Wavelet packet decompositions based on tree structured 2-channel filter banks with conjugate quadrature filters (CQF) have recently found many applications in the area of audio coding. Their time-frequency tiling is dual to that of timevarying modulated lapped transforms (MLT). In this paper we present a new orthonormal wavelet packet basis, which is constructed by frequency-varying MLT. These ...

Health monitoring methods using active sensors (e.g. piezoelectric transducers) and wavelet transforms are being developed in the Department of Mechanical Engineering at the University of South Carolina. In these methods, wave propagation signals are collected using arrays of piezoelectric transducers placed on or embedded in a structure. The collected signals are analyzed using appropriate wav...

This paper describes an approach for texture characterization based on various wavelet decomposition transforms and its application for the discrimination of visually similar renal stone images. The proposed feature extraction algorithm applies Daubechies, Symlet, Coieflet, orthogonal and reverse biorthogonal wavelet transforms and uses approximation and detail coefficients to characterize rena...

Progressive texture synthesis can provide an added functional advantage to existing texture synthesis algorithms, which are time consuming and fail to deliver in some application areas. To provide practical solutions to this challenge we have previously proposed a Discrete Wavelet Transform (DWT) based texture synthesis algorithm for 2D surfaces. In this paper we propose the extension of this a...

De-noising algorithms based on wavelet thresholding replace small wavelet coeecients by zero and keep or shrink the coeecients with absolute value above the threshold. The optimal threshold minimizes the error of the result as compared to the unknown, exact data. To estimate this optimal threshold, we use Generalized Cross Validation. This procedure does not require an estimate for the noise en...

As the number of applications and use of wavelet transforms continues to grow, so does the number of classes and variations of wavelet transform algorithms. All of these algorithms incorporate a filter convolution in some implementation, typically, as part of an iterated filter bank. In contrast to implementations of the classical Fourier transform where there is at most a choice of sign and no...

This paper presents parallel algorithms for computing multi-dimensional wavelet transforms on both shared memory and distributed memory machines. Traditional data partitioning methods for n-dimensional Discrete Wavelet Transforms (DWTs) call for data redistribution once a one dimensional wavelet transform is computed along each dimension. To avoid the data communication inherent in this redistr...

De-noising algorithms based on wavelet thresholding replace small wavelet coeecients by zero and keep or shrink the coeecients with absolute value above the threshold. The optimal threshold minimizes the error of the result as compared to the unknown, exact data. To estimate this optimal threshold, we use Generalized Cross Validation. This procedure is fast and does not require an estimation fo...

Ramesh Neelamani December 23, 1998 Project Advisor: Prof. C.S. Burrus ELEC 696 Project Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005. [email protected] Abstract Lifting has traditionally been described in the time/spatial domain and the intuition behind the entire scheme holds in this domain. It is known that the lifting scheme, as conventionally desc...

We describe several \wavelet transforms" which characterize smoothness spaces and for which the coe cients are obtained by sampling rather than integration. We use them to re-interpret the empirical wavelet transform, i.e. the common practice of applying pyramid lters to samples of a function.