Atelier doctorant le 16 juin à ESIEE Paris

L'équipe A3SI du LIGM (unité mixte de recherche de l'Université Paris Est) organise un atelier doctorants le jeudi 16 juin de 13h30 à 15h00 à ESIEE PARIS (amphi 260). Trois doctorants exposeront leurs travaux :

Approche variationnelle pour la segmentation d'images angiographiques
Olivia Miraucourt, LIGM, ESIEE

Dans un premier temps, un bref tour d'horizon des méthodes variationnelles utilisées pour la segmentation sera présenté.
Nous proposerons un premier modèle qui inclut un a priori de tubularité dans les modèles variationnels de débruitage ROF et TV-L1.

Néanmoins, bien que ces modèles permettent de rehausser les vaisseaux dans l'image, ils ne permettent pas de les segmenter.

C'est pourquoi nous proposerons un deuxième modèle qui inclut à la fois un a priori de tubularité et un a priori de direction dans le modèle variationnel de segmentation de Chan-Vese. Des résultats sont fournis sur des images synthétiques 2D, ainsi que sur des images rétiniennes de la base DRIVE.

Shallow Packings and Geometry
Bruno Jartoux, LIGM, ESIEE

We introduce a combinatorial analogue of Euclidean sphere packing problems: Given a family of sets, one wants to find a largest subfamily whose sets have pairwise high symmetric difference (a packing).
When such set families arise from geometry it is often possible to significantly sharpen the upper bound on the cardinality of packings.

Haussler's bound on their size (1995) was recently refined (2014-2016). We discuss these results; in particular we prove their tightness by giving a simple construction of packings whose size reaches those bounds.

Exploiting image noise in digital image forensics
Thibault Julliand, LIGM, ESIEE

Noise is an intrinsic specificity of all forms of imaging, and can be found in various forms in all domains of digital imagery. We will present an overall review of digital image noise, from its causes and models to the degradations it suffers along the image acquisition pipeline. We will then show how noise can be exploited in image forensics.
Image forensics is based on detecting various manipulations on images. Here, we will focus on a specific method called image splicing. Image splicing is a common manipulation which consists in copying part of an image in a second image. Here, we will show how to exploit the variation in noise characteristics in spliced images, caused by the difference in camera and lighting conditions during the image acquisition. Our method automatically gives a probability of alteration for any area of the image, using a local analysis of noise density. We consider both Gaussian and Poisson noise components to modelize the noise in the image.