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Vertex-Frequency Analysis of Graph Signals

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Cover of 'Vertex-Frequency Analysis of Graph Signals'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction to Graph Signal Processing
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    Chapter 2 Transformation from Graphs to Signals and Back
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    Chapter 3 The Spectral Graph Wavelet Transform: Fundamental Theory and Fast Computation
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    Chapter 4 Spectral Design of Signal-Adapted Tight Frames on Graphs
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    Chapter 5 Wavelets on Graphs via Deep Learning
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    Chapter 6 Oversampled Transforms for Graph Signals
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    Chapter 7 Local-Set-Based Graph Signal Sampling and Reconstruction
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    Chapter 8 Time-Varying Graph Signals Reconstruction
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    Chapter 9 Uncertainty Principle on Graphs
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    Chapter 10 A Filtering Framework for Time-Varying Graph Signals
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    Chapter 11 Vertex-Frequency Energy Distributions
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    Chapter 12 Shape Analysis of Carpal Bones Using Spectral Graph Wavelets
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    Chapter 13 Estimating the Complexity of the Cerebral Cortex Folding with a Local Shape Spectral Analysis
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    Chapter 14 Wavelet-Based Visual Data Exploration
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    Chapter 15 Graph-Based Wavelet Multiresolution Modeling of Multivariate Terrain Data
Attention for Chapter 13: Estimating the Complexity of the Cerebral Cortex Folding with a Local Shape Spectral Analysis
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Citations

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Chapter title
Estimating the Complexity of the Cerebral Cortex Folding with a Local Shape Spectral Analysis
Chapter number 13
Book title
Vertex-Frequency Analysis of Graph Signals
Published by
Springer, Cham, December 2018
DOI 10.1007/978-3-030-03574-7_13
Book ISBNs
978-3-03-003573-0, 978-3-03-003574-7
Authors

Hamed Rabiei, Frédéric Richard, Olivier Coulon, Julien Lefèvre

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 7 100%

Demographic breakdown

Readers by professional status Count As %
Professor 2 29%
Researcher 2 29%
Student > Ph. D. Student 1 14%
Unknown 2 29%
Readers by discipline Count As %
Mathematics 2 29%
Environmental Science 1 14%
Computer Science 1 14%
Economics, Econometrics and Finance 1 14%
Engineering 1 14%
Other 0 0%
Unknown 1 14%

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