Images and Finite ElementsMS64

Images are most often represented by pixel data on Cartesian grids. However, finite element models may be preferred in certain situations. Examples include images on triangulated surfaces, or when higher-order representations and adaptivity play a role. Choosing a finite element discretization has interesting implications on the algorithmic solution of image restoration and related problems as well as their duals, which will be highlighted by the speakers in this minisymposium.

Discrete total variation with finite elements
Roland Herzog (Technische Universität Chemnitz)
Adaptive finite element approximation of the ROF model
Marijo Milicevic (University of Freiburg)
Fast and robust boundary segmentation using 2nd order shape sensitivity of variational models
Gunay Dogan (Theiss Research, NIST)
Adaptive finite elements for Mumford-Shah-type functionals in transport network modelling
Rossmanith Carolin (Westfälische Wilhelms-Universität Münster)
Roland Herzog (Technische Universität Chemnitz)
Stephan Schmidt (University of Würzburg)
finite elements, image reconstruction, nonlinear optimization, numerical linear algebra