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＜電子ブック＞
Modeling and Inverse Problems in Imaging Analysis / by Bernard Chalmond
(Applied Mathematical Sciences. ISSN:2196968X ; 155)

版	1st ed. 2003.
出版者	(New York, NY : Springer New York : Imprint: Springer)
出版年	2003
本文言語	英語
大きさ	XXII, 314 p : online resource
著者標目	*Chalmond, Bernard author SpringerLink (Online service)
件　名	LCSH:Mathematical models LCSH:Mathematics LCSH:Statistics  LCSH:Image processing -- Digital techniques  全ての件名で検索 LCSH:Computer vision LCSH:Mathematical physics FREE:Mathematical Modeling and Industrial Mathematics FREE:Applications of Mathematics FREE:Statistical Theory and Methods FREE:Computer Imaging, Vision, Pattern Recognition and Graphics FREE:Theoretical, Mathematical and Computational Physics
一般注記	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 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 HTTP:URL=https://doi.org/10.1007/978-0-387-21662-1

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