In recent years there have been advances in the theory of nonlinear eigenvalue problems related to image processing and computer vision. The formulations of nonlinear transforms, related to one-homogeneous functionals, such as total-variation, has opened way to various applications of image decomposition, face fusion, denoising and more. Theory related to 1-Laplacian eigenvectors on graphs has contributed to better understanding of classification, segmentation and clustering methods. In addition, new numerical methods for solving these hard problems have been proposed. In this two-part minisymposium researchers will present their latest results and discuss future trends in this emerging field.

Theoretical analysis of flows estimating eigenfunctions of one-homogeneous functionals

Aujol Jean-Francois (University of Bordeaux)

Continuum limit of total variation defined on geometric graphs

Garcia Trillos Nicolas (Brown University)

Bias reduction in variational regularization

Camille Sutour (University Paris Descartes)

Organizers:

Gilboa Guy (Electrical Engineering Department, Technion)

Aujol Jean-Francois (University of Bordeaux)

Keywords:

image segmentation, machine learning, nonlinear optimization, partial differential equation models