Automatic edge and target extraction base on pulse-couple neuron networks wavelet theory (PCNNW)
K. Berthe, Yang Yang · 2002
Recent developments in pulse-coupled neural networks (PCNN) techniques provide is efficiency in edge and target extraction. The detection of targets is facilitated by PCNN multiscale image factorization. But noise is still the enemy of PCNN. An efficient new pulse-coupled neural networks technique has been proposed by combining with wavelet theory. The new pulse-couple neuron network (PCNNW) is based on multiresolution decomposition for extracting the features of interest in the images by eliminating the noise. On the other hand the wavelet coefficients provide supplemental discrimination and lead to characteristic sets of numbers useful in identifying image factors of interest. The efficiency of the new method has been attested through some test images.