ÐDyadic wavelet transform has been used to derive an affine invariant function. First, an invariant function using two dyadic levels is derived. Then, this invariant function is used to derive another invariant function using six dyadic levels. We introduced the wavelet-based conic equation. The invariant function is based on analyzing the object boundary using the dyadic wavelet transform. Exp...

An application of Beta wavelet networks to synthesize pass-high and pass-low wavelet filters is investigated in this work. A Beta wavelet network is constructed using a parametric function called Beta function in order to resolve some nonlinear approximation problem. We combine the filter design theory with wavelet network approximation to synthesize perfect filter reconstruction. The order fil...

Wavelet radiosity is, by its nature, restricted to parallelograms or triangles. This paper presents an innovative technique enabling wavelet radiosity computations on planar surfaces of arbitrary shape, including concave contours or contours with holes. This technique replaces the need for triangulating such complicated shapes, greatly reducing the complexity of the wavelet radiosity algorithm ...

The paper formulates and proves a second order interpolation result for square-integrable functions by means of locally nite series of Daubechies' wavelets. Sample values of a suuciently smooth function can be used as coeecients of a wavelet expansion at a ne scale, and the corresponding wavelet interpolation function converges in Sobolev norms of rst order to the original function. This has ap...

This article studies change-points of a function for noisy data observed from a transformation of the function. The proposed method uses a wavelet-vaguelette decomposition to extract information about the wavelet transformation of the function from the data and then detect and estimate change-points by the wavelet transformation. Asymptotic theory for the detection and estimation is established...

Wavelet transform has been developed and applied in various areas of science and engineering. It offers better signal processing capabilities due to the existence of a large number of wavelet functions. The superiority comes with a requirement of methods to select a proper wavelet function for each signal or application. Selection methods based on signal properties are not always applicable due...