Modeling and Inverse Problems in Image Analysis

Bernard Chalmond

332 pages, parution le 11/02/2003

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Résumé

More mathematicians 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 materials necessary to the models expressed 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 models come from industrial projects in which the author was involved in robot vision and radiography: tracking 3D lines, radiographic image processing, 3D reconstruction and tomography, matching, 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 applied mathematics, computer vision, and physics.

Contents

Spline Models

Nonparametric Spline Models
Parametric Spline Models
Auto-Associative Models

Markov Models

Fundamental Aspects
Bayesian Estimation
Simulation and Optimization
Parameter Estimation

Modeling in Action

Model-Building
Degradation in Imaging
Detection of Filamentary Entities
Reconstruction and Projections
Matching

L'auteur - Bernard Chalmond

Ecole Normale Supérieure de Cachan, France

Caractéristiques techniques

	PAPIER
Éditeur(s)	Springer
Auteur(s)	Bernard Chalmond
Parution	11/02/2003
Nb. de pages	332
Format	16 x 24
Couverture	Relié
Poids	608g
Intérieur	Noir et Blanc
EAN13	9780387955476
ISBN13	978-0-387-95547-6