© 2003

Modeling and Inverse Problems in Imaging Analysis


Part of the Applied Mathematical Sciences book series (AMS, volume 155)

Table of contents

  1. Front Matter
    Pages i-xxii
  2. Introduction

    1. Bernard Chalmond
      Pages 1-19
  3. Spline Models

    1. Front Matter
      Pages 21-21
    2. Bernard Chalmond
      Pages 23-50
    3. Bernard Chalmond
      Pages 51-73
    4. Bernard Chalmond
      Pages 75-95
  4. Markov Models

    1. Front Matter
      Pages 97-97
    2. Bernard Chalmond
      Pages 99-112
    3. Bernard Chalmond
      Pages 113-122
    4. Bernard Chalmond
      Pages 123-145
    5. Bernard Chalmond
      Pages 147-174
  5. Modeling in Action

    1. Front Matter
      Pages 175-175
    2. Bernard Chalmond
      Pages 177-188
    3. Bernard Chalmond
      Pages 189-226
    4. Bernard Chalmond
      Pages 227-241
    5. Bernard Chalmond
      Pages 243-268
    6. Bernard Chalmond
      Pages 269-288
  6. Erratum

    1. Bernard Chalmond
      Pages 313-313
  7. Back Matter
    Pages 289-312

About this book


More mathematics have been taking part in the development of digital image processing as a science, and the contributions are reflected in the increasingly important role modeling has played solving complex problems. This book is mostly concerned with energy-based models. Through concrete image analysis problems, the author develops consistent modeling, a know-how generally hidden in the proposed solutions.

The book is divided into three main parts. The first two parts describe the theory behind the applications that are presented in the third part. These materials include splines (variational approach, regression spline, spline in high dimension) and random fields (Markovian field, parametric estimation, stochastic and deterministic optimization, continuous Gaussian field). Most of these applications come from industrial projects in which the author was involved in robot vision and radiography: tracking 3-D lines, radiographic image processing, 3-D reconstruction and tomography, matching and deformation learning. Numerous graphical illustrations accompany the text showing the performance of the proposed models.

This book will be useful to researchers and graduate students in mathematics, physics, computer science, and engineering.


3D LED Performance Tracking image analysis image processing optimization simulation

Authors and affiliations

  1. 1.Department of PhysicsUniversity of Cergy-PontoiseCergy-Pontoise CedexFrance

Bibliographic information

Industry Sectors
Finance, Business & Banking


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)