Xue-Cheng Tai / Knut-Andreas Lie / Tony F. Chan / Stanley Osher

Image Processing Based on Partial Differential Equations

Proceedings of the International Conference on PDE-Based Image Processing and Related Inverse Problems, CMA, Oslo, August 8-12, 2005
Herausgegeben:Tai, Xue-Cheng; Lie, Knut-Andreas; Chan, Tony F.; Osher, Stanley

• Gebundenes Buch

Produktbeschreibung

The book contains twenty-two original scienti?c research articles that address the state-of-the-art in using partial di?erential equations for image and signal processing. The articles arose from presentations given at the inter- tional conference on PDE-Based Image Processing and Related Inverse Pr- lems, held at the Centre of Mathematics for Applications, University of Oslo, Norway, August 8-12, 2005. The purpose of the conference was to bring together international - searchers to present various aspects of new developments in using numerical techniques for partial di?erential equations to analyse and process digital - ages. Various aspects of new trends and techniques in this ?eld were discussed in the conference, covering the following topics: - Level set methods and applications - Total variation regularization and other nonlinear ?lters - Noise analysis and removal - Image inpainting - Image dejittering - Optical ?ow estimation - Image segmentation - Image registration - Analysis and processing of MR images and brain mapping - Image construction techniques - Level set methods for inverse problems Inverse problems for partial di?erential equations have large areas of app- cations. Although image analysis and PDE inverse problems seem to be - related at a ?rst glance, there are many techniques used in one of these two areas that are useful for the other. One goal of the conference was to highlight some of the recent e?orts in merging some of the techniques for these two research areas.

Produktdetails

• Mathematics and Visualization
• Verlag: Springer / Springer Berlin Heidelberg / Springer, Berlin
• Artikelnr. des Verlages: 11693628, 978-3-540-33266-4
• 2007
• Seitenzahl: 452
• Erscheinungstermin: 1. Dezember 2006
• Englisch
• Abmessung: 241mm x 160mm x 29mm
• Gewicht: 783g
• ISBN-13: 9783540332664
• ISBN-10: 3540332669
• Artikelnr.: 22451890

Autorenporträt

Xue-Cheng Tai, University of Bergen, Norway / Knut-Andreas Lie, SINTEF, Norway / Tony F. Chan, The National Science Foundation, Arlington, USA / Stanley Osher, UCLA, Los Angeles, CA, USA

Inhaltsangabe

Digital Image Inpainting, Image Dejittering, and Optical Flow Estimation.- Image Inpainting Using a TV-Stokes Equation.- Error Analysis for H1 Based Wavelet Interpolations.- Image Dejittering Based on Slicing Moments.- CLG Method for Optical Flow Estimation Based on Gradient Constancy Assumption.- Denoising and Total Variation Methods.- On Multigrids for Solving a Class of Improved Total Variation Based Staircasing Reduction Models.- A Method for Total Variation-based Reconstruction of Noisy and Blurred Images.- Minimization of an Edge-Preserving Regularization Functional by Conjugate Gradient Type Methods.- A Newton-type Total Variation Diminishing Flow.- Chromaticity Denoising using Solution to the Skorokhod Problem.- Improved 3D Reconstruction of Interphase Chromosomes Based on Nonlinear Diffusion Filtering.- Image Segmentation.- Some Recent Developments in Variational Image Segmentation.- Application of Non-Convex BV Regularization for Image Segmentation.- Region-Based Variational Problems and Normal Alignment - Geometric Interpretation of Descent PDEs.- Fast PCLSM with Newton Updating Algorithm.- Fast Numerical Methods.- Nonlinear Multilevel Schemes for Solving the Total Variation Image Minimization Problem.- Fast Implementation of Piecewise Constant Level Set Methods.- The Multigrid Image Transform.- Minimally Stochastic Schemes for Singular Diffusion Equations.- Image Registration.- Total Variation Based Image Registration.- Variational Image Registration Allowing for Discontinuities in the Displacement Field.- Inverse Problems.- Shape Reconstruction from Two-Phase Incompressible Flow Data using Level Sets.- Reservoir Description Using a Binary Level Set Approach with Additional Prior Information About the Reservoir Model.