2 edition of **Variational methods in imaging** found in the catalog.

Variational methods in imaging

- 49 Want to read
- 40 Currently reading

Published
**2009** by Springer in New York, NY .

Written in English

- Imaging systems,
- Variational principles

**Edition Notes**

Includes bibliographical references (p. 287-307) and index.

Statement | Otmar Scherzer ... [et al.]. |

Series | Applied mathematical sciences -- v. 167, Applied mathematical sciences (Springer-Verlag New York Inc.) -- v. 167. |

Contributions | Scherzer, Otmar, 1964- |

Classifications | |
---|---|

LC Classifications | TK8315 .V37 2009 |

The Physical Object | |

Pagination | xiii, 320 p. : |

Number of Pages | 320 |

ID Numbers | |

Open Library | OL23677059M |

ISBN 10 | 0387309314, 0387692770 |

ISBN 10 | 9780387309316, 9780387692777 |

LC Control Number | 2008934867 |

B. Book Chapters and , PDE-based Algorithms for Simultaneous Image Registration and Segmentation, book chapter in Geometric Level Set Methods in Imaging, Vision and Graphics, Springer Verlag, (), , Characterization of Diﬀusion Anisotropy in DWI, book chapter in Handbook of.

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This book is devoted to the study of variational methods in imaging. The presentation is mathematically rigorous and covers a detailed treatment of the approach from an inverse problems point of view.

Key Features: Introduces variational methods with motivation Variational methods in imaging book the deterministic, geometric, and stochastic point of view. Variational Methods in Imaging (Applied Mathematical Sciences Book ) - Kindle edition by Otmar Scherzer, Markus Grasmair, Harald Grossauer, Markus Haltmeier, Frank Lenzen.

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This book is devoted to the study of variational methods in imaging. The presentation is mathematically rigorous and covers a detailed treatment of the approach from an inverse problems point of view.

Key Features: Introduces variational methods with motivation from the deterministic, geometric,Brand: Springer-Verlag New York. With a focus on the interplay between mathematics and applications of imaging, the first part covers topics from optimization, inverse problems and shape spaces to computer vision and computational anatomy.

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Chapter 9 of this book is available open access under a CC BY license at With a Variational methods in imaging book on the interplay between mathematics and applications of imaging, the first part covers topics from optimization, inverse problems and shape spaces to computer vision and computational anatomy.

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To motivate the general approach and possibilities of variational methods in mathematical imaging, we begin with several examples. This is a preview of subscription content, log in to check access. References.

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It covers all aspects of variational methods in image processing, with essential applications. Homework problems are also given at the end of each chapter. Buy Variational Methods in Imaging (Applied Mathematical Sciences) Softcover reprint of hardcover 1st ed.

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Variational Methods in Imaging With 72 Figures &Sprringei r. Contents Part I Fundamentals of Imaging 1 Case Examples of Imaging 3 Denoising 3 Chopping and Nodding 6 Image Inpainting 8 X-ray-Based Computerized Tomography 10 Recent Topics on Denoising with Variational Methods 5 Variational Calculus for Non-convex.

Variational methods and effective algorithms for imaging and vision. in medicine, the sciences, and technology, in the public and private sector equally.

The rapid development of new imaging hardware, the advance in medical imaging, the advent of multi-sensor data fusion and multimodal imaging, as well as the advances in computer vision.

"This book will be immensely useful both as a reference and textbook, as it presents the fundamentals of variational methods in image processing. It covers all aspects of variational methods in image processing, with essential applications.

Homework problems are also given at the end of each chapter. Part I: Fundamentals of Imaging.- Case examples of imaging.- Image and Noise Models.- Part II: Regularization.- Variational Regularization Methods for the Solution of Inverse Problems.- Convex Regularization Methods for Denoising.- Variational Calculus for Non-convex Regularization.- Semi-group Theory and Scale Spaces.- Inverse Scale Spaces A comprehensive guide to using energy principles and variational methods for solving problems in solid mechanics.

This book provides a systematic, highly practical introduction to the use of energy principles, traditional variational methods, and the finite element method for the solution of engineering problems involving bars, beams, torsion, plane elasticity, trusses, and plates. Variational Methods for Multimodal Image Matching Article (PDF Available) in International Journal of Computer Vision 50(3) December with Reads How we.

His research focuses on applied and computational mathematics, in particular variational methods for imaging problems, numerical optimization and analysis, and computational problems in machine learning. Xuanqin Mou is a Professor with Xi'an Jiaotong University. He is the Director of the National Data Broadcasting Engineering and Technology.

Variational methods for the numerical solution of nonlinear elliptic problems / Roland Glowinski, University of Houston, Houston, Texas.

pages cm. -- (CBMS-NSF regional conference series in applied mathematics ; 86) Includes bibliographical references and index. ISBN 1. Nonlinear functional analysis.

Elliptic functions. Scale Space and Variational Methods in Computer Vision, () Efficient segmentation and correction model for brain MR images with level set framework based on basis functions. Magnetic Resonance Imag Cited by: J.-M.

Morel and S. Solimini, Variational Methods in Image Segmentation: With Seven Image Processing Experiments (Progress in Nonlinear Differential Equations and Their Applications), Birkhauser S. Osher and R. Fedkiw, Level Set Methods and Dynamic Implicit Surfaces, Springer-Verlag, Variational Methods in Imaging (Applied Mathematical Sciences Book ) eBook: Otmar Scherzer, Markus Grasmair, Harald Grossauer, Markus.

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The Issues The Context Imaging Thin Structures Micromagnetics Foams Mumford-Shah model Staircasing in Imaging Inpainting. This book is issued from a 30 years experience on the presentation of variational methods to successive generations of students and researchers in Engineering.

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It aims at presenting an interplay between research in applied mathematics (PDE’s, optimization, inverse problems, optimal transport, shape spaces) and applications in imaging (image processing, computer vision. Handbook of Mathematical Methods in Imaging / The Handbook of Mathematical Methods in Imaging provides a comprehensive treatment of the mathematical techniques used in imaging science.

The material is grouped into two central themes, namely, Inverse Problems (Algorithmic Reconstruction) and Signal and Image Processing. Variational Methods in Image Processing (Chapman & Hall/CRC Mathematical and Computational Imaging Sciences Series) by Luminita A.

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Vese; Carole Le Guyader. CRC Press LLC, Hardcover. Very Good. Disclaimer:A copy that has been read, but remains in excellent condition. Pages are intact and are not marred by notes or highlighting, but may contain a neat previous owner name.

The spine remains undamaged. At ThriftBooks, our motto is: Read. This book constitutes the refereed proceedings of the 4th International Conference on Scale Space Methods and Variational Methods in Computer Vision, SSVMheld in Schloss Seggau near Graz, Austria, in June The 42 revised full papers presented were carefully reviewed and selected 69 submissions.

Variational Methods. Introduction MotivationE-LPDE Outline 1 Introduction Motivation Derivation of Euler-Lagrange Equation Variational Problem and P.D.E.

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