Texture segmentation still constitutes an ongoing challenge, especially when processing large-size images. In this contribution we focus on (i) extracting simultaneously characteristics such as local regularity and local variance, integrating a scale-free (or fractal) wavelet-leader modeling that allowed the problem to be reformulated in a convex optimization framework by including a Total Variation penalization and (ii) investigating the potential of block-coordinate strategies in order to deal with the memory and computational cost induced by the minimization.

This presentation is part of Minisymposium “MS78 - Recent developments in variational image modeling”
organized by: Sonia Tabti (Université de Caen, CNRS) , Rabin Julien (CNRS, Normandie Univ.) .

Authors:

Nelly Pustelnik (CNRS, Laboratoire de Physique de l'ENS de Lyon)

Patrice Abry (CNRS, Laboratoire de Physique de l'ENS de Lyon)

Pascal Barbara (Laboratoire de Physique de l'ENS de Lyon)

Keywords:

image segmentation, nonlinear optimization, statistical inverse estimation methods