The linear canonical transform (LCT) has attained respectable status within a short span and is being broadly employed across several disciplines of science engineering including signal processing, optical radar systems, electrical communication quantum physics etc, mainly due to the extra degrees freedom simple geometrical manifestation. In this article, we introduce novel multiresolution anal...

We study p-adic multiresolution analyses (MRAs). A complete characterisation of test functions generating MRAs (scaling functions) is given. We prove that only 1-periodic test functions may be taken as orthogonal scaling functions. We also suggest a method for the construction of wavelet functions and prove that any wavelet function generates a p-adic wavelet frame.

This paper presents a study on the development of new multiresolution directional analysis tools for texture denoising of medical images. Multiresolution texture analysis is performed with wavelet packets and brushlet expansions to exploit spatio-temporal coherence and identify persistent anatomical structures while removing uncorrelated noise components. Denoising is performed via thresholding...

In this paper, we propose a fast multi-resolution transform using wavelet packet transform (WPT). This fast algorithm switches between a transform coder and a subband coder on user discretion. The proposed algorithm uses discrete approximate trigonometric expansions, which have previously been proposed for exploiting spatial and spectral correlation in multidimensional signals. Specifically, we...

With the availability of large scale computing platforms and instrumentation for data gathering, increased emphasis is being placed on efficient techniques for analyzing large and extremely high-dimensional datasets. In this paper, we present a novel algebraic technique based on a variant of semi-discrete matrix decomposition (SDD), which is capable of compressing large discretevalued datasets ...

To analyze given object shapes, it is necessary rst to model the shapes and then to analyze the models. This paper proposes a method of modeling and analyzing two-dimensional (2D) and three-dimensional (3D) shapes based on singularities. First, a function is dened on an object. The object is then modeled by the distribution of the singularities of the function. Finally, the extracted singular p...

The use of exploratory methods is an important step in the understanding of data. When clustering functional data, most methods have used traditional clustering techniques on a vector of estimated basis coefficients, assuming that the underlying signal functions live in the L2-space. Bayesian methods use models which imply the belief that some observations are realizations from some signal plus...

This paper focuses on a novel framework for information retrieval of images based only on color information. Regions homogeneous in color of the input image are detected using a multiresolution analysis. In order to avoid the selection of regions having similar color, a new clustering scheme based on repulsion between electric charges, is proposed. The resulting model is completely automatic an...