Diffusion-Driven Wavelet Design for Shape Analysis

Tingbo Hou

ISBN 10: 0367378248 / ISBN 13: 9780367378240

Published by Taylor & Francis Ltd Okt 2019, 2019

About this Item

Neuware.

Seller Inventory # 9780367378240

Bibliographic Details

• Title: Diffusion-Driven Wavelet Design for Shape Analysis
• Publisher: Taylor & Francis Ltd Okt 2019
• Publication Date: 2019
• Language: English
• ISBN 10: 0367378248
• ISBN 13: 9780367378240
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 229W x 152H x 0D millimeters 229 millimeters width by 152 millimeters height by 0 millimeters depth
• Weight: 453 grams

About this title

Synopsis

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

About the Author

Hou, Tingbo; Qin, Hong