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.To alleviate this problem, we suggest using *M - -$$p. (12-12) ^Iaia#39;-wwa#39;alt;a#39;1 La#39; ( 1213) where (a, b) is the usual scalar product. These functional ... U(x\y). = aUiix). +. JVc/2/(/, 4. L. e. where a is the regularization parameter. Unlike all the energies anbsp;...

Title | : | Modeling and Inverse Problems in Imaging Analysis |

Author | : | Bernard Chalmond |

Publisher | : | Springer Science & Business Media - 2003-01-14 |

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