PAPADAKIS, Nicolas; RABIN, Julien

doi:10.1007/s10851-017-0725-5

Métadonnées

Afficher la notice complète

Licence d’utilisation du document

PAPADAKIS, Nicolas
Institut de Mathématiques de Bordeaux [IMB]

RABIN, Julien
Equipe Image - Laboratoire GREYC - UMR6072

Langue

Article de revue

Ce document a été publié dans

Journal of Mathematical Imaging and Vision. 2017

Springer Verlag

Résumé en anglais

We investigate in this work a versatile convex framework for multiple image segmentation, relying on the regularized optimal mass transport theory. In this setting, several transport cost functions are considered and used to match statistical distributions of features. In practice, global multidimensional histograms are estimated from the segmented image regions and are compared to reference models that are either fixed histograms given a priori, or directly inferred in the non-supervised case. The different convex problems studied are solved efficiently using primal--dual algorithms. The proposed approach is generic and enables multiphase segmentation as well as co-segmentation of multiple images.< Réduire