On a new multigrid algorithm for image segmentationMS22

Global and selective image segmentation are very important applications of image processing techniques. The latter is of particular importance in medical imaging. We introduce a new convex selective segmentation model which achieves results which could not previously be attained. We also introduce a new multigrid framework for solving this model, and a whole class of segmentation models with O(N) complexity.

This presentation is part of Minisymposium “MS22 - Mapping problems in imaging, graphics and vision (3 parts)
organized by: Ronald Lui (Chinese University of Hong Kong) , Ke Chen (University of Liverpool) .

Ke Chen (University of Liverpool)
Mike Roberts (University of Liverpool)
fast solvers, image segmentation, inverse problems, multigrid, nonlinear optimization, partial differential equation models, selective segmentation