Modeling and Inverse Problems in Imaging Analysis

This book is devoted to energy-based modeling issues. It will be useful to researchers and graduate students in mathematics, computer science, physics, and engineering.

From the reviews:

"...This book is an excellent introduction to Bayesian imaging and spline models in image analysis. It can be used for courses aimed at both mathematical statisticians who want to learn more about applications to imaging and engineers who aim to incorporate adequate mathematical formalism into their research."-- MATHEMATICAL REVIEWS

"The introduction of this book clearly explains at a level any undergraduate student in mathematics can understand the basic concepts of image analysis. The examples throughout the book are well-explained and rich. The different types of modeling are also explained . this book can be advised to students or beginning researchers who want to have a good overview with an easy, self-contained introduction to the field of Image Analysis." (Peter Leoni, Physicalia, Vol. 28 (4-6), 2006)

Contenu
1 Introduction.- 1.1 About Modeling.- 1.2 Structure of the Book.- I Spline Models.- 2 Nonparametric Spline Models.- 3 Parametric Spline Models.- 4 Auto-Associative Models.- II Markov Models.- 5 Fundamental Aspects.- 6 Bayesian Estimation.- 7 Simulation and Optimization.- 8 Parameter Estimation.- III Modeling in Action.- 9 Model-Building.- 10 Degradation in Imaging.- 11 Detection of Filamentary Entities.- 12 Reconstruction and Projections.- 13 Matching.- References.- Author Index.