Variational Regularization of 3D Data: Experiments with MATLAB®

✍ Scribed by Hebert Montegranario, Jairo Espinosa (auth.)

• Publisher: Springer-Verlag New York
• Year: 2014
• Tongue: English
• Leaves: 87
• Series: SpringerBriefs in Computer Science
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages i-x
3D Data in Computer Vision and Technology....Pages 1-4
Function Spaces and Reconstruction....Pages 5-17
Variational Methods....Pages 19-29
Interpolation. From One to Several Variables....Pages 31-43
Functionals and Their Physical Interpretations....Pages 45-49
Regularization and Inverse Theory....Pages 51-57
3D Interpolation and Approximation....Pages 59-67
Radial Basis Functions....Pages 69-81
Back Matter....Pages 83-85

✦ Subjects

Image Processing and Computer Vision; Math Applications in Computer Science; Simulation and Modeling