Modeling and Inverse Problems in Imaging Analysis (Hardcover, 2003 ed.)

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.

General

Imprint:	Springer-Verlag New York
Country of origin:	United States
Series:	Applied Mathematical Sciences, 155
Release date:	2003
First published:	2003
Translators:	Kari A. Foster
Authors:	Bernard Chalmond
Dimensions:	235 x 155 x 20mm (L x W x T)
Format:	Hardcover
Pages:	314
Edition:	2003 ed.
ISBN-13:	978-0-387-95547-6
Categories:	Books > Science & Mathematics > Mathematics > Applied mathematics > Mathematical modelling Books > Computing & IT > Applications of computing > Image processing > General Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Applied optics > General
LSN:	0-387-95547-X
Barcode:	9780387955476

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