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Titlebook: Wavelets in Signal and Image Analysis; From Theory to Pract Arthur A. Petrosian,François G. Meyer Book 2001 Springer Science+Business Media

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书目名称Wavelets in Signal and Image Analysis
副标题From Theory to Pract
编辑Arthur A. Petrosian,François G. Meyer
视频video
丛书名称Computational Imaging and Vision
图书封面Titlebook: Wavelets in Signal and Image Analysis; From Theory to Pract Arthur A. Petrosian,François G. Meyer Book 2001 Springer Science+Business Media
描述Despite their novelty, wavelets have a tremendous impact on anumber of modern scientific disciplines, particularly on signal andimage analysis. Because of their powerful underlying mathematicaltheory, they offer exciting opportunities for the design of newmulti-resolution processing algorithms and effective patternrecognition systems. .This book provides a much-needed overview of current trends in thepractical application of wavelet theory. It combines cutting edgeresearch in the rapidly developing wavelet theory with ideas frompractical signal and image analysis fields. Subjects dealt withinclude balanced discussions on wavelet theory and its specificapplication in diverse fields, ranging from data compression toseismic equipment. In addition, the book offers insights into recentadvances in emerging topics such as double density DWT, multiscaleBayesian estimation, symmetry and locality in image representation,and image fusion. ..Audience:. This volume will be of interest to graduate studentsand researchers whose work involves acoustics, speech, signal andimage processing, approximations and expansions, Fourier analysis, andmedical imaging.
出版日期Book 2001
关键词Textur; Wave; classification; data compression; diagnosis; image analysis; image processing; ultrasound
版次1
doihttps://doi.org/10.1007/978-94-015-9715-9
isbn_softcover978-90-481-5838-6
isbn_ebook978-94-015-9715-9Series ISSN 1381-6446
issn_series 1381-6446
copyrightSpringer Science+Business Media B.V. 2001
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Wavelet Domain Features for Texture/Pattern Description, Classification and Replicability Analysisd by traversing a tree. The algorithm is tested on a database with 340 images demonstrating an excellent classification accuracy. Additionally, we demonstrate the efficiency of our perceptual feature set with an application in texture/pattern retrieval.
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Wavelet Domain Features for Texture/Pattern Description, Classification and Replicability Analysisd by traversing a tree. The algorithm is tested on a database with 340 images demonstrating an excellent classification accuracy. Additionally, we demonstrate the efficiency of our perceptual feature set with an application in texture/pattern retrieval.
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Discrete-Time Waveletsnd reviews their properties in a systematic and consistent way. Different kinds of orthogonality between the wavelets are addressed and the corresponding sufficient and necessary conditions are derived. It is shown when discrete-time wavelets can be samples of continuous-time wavelets. The condition
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The Double Density DWTs, or . In particular, we consider the design of systems that are analogous to Daubechies’ orthonormal wavelets — that is, the design of minimal length wavelet filters satisfying certain polynomial properties, but now in the oversampled case. The wavelets are constructed using maximally flat FIR fil
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