A First Course on Wavelets

Hernández, Eugenio; Weiss, Guido

doi:10.1201/9780367802349

Cited by 220 publications

(64 citation statements)

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“…Thus by [Theorem 1.6, Sec. 2.1, [11]] and [Theorem 1.7, Sec.2.1, [11]] , {V j } j∈Z generates an MRA with scaling function θ.…”

Section: -D Mra and Wavelet Methodsmentioning

confidence: 99%

Daubechies Wavelet Transformations

Fleet¹,

Patrick²

2008

Discrete Wavelet Transformations

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In this paper we investigate wavelet-finite difference method for solving two-dimensional model of drug release in the cardiovascular tissue from the stent. We use a double tensor product to rich a two dimensional wavelet.By using this two dimensional wavelet in space and finite difference method for variable t we convert the drug release model to a system of equation. The Lax-Richtmyer theorem shows that this system is convergent and we obtain a good approximation for a solution of our problem.

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“…Thus by [Theorem 1.6, Sec. 2.1, [11]] and [Theorem 1.7, Sec.2.1, [11]] , {V j } j∈Z generates an MRA with scaling function θ.…”

Section: -D Mra and Wavelet Methodsmentioning

confidence: 99%

Daubechies Wavelet Transformations

Fleet¹,

Patrick²

2008

Discrete Wavelet Transformations

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“…If the dilation factor is an integer, then a great deal is known about the properties of the affine system X ( ). For example, one can give different characterizations for X ( ) to be a tight frame or an orthonormal basis, see [6], [9], [11], [13], [15]. For noninteger dilation factors significant progress has been made recently by Chui and Shi [10] who gave a characterization of pairs of dual frame wavelets.…”

Section: The Characterization Of Affine Tight Framesmentioning

confidence: 99%

“…Their method of proof yields in fact a slightly stronger result which is of great importance in this work. The motivation behind this development is Auscher's proof [2] of the nonexistence of well-localized wavelets in H 2 (R) =Ľ 2 (0, ∞), see also [13,Chapter 7.6].…”

Section: The Characterization Of Affine Tight Framesmentioning

confidence: 99%

On a Problem of Daubechies

Bownik

2003

Constr. Approx.

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“…Continuous wavelet transforms are operated on all possible dilation and translation while discrete wavelet transforms are used in particular subset of dilation and translation values [2]. Wavelets provide better signal representations because of multi-resolution analysis and additional time localized properties.…”

Section: Introductionmentioning

confidence: 99%

Face Recognition using Discrete Haar Wavelet Transforms

Kumari*¹,

Kumar*²,

Saxena³

2019

IJRTE

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Wavelet transform is being applied as an important analyzing tool in signal and image processing because it provides better signal representations with less computation time because of property of multiresolution analysis. An analogue image is transformed into a discrete image in a space through a sampling process. The wavelet transforms and analysis of discrete image are performed using Haar wavelet, level-2. Through analyzing and comparing different patterns, one or more persons are identified in the face recognition process. It is accomplished by using correlation coefficient and Euclidean distance of scaling coefficients of wavelet transformed images. In the face recognition the typically facial features are extracted and compared to a database for finding the best match. Matching of features is performed between test image and stored database images.

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A First Course on Wavelets

Cited by 220 publications

References 0 publications

Daubechies Wavelet Transformations

Daubechies Wavelet Transformations

On a Problem of Daubechies

Face Recognition using Discrete Haar Wavelet Transforms

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