Diffusion-Driven Wavelet Design for Shape Analysis

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3D mesh processing
A01=Hong Qin
A01=Tingbo Hou
Anisotropic Diffusion
Author_Hong Qin
Author_Tingbo Hou
beltrami
Category=UB
Diffusion Wavelets
eigenfunctions
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Fourier Domain
functional analysis methods
geometric modelling techniques
Geometry Processing
hat
heat
Heat Diffusion
Heat Equation
Isometric Deformation
kernel
Kernel Signature
laplace
Laplace Beltrami Eigenfunctions
Laplace Beltrami Operator
manifold learning
medical image analysis
mexican
Mexican Hat Wavelet
operator
Point Cloud
Qin 12b
Riesz Basis
Saliency Maps
Scaling Functions
Semigroup Identity
Shape Registration
Shape Retrieval
spectral graph theory
spectral shape comparison techniques
transform
Vertex Coordinates
Wavelet Coefficients
Wavelet Design
Wavelet Tools
Wavelet Transform
wavelets
WS

Product details

  • ISBN 9780367378240
  • Weight: 453g
  • Dimensions: 152 x 229mm
  • Publication Date: 23 Oct 2019
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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From Design Methods and Generation Schemes to State-of-the-Art ApplicationsWavelets are powerful tools for functional analysis and geometry processing, enabling researchers to determine the structure of data and analyze 3D shapes. Suitable for researchers in computer graphics, computer vision, visualization, medical imaging, and geometric modeling as well as graduate and senior undergraduate students in computer science, Diffusion-Driven Wavelet Design for Shape Analysis presents recent research results in wavelet designs on 3D shapes and their applications in shape analysis. It explains how to apply the design methods to various types of 3D data, such as polygonal meshes, point clouds, manifolds, and volumetric images.

Extensions of Wavelet Generation on Volumetric and Manifold DataThe first part of the book introduces design methods of wavelets on manifold data, incorporating interdisciplinary knowledge from differential geometry, functional analysis, Fourier transform, spectral graph theory, and stochastic processes. The authors show how wavelets are purely determined by the shape geometry and how wavelet transforms are computed as inner products of wavelet kernels and input functions.

Wavelets for Solving Computer Graphics ProblemsThe second part presents applications in shape analysis/representation. The book looks at wavelets as spectral tools for geometry processing with filters in a joint space-frequency domain and examines wavelets as detail extractors for shape feature definition and detection. Going beyond these fundamental applications, the book also covers middle- and high-level applications, including shape matching, shape registration, and shape retrieval.

Easy-to-Understand Implementations and AlgorithmsUnlike many other wavelet books, this one does not involve complicated mathematics. Instead, the book uses simplified formulations and illustrative example

Hou, Tingbo; Qin, Hong

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