5 April 2002 Automatic edge and target extraction based on pulse-couple neuron networks wavelet theory (PCNNW)
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Proceedings Volume 4668, Applications of Artificial Neural Networks in Image Processing VII; (2002); doi: 10.1117/12.461669
Event: Electronic Imaging, 2002, San Jose, California, United States
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.
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Kya Berthe Abraham, Yang Yang, "Automatic edge and target extraction based on pulse-couple neuron networks wavelet theory (PCNNW)", Proc. SPIE 4668, Applications of Artificial Neural Networks in Image Processing VII, (5 April 2002); doi: 10.1117/12.461669; http://dx.doi.org/10.1117/12.461669
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KEYWORDS
Wavelets

Neurons

Image processing

Interference (communication)

Denoising

Neural networks

Image segmentation

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