This is not a purely mathematical text. It presents the basic principle of wavelet theory to electrical and electronic engineers, computer scientists, and students, as well as the ideas of how wavelets can be applied to pattern recognition. It also contains many research results from the authors' research team.
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This review is from: Wavelet Theory and Its Application to Pa (Machine Perception & Artificial Intelligence) (Hardcover)
I was interested in modern research relating wavelets to pattern recognition. What I found was a marginal book which had poorly constructed proofs related to wavelets. The book was even more disappointing in its attempt at covering pattern recognition.The book has little to no new material, and is poor at attempting to explain existing concepts. Overall a disappointing book on a very interesting topic.
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Front Cover | Front Flap | Table of Contents | First Pages | Index | Back Flap | Back Cover | Surprise Me!
Inside This Book
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First Sentence:
Wavelet analysis is a relatively recent development of applied mathematics in 1980s. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
data preprocessing phase, series density estimator, orthogonal wavelet series, multiresolution support, divider dimension, wavelet descriptors, generalized linear phase, supported orthonormal wavelets, maximal modulus, new type style, corresponding scaling function, filtering coefficients, orthonormal decomposition, spline wavelet, wavelet reconstruction, decomposition layers, wavelet bases, wavelet transform, cascade algorithm, basic wavelet, wavelet theory, character processing, frequency window, orthonormal wavelet basis, jth layer Key Phrases - Capitalized Phrases (CAPs): (learn more)
Some Typical Wavelet Bases, Center Radius Center Radius Area, Time Window Freq, Window Time-Freq, Fang Song, Characterization of Lipschitz Regularity of Signal, Reference Line Detection New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Front Flap | Table of Contents | First Pages | Index | Back Flap | Back Cover | Surprise Me!
Wavelet analysis is a relatively recent development of applied mathematics in 1980s. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
data preprocessing phase, series density estimator, orthogonal wavelet series, multiresolution support, divider dimension, wavelet descriptors, generalized linear phase, supported orthonormal wavelets, maximal modulus, new type style, corresponding scaling function, filtering coefficients, orthonormal decomposition, spline wavelet, wavelet reconstruction, decomposition layers, wavelet bases, wavelet transform, cascade algorithm, basic wavelet, wavelet theory, character processing, frequency window, orthonormal wavelet basis, jth layer Key Phrases - Capitalized Phrases (CAPs): (learn more)
Some Typical Wavelet Bases, Center Radius Center Radius Area, Time Window Freq, Window Time-Freq, Fang Song, Characterization of Lipschitz Regularity of Signal, Reference Line Detection New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Front Flap | Table of Contents | First Pages | Index | Back Flap | Back Cover | Surprise Me!
Citations (learn more)
This book cites 26
books:
See all 26 books this book cites
- Real and Complex Analysis (International Series in Pure and Applied Mathematics) by Walter Rudin in Back Matter
- Elliptic Partial Differential Equations of Second Order by David Gilbarg in Back Matter
- Hong Kong (Where We Live) by Donna Bailey in Front Matter
- Wavelets for Computer Graphics: Theory and Applications (The Morgan Kaufmann Series in Computer Graphics) by Eric J. Stollnitz in Back Matter
- Machine Vision for Advanced Production by M. Pietikainen in Front Matter
See all 26 books this book cites
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