Bonjour à tous,

J'ai le plaisir de vous inviter à ma soutenance de thèse intitulée
``Tree-Based Shape Spaces: Definition and Applications in Image
Processing and Computer Vision''.
Celle-ci aura lieu le 12 Décembre 2013 à 14h00 dans l'amphithéâtre 260
de l'ESIEE Paris, située au 2, boulevard Blaise Pascal, Cité
Descartes, à Noisy-le-Grand (93).  Vous trouverez un plan d'accès à
l'école à l'adresse suivante :

            http://www.esiee.fr/Infos-pratiques/acces.php

La soutenance sera suivie d'un pot.

Composition du jury de thèse
----------------------------

Rapporteurs :
Philippe Salembier (Universitat Politècnica de Catalunya)
Lionel Moisan (Université Paris Descartes)

Examinateurs :
Jean Serra (ESIEE Paris)
Coloma Ballester (Universitat Pompeu Fabra)
Michael H.F. Wilkinson (Rijksuniversiteit Groningen)
Beatriz Marcotegui (Mines ParisTech)
Pascal Monasse (Ecole des Ponts ParisTech)

Directeurs de thèse :
Laurent Najman (ESIEE Paris - Université Paris-Est Marne-la-Vallée)
Thierry Géraud (EPITA)

Résumé de la thèse
------------------

This PhD work presents a versatile framework dealing with tree-based
image representations.  Those representations have been proved to be
very useful in a large number of applications. Notably, in
mathematical morphology, those representations form the basis for
efficient implementation of connected operators.

Connected operators are filtering tools that act by merging some
elementary regions of an image.  A popular filtering strategy relies
on (1) building a tree representation of the image; (2) computing a
shape-based attribute on each node of the tree; and (3) removing the
nodes for which the attribute is too low. This operation can be seen
as a thresholding of the tree, seen as a graph whose nodes are
weighted by the attribute function.

Rather than being satisfied with a mere thresholding, we propose to
expand on this idea.  We define the notion of tree-based shape spaces
as seeing a tree as a graph whose neighbourhood is given by the
parenthood relationship.  Then we apply some connected filters on such
shape spaces.  This simple processing, that we call "shape-based
morphology", has several deep consequences. Firstly, it is a
generalization of the existing tree-based connected
operators. Besides, it allows us to propose two new classes of
connected operators: shape-based lower/upper levelings and
shapings. Secondly, this framework can be used for object
detection/segmentation by selecting relevant nodes in the shape space.
Last, we can also apply this framework for transforming hierarchies
using extinction values, so that we obtain some hierarchical image
simplification and segmentation methods.

We have developed several applications using this framework.
- A first one is a truly contrast invariant local feature detection
method called Tree-Based Morse Regions (TBMR), which can be seen as a
variant of MSER with better performances.
- Some applications to retinal image analysis (including blood vessel
segmentation and optic nerve head segmentation) have been developed
using shape-based lower/upper levelings.
- We have also proposed an efficient image simplification
method by minimizing the Mumford-Shah functional, which belongs to the
shaping family.
- An object detection scheme have been developed by defining an
efficient context-based energy estimator.
- Last, we have proposed an extension of constrained connectivity
based on our novel hierarchy transforms.

Amicalement,
Yongchao Xu

-- 
Yongchao XU Ph.D student
EPITA Research and Development Laboratory (LRDE)
Laboratoire d'informatique de l'Institut Gaspard Monge (LIGM)
Équipe Algorithmes, Architectures, Analyse et synthèse d'images (A3SI)
Université Paris Est, Marne-la-Vallée 
France