(Thesis) Thesis (Ph. D.)--University of Florida, 1996.; (Bibliography) Includes bibliographical references (leaves 168-173).; (Statement of Responsibility) by Kwang Rip Hyun.; Typescript.; Vita.

Thesis (Ph.D.)--University of Adelaide, Dept. of Electrical and Electronic Engineering, 2002; Includes bibliographical references (leaves 199-214); xxx, 214 leaves : ill. (some col.), plates (col.) ; 30 cm.; Title page, contents and abstract only. The complete thesis in print form is available from the University Library.

In this thesis, wavelet transforms are chosen as the primary analytical tool for texture analysis. Specifically, Dual-Tree Complex Wavelet Transform is applied to the texture segmentation problem. Several possibilities for feature extraction and clustering steps are examined, new schemes being introduced and compared to known techniques.; Thesis (Ph.D.)--University of Adelaide, Dept. of Electrical and Electronic Engineering, 2003; "August, 2003"; Bibliography: p. 261-268.; xxvi, 268 p. : ill. (some col.) ; 30 cm.; Title page, contents and abstract only. The complete thesis in print form is available from the University Library.

Defence date: 13 July 1998; Examining board: Prof. Anindya Banerjee, Oxford University ; Prof. Søren Johansen, EUI ; Prof. Grayham Mizon, EUI, Supervisor ; Prof. Klaus Sandmann, University of Mainz

In this thesis, we generalize the classical discrete wavelet transform, and construct wavelet transforms that are shift-invariant, time-varying, undecimated, and signal dependent. The result is a set of powerful and efficient algorithms suitable for a wide variety of signal processing tasks, e.g., data compression, signal analysis, noise reduction, statistical estimation, and detection. These algorithms are comparable and often superior to traditional methods. In this sense, we put wavelets in action.

The fundamental concept of orthogonality of mathematical objects occurs in a wide variety of physical and engineering disciplines. The theory of orthogonal functions, for example, is central to the development of Fourier series and wavelets, essential for signal processing. In particular, various families of classical orthogonal polynomials have traditionally been applied to fields such as electrostatics, numerical analysis, and many others. This thesis develops the main ideas necessary for understanding the classical theory of orthogonal polynomials. Special emphasis is given to the Jacobi polynomials and to certain important subclasses and generalizations, some recently discovered. Using the theory of hypergeometric power series and their q -extensions, various structural properties and relations between these classes are systematically investigated. Recently, these classes have found significant applications in coding theory and the study of angular momentum, and hold much promise for future applications. orthogonal polynomials, hypergeometric series

Inverse problems involve estimating parameters or data from inadequate observations; the observations are often noisy and contain incomplete information about the target parameter or data due to physical limitations of the measurement devices. Consequently, solutions to inverse problems are non-unique. To pin down a solution, we must exploit the underlying structure of the desired solution set. In this thesis, we formulate novel solutions to three image processing inverse problems: deconvolution, inverse halftoning, and JPEG compression history estimation for color images. Deconvolution aims to extract crisp images from blurry observations. We propose an efficient, hybrid Fourier-Wavelet Regularized Deconvolution (ForWaRD) algorithm that comprises blurring operator inversion followed by noise attenuation via scalar shrinkage in both the Fourier and wavelet domains. The Fourier shrinkage exploits the structure of the colored noise inherent in deconvolution, while the wavelet shrinkage exploits the piecewise smooth structure of real-world signals and images. ForWaRD yields state-of-the-art mean-squared-error (MSE) performance in practice. Further, for certain problems, ForWaRD guarantees an optimal rate of MSE decay with increasing resolution. Halftoning is a technique used to render gray-scale images using only black or white dots. Inverse halftoning aims to recover the shades of gray from the binary image and is vital to process scanned images. Using a linear approximation model for halftoning...

Se presentan esquemas de caracterización dinámica aplicados sobre señales electromiográficas utilizando una estructura de bancos de filtros con variación de parámetros adaptativos. Los bancos de filtros son calculados a partir de esquemas de actualización. Los criterios de adaptabilidad utilizados para la caracterización dinámica son momentos estadísticos y vector gradiente. Para la selección de características se utiliza el módulo máximo. Los resultados de clasificación con los métodos adaptativos son superiores en un 9 % a los resultados obtenidos con bancos de filtros de parámetros fijos. Se utiliza para la clasificación un clasificador bayesiano con la distancia de Mahalanobis.

This article is a continuation of the recent paper [Grohs, Intrinsic localization of anisotropic frames, ACHA, 2013], where off-diagonal-decay properties (often referred to as 'localization' in the literature) of Moore-Penrose pseudoinverses of (bi-infinite) matrices are established, whenever the latter possess similar off-diagonal-decay properties. This problem is especially interesting if the matrix arises as a discretization of an operator with respect to a frame or basis. Previous work on this problem has been restricted to wavelet- or Gabor frames. In the previous work we extended these results to frames of parabolic molecules, including curvelets or shearlets as special cases. The present paper extends and unifies these results by establishing analogous properties for frames of $\alpha$-molecules as introduced in recent work [Grohs, Keiper, Kutyniok, Sch\"afer, Alpha molecules: curvelets, shearlets, ridgelets, and beyond, Proc. SPIE. 8858, 2013]. Since wavelets, curvelets, shearlets, ridgelets and hybrid shearlets all constitute instances of $\alpha$-molecules, our results establish localization properties for all these systems simultaneously.; Comment: 16 pages

New results on uniform convergence in probability for expansions of Gaussian random processes using compactly supported wavelets are given. The main result is valid for general classes of nonstationary processes. An application of the obtained results to stationary processes is also presented. It is shown that the convergence rate of the expansions is exponential.; Comment: This is an Author's Accepted Manuscript of an article published in the Communications in Statistics - Theory and Methods. 15 pages

Quantization with coherent states allows to " quantize " any space X of parameters. In the case where X is a phase space, this leads to the usual quantum mechanics. But the procedure is much more general, and does not require a symplectic, or any kind of structure in X, other than a measure. It is simply considered as a different way to look at the system, the choice of a resolution, in analogy with data handling, where coherent states (e.g., under the form of wavelets) are very efficient. Here, we present the complex coherent states quantization of the 2-sphere, with emphasis on the links with group representation. We show how this procedure leads naturally to the fuzzy sphere and to non commutative geometry.; Comment: 9 pages, no figure. Int. J. Theor. Phys. 2003, in press

In this paper, we study scalar multivariate non-stationary subdivision schemes with integer dilation matrix M=mI, m >=2, and present a general approach for checking their convergence and for determining their H\"older regularity. The combination of the concepts of asymptotic similarity and approximate sum rules allows us to link stationary and non-stationary settings and to employ recent advances in methods for exact computation of the joint spectral radius. As an application, we prove a recent conjecture on the H\"older regularity of the generalized Daubechies wavelets. We illustrate our results with several examples.

We show that the representations of the Cuntz C$^\ast$-algebras $O_n$ which arise in wavelet analysis and dilation theory can be classified through a simple analysis of completely positive maps on finite-dimensional space. Based on this analysis, an application in quantum information theory is obtained; namely, a structure theorem for the fixed point set of a unital quantum channel. We also include some open problems motivated by this work.; Comment: 15 pages, preprint version

We prove and construct Shannon-like Parseval wavelet frames for a class of two step connected, and simply connected nilpotent Lie groups, using a mixture of representation theory, group Fourier theory, and Gabor theory. Moreover, we are able to compute an upperbound for the norm of these Parseval frame wavelets.

Tight-frame, a generalization of orthogonal wavelets, has been used successfully in various problems in image processing, including inpainting, impulse noise removal, super-resolution image restoration, etc. Segmentation is the process of identifying object outlines within images. There are quite a few efficient algorithms for segmentation that depend on the variational approach and the partial differential equation (PDE) modeling. In this paper, we propose to apply the tight-frame approach to automatically identify tube-like structures such as blood vessels in Magnetic Resonance Angiography (MRA) images. Our method iteratively refines a region that encloses the possible boundary or surface of the vessels. In each iteration, we apply the tight-frame algorithm to denoise and smooth the possible boundary and sharpen the region. We prove the convergence of our algorithm. Numerical experiments on real 2D/3D MRA images demonstrate that our method is very efficient with convergence usually within a few iterations, and it outperforms existing PDE and variational methods as it can extract more tubular objects and fine details in the images.

In this paper the whole family of fractional Brownian motions is constructed as a single Gaussian field indexed by time and the Hurst index simultaneously. The field has a simple covariance structure and it is related to two generalizations of fractional Brownian motion known as multifractional Brownian motions. A mistake common to the existing literature regarding multifractional Brownian motions is pointed out and corrected. The Gaussian field, due to inherited ``duality'', reveals a new way of constructing martingales associated with the odd and even part of a fractional Brownian motion and therefore of the fractional Brownian motion. The existence of those martingales and their stochastic representations is the first step to the study of natural wavelet expansions associated to those processes in the spirit of our earlier work on a construction of natural wavelets associated to Gaussian-Markov processes.; Comment: Published at http://dx.doi.org/10.1214/074921706000000770 in the IMS Lecture Notes Monograph Series (http://www.imstat.org/publications/lecnotes.htm) by the Institute of Mathematical Statistics (http://www.imstat.org)

Compared with the traditional spherical harmonics, the spherical needlets are a new generation of spherical wavelets that possess several attractive properties. Their double localization in both spatial and frequency domains empowers them to easily and sparsely represent functions with small spatial scale features. This paper is divided into two parts. First, it reviews the spherical harmonics and discusses their limitations in representing functions with small spatial scale features. To overcome the limitations, it introduces the spherical needlets and their attractive properties. In the second part of the paper, a Matlab package for the spherical needlets is presented. The properties of the spherical needlets are demonstrated by several examples using the package.; Comment: 12 pages, 7 figures, technical report

Exascale computing promises quantities of data too large to efficiently store and transfer across networks in order to be able to analyze and visualize the results. We investigate Compressive Sensing (CS) as a way to reduce the size of the data as it is being stored. CS works by sampling the data on the computational cluster within an alternative function space such as wavelet bases, and then reconstructing back to the original space on visualization platforms. While much work has gone into exploring CS on structured data sets, such as image data, we investigate its usefulness for point clouds such as unstructured mesh datasets found in many finite element simulations. We sample using second generation wavelets (SGW) and reconstruct using the Stagewise Orthogonal Matching Pursuit (StOMP) algorithm. We analyze the compression ratios achievable and quality of reconstructed results at each compression rate. We are able to achieve compression ratios between 10 and 30 on moderate size datasets with minimal visual deterioration as a result of the lossy compression.; Comment: 18 pages, 7 figures

We set up a new general coorbit space theory for reproducing representations of a locally compact second countable group $G$ that are not necessarily irreducible nor integrable. Our basic assumption is that the kernel associated with the voice transform belongs to a Fr\'echet space $\mathcal T$ of functions on $G$, which generalizes the classical choice $\mathcal T=L_w^1(G)$. Our basic example is $ \mathcal T=\bigcap_{p\in(1,+\infty)} L^p(G)$, or a weighted versions of it. By means of this choice it is possible to treat, for instance, Paley-Wiener spaces and coorbit spaces related to Shannon wavelets and Schr\"odingerlets.; Comment: 52 pages, 1 figures

This survey paper discusses the history of approximation formulas for n-th order derivatives by integrals involving orthogonal polynomials. There is a large but rather disconnected corpus of literature on such formulas. We give some results in greater generality than in the literature. Notably we unify the continuous and discrete case. We make many side remarks, for instance on wavelets, Mantica's Fourier-Bessel functions and Greville's minimum R_alpha formulas in connection with discrete smoothing.; Comment: 35 pages, 3 figures; minor corrections, subsection 3.11 added; accepted by J. Approx. Theory