In this paper, we present a direct solution method based on wavelet networks for image compression. Wavelet networks are a combination of radial basis function (RBF) networks and wavelet decomposition, where radial basis functions were replaced by wavelets. The results show that the wavelet networks approach succeeded to improve high performances in terms of compression ratio and reconstruction...

Image Enhancement is for Improving Visibility and can also be used for different task, It is also important to provide a better representation for further automated image processing such as image analysis, detection, segmentation, recognition and data hiding which can be gain by image enhancement technique there are various method are available for the enhancement like histogram equalization et...

Abstract A simple yet effective architectural design of radial basis function neural networks (RBFNN) makes them amongst the most popular conventional networks. The current generation network is equipped with multiple kernels which provide significant performance benefits compared to previous using only a single kernel. In existing multi-kernel RBF algorithms, formed by convex combination base/...

In this paper we investigate the use of compactly supported RBF kernels for nonlinear function estimation with LS-SVMs. The choice of compact kernels recently proposed by Genton may lead to computational improvements and memory reduction. Examples however illustrate that compactly supported RBF kernels may lead to severe loss in generalization performance for some applications, e.g. in chaotic ...

The use of kernels is a key factor in the success of many classification algorithms by allowing nonlinear decision surfaces. Radial basis function (RBF) kernels are commonly used but often associated with dense Gram matrices. We consider a mathematical operator to sparsify any RBF kernel systematically, yielding a kernel with a compact support and sparse Gram matrix. Having many zero elements i...

AbstractThis paper describes an experiment on face recognition using a simple feature vector and Support Vector Machine (SVM) classifier. Polynomial and Radial Basis Function (RBF) kernels of SVM are used for classification. The dataset in this experiment consists of a set of images of eight different faces (eight classes) containing ten different images for a single class. The experiment is pe...

This paper proposes a comparison between wavelet neural networks (WNN), RBF neural network and polynomial approximation in term of 1-D and 2-D functions approximation. We present a novel wavelet neural network, based on Beta wavelets, for 1-D and 2-D functions approximation. Our purpose is to approximate an unknown function f: Rn R from scattered samples (xi; y = f(xi)) i=1....n, where first, w...

It is important to detect abnormal brains accurately and early. The wavelet-energy (WE) was a successful feature descriptor that achieved excellent performance in various applications; hence, we proposed a WE based new approach for automated abnormal detection, and reported its preliminary results in this study. The kernel support vector machine (KSVM) was used as the classifier, and quantum-be...

Entropy as a measure of uncertainty was used to represent the results of the wavelet technique in electrochemical noise analysis. The experimental signals were obtained by recording the electrochemical potential and current noise of 7075 aluminum alloy in 3.5% NaCl solution. The electrochemical potential and current noise were decomposed into 16 levels using Daubechies wavelets. Wavelet output ...