavelet theory. The new Pulse-Coupled Neural Network Wavelet (PCNNW) is based on multi-resolution decomposition for extracting the main features of the images by eliminating the noise. In addition, the wavelet coefficients provide the Pulse-Coupled Neural Network (PCNN) supplemental discrimination and lead to characteristic sets of numbers useful in identifying image factors of interest. The efficiency of the method has been tested and compared with other PCNN denoising methods.

Paper Details

Date Published: 5 April 2002
PDF: 9 pages
Proc. SPIE 4668, Applications of Artificial Neural Networks in Image Processing VII, (5 April 2002); doi: 10.1117/12.461669
Show Author Affiliations
Kya Berthe Abraham, Univ. of Science and Technology (China)
Yang Yang, Univ. of Science and Technology (China)

Published in SPIE Proceedings Vol. 4668:
Applications of Artificial Neural Networks in Image Processing VII
Nasser M. Nasrabadi; Aggelos K. Katsaggelos, Editor(s)

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