Overview
- Includes supplementary material: sn.pub/extras
Part of the book series: SpringerBriefs in Computer Science (BRIEFSCOMPUTER)
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Table of contents (8 chapters)
Keywords
About this book
Variational Regularization of 3D Data provides an introduction to variational methods for data modelling and its application in computer vision. In this book, the authors identify interpolation as an inverse problem that can be solved by Tikhonov regularization. The proposed solutions are generalizations of one-dimensional splines, applicable to n-dimensional data and the central idea is that these splines can be obtained by regularization theory using a trade-off between the fidelity of the data and smoothness properties.
As a foundation, the authors present a comprehensive guide to the necessary fundamentals of functional analysis and variational calculus, as well as splines. The implementation and numerical experiments are illustrated using MATLAB®. The book also includes the necessary theoretical background for approximation methods and some details of the computer implementation of the algorithms. A working knowledge of multivariable calculus and basic vector and matrix methods should serve as an adequate prerequisite.
Authors and Affiliations
Bibliographic Information
Book Title: Variational Regularization of 3D Data
Book Subtitle: Experiments with MATLAB®
Authors: Hebert Montegranario, Jairo Espinosa
Series Title: SpringerBriefs in Computer Science
DOI: https://doi.org/10.1007/978-1-4939-0533-1
Publisher: Springer New York, NY
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Author(s) 2014
Softcover ISBN: 978-1-4939-0532-4Published: 14 March 2014
eBook ISBN: 978-1-4939-0533-1Published: 14 March 2014
Series ISSN: 2191-5768
Series E-ISSN: 2191-5776
Edition Number: 1
Number of Pages: X, 85
Number of Illustrations: 21 b/w illustrations
Topics: Image Processing and Computer Vision, Math Applications in Computer Science, Simulation and Modeling