The
The scientific focus of the laboratory is the combined study of computer
and biological vision. We think that a more detailed knowledge of the
visual perception in humans and non-human primates can have a potential
impact on algorithm design, performance evaluation and cues on such questions
as how to interface an artificial vision system with people, possibly handicapped.
From a more general viewpoint and at another level, biological visual perception, in particular in non human primates and humans is poorly understood and modeled. Making progress in this understanding is a grand scientific and philosophic challenge that frames our work.
We conduct research in the following three main areas.
A
We are interested in using variational methods and partial differential equations because they are the tools that allow us to
Mathematically model a large number of computer vision problems such as segmentation, stereo, motion analysis or shape recognition.
Study the existence and uniqueness of solutions.
Design efficient algorithms for approximating those solutions.
Within this general framework, we are interested in the following two main areas
Feature integration, as seen from the algorithmic and biological viewpoints. We are currently investigating:
Shape from shading, to integrate occluding edges, shadows and
textures. This work is done in the context of the theory of viscosity
solutions.
Stereo, a problem that also requires the integration of occluding
contours, shadows and textures.
The combination of stereo and motion.
The combination of color, texture and motion for image segmentation.
Shape representation and learning. We investigate the problem of acquiring geometric models from image sequences from the algorithmic and biologic viewpoints. We have proposed a number of mathematical formulation of this problem. We work on 3D deterministic and stochastic shapes representation and learning. Results can be included in the work on feature integration and are potentially useful for guiding segmentation and recognition.
Brain imaging is a well-adapted tool to improve our knowledge of brain functioning, in particular of visual perception. Challenging computer vision problems can also be posed by this type of imagery. The evolution of technology gives access to ever increasing spatio-temporal resolutions, resulting in the measurement of cortical areas whose sizes are now compatible with our modelization tools. We focus on the following modalities, Magnetic Resonance (MR), electroencephalography (EEG) and magnetoencephalography (MEG).
In the case of MR, we use:
Functional magnetic resonance images (fMRI).
This is an indirect way of measuring brain activity through the Blood Oxygenation Level Dependent (BOLD) signal which is thought to be correlated with neuronal activity. Spatial resolution is, at best, of the order of one millimeter while temporal resolution is of the order of a tenth of a second.
Diffusion tensor magnetic resonance imagery (DTMRI).
This particular modality provides a measure of the diffusion of water molecules in tissues from which one can infer the geometry of the neural fibers connecting various areas of the brain (anatomical connectivity). This measure is also correlated to the electrical conductivity.
Anatomical magnetic resonance imagery (aMRI)
This can provide, through an algorithmic process called segmentation, a valuable geometric description of brain areas, e.g. the cortex, the white matter, the cerebrospinal fluid, etc...
EEG and MEG, which we note MEEG, provide measurements which are highly correlated to the electrical activity of the brain. The spatial resolution is of the order of one centimeter while the temporal resolution is of the order of one millisecond.
These three modalities are complementary from the standpoint of their spatio-temporal resolutions and of the kind of information they can deliver. We work in the following six areas.
Spatio-temporal modeling of fMRI signals to obtain more accurate and more detailed cortical activity maps than those provided by the currently available software packages.
Spatio-temporal modeling of MEEG signals.
The use of DTMRI to describe anatomical connectivities and improve the existing models of electrical conductivities currently used in MEEG.
The analysis of the well-posedness of the inversed problem in MEEG, i.e. the existence and uniqueness of a particular brain activity that best accounts for the EEG/MEG measurements.
The development of new numerical methods for solving the MEEG problem.
The integration of these three modalities for studying visual perception in humans and non human primates.
We use the results obtained within our second main research area to model the way the brain completes a visual perception task at a more general level than the "voxel" level. The spatio-temporal activity maps measured on humans and non human primates play an important role in this modeling which carries two main advantages. First we push forward the state of the art of the knowledge of the brain processes underlying visual perception and second we may discover interesting sources of inspiration for our work in computer vision.
We also study cortical activity at a finer level than the "voxel" by fitting models of neurones assemblies to the measurements provided by fMRI, MEEG and possible micro-electrodes. We have two targets
The use of cortical columns models to analyze the cortical activity measured by fMRI or MEEG goes beyond the use of the classical models of electric dipoles and opens up new perspectives as to what algorithmic complexity underlies this activity. This is also likely to have an impact on our understanding of the inverse problem in MEEG and therefore on the way we solve it.
Such a description is closely related to the formalism of dynamic systems and, perhaps, partial differential equations. This is a fascinating possibility of connecting with our first main research area.
In many medical computer vision tasks, the relevant data is attached
to a specific tissue such as the cortex or the colon. This situation
calls for regularization techniques which are defined over non flat
surfaces. We have introduced the Beltrami flow over
manifolds. This new regularization technique overcomes the
over-smoothing of the
In this work, we propose a novel and efficient approach for active
unsurpervised texture segmentation. First, we show how we can extract
a small set of good features for texture segmentation based on the
structure tensor and nonlinear diffusion. Then, we propose a
variational framework that incorporates these features in a level set
based unsupervised segmentation that adaptively takes into account
their estimated statistical information inside and outside the region
to segment. The approach has been tested on various textured images,
and its performance compares favorably with recent studies
In this work we integrate colour, texture, and motion into a
segmentation process. The segmentation consists of two steps, which
both combine the given information: a pre-segmentation step based on
nonlinear diffusion for improving the quality of the features, and a
variational framework for vector-valued data using a level set
approach and a statistical model to describe the interior and the
complement of a region. For the nonlinear diffusion we apply a novel
diffusivity closely related to the total variation diffusivity, but
being strictly edge enhancing. A multi-scale implementation is used in
order to obtain more robust results. In several experiments we
demonstrate the usefulness of integrating many kinds of
information. Good results are obtained for both object segmentation
and tracking of multiple objects
In this work, we re-visit active shape models, a popular technique to
object extraction, and introduce a level set variant of them. Such an
approach can account for prior shape knowledge quite efficiently as
well as use data/image terms of various form and complexity while
being able to deal with important local deformations and changes of
topology. Promising experimental results demonstrate the potential of
our approach
In this work, we address the problem of vector-valued image
regularization with variational methods and PDE's. From the study of
existing global and local formalisms, we propose a new framework that
unifies a large number of previous methods within a generic local
formulation. On one hand, resulting equations are more adapted to
analyze the local geometric behaviors of the diffusion processes. On
the other hand, it can be used to design a new regularization PDE that
takes important local smoothing properties into account. Specific
numerical schemes are also naturally emerging from this
formulation. Finally, we illustrate the capability of our approach to
deal with classical image processing applications, such as color image
restoration, inpainting, magnification and flow visualization. This
work has been awarded by the best student paper at CVPR'2003
We present a common variational framework for dense depth recovery and
dense three-dimensional motion field estimation from multiple video sequences,
which is robust to camera spectral sensitivity differences and
illumination changes.
For this purpose, we first show that both problems reduce to a generic
image matching problem after backprojecting the input images onto suitable
surfaces.
We then solve this matching problem in the case of statistical
similarity criteria that can handle frequently occurring non-affine image
intensities dependencies.
Our method leads to an efficient and elegant implementation based on
fast recursive filters. The computation time is of a few seconds per frame for
medium resolution reconstructions. We obtain good results both
on real and synthetic images. Preliminary results have been published in
In this work, we overcome a major drawback of the level set framework:
the lack of point correspondences.
We maintain explicit backward correspondences from the evolving interface to
the initial one by advecting the initial point coordinates with the same speed
as the level set function.
Our method leads to a system of coupled Eulerian partial differential equations.
We show in a variety of numerical experiments that it can handle both normal and
tangential velocities, large deformations, shocks, rarefactions and topological
changes.
Applications are many in computer vision and elsewhere since our method can upgrade
virtually any level set evolution.
We complement our work with the design of non zero tangential velocities
that preserve the relative area of interface patches; this feature
may be crucial in such applications as computational geometry, grid generation
or unfolding of the organs' surfaces, e.g. brain, in medical imaging. Preliminary results have been published in
In this work we propose a framework for dealing with several problems related
to the analysis of shapes. Two related such problems are the definition of the relevant
set of shapes and that of defining a metric on it. Following a recent research monograph by Delfour and Zolesio
This works presents a novel framework for image segmentation based on
stochastic optimization. During
the last few years, several segmentation methods have been
proposed to integrate different information in a variational
framework, where an objective function depending on both boundary
information and region information is minimized using a
gradient-descent method. Some recent methods are even able to
extract the region model during the segmentation process itself.
Yet, in complex cases, the objective function does not have any
computable gradient. In other cases, the minimization process gets
stuck in some local minimum, while no multi-resolution approach
can be invoked. To deal with those two frequent problems, we
propose a stochastic optimization approach and show that even a
simple Simulated Annealing method is powerful enough in many
cases. Based on recent work on Stochastic Partial Differential
Equations (SPDEs), we propose a simple and well-founded method to
implement the stochastic evolution of a curve in a Level Set
framework. In
We address three crucial issues encountered in DT-MRI (Diffusion
Tensor Magnetic Resonance Imaging) : diffusion tensor Estimation,
Regularization and fiber bundle Visualization. We first review related
algorithms existing in the literature and propose then alternative
variational formalisms that lead to new and improved schemes, thanks
to the preservation of important tensor constraints (positivity,
symmetry). We illustrate how our complete DT-MRI processing pipeline
can be successfully used to construct and draw fiber bundles in the
white matter of the brain, from a set of noisy raw MRI images
In this work, we introduce a novel approach to the cerebral white
matter connectivity mapping from diffusion tensor MRI. DT-MRI is the
unique non-invasive technique capable o f probing and quantifying the
anisotropic diffusion of water molecules in biolog ical tissues. We
address the problem of consistent neural fibers reconstruction in
areas of complex diffusion profiles with potentially multiple fibers
orientat ions. Our method relies on a global modelization of the
acquired MRI volume as a Riemannian manifold
Numerical experimentations conducted on synthetic and real diffusion MRI datasets illustrate the potentialities of this global approach.
This work investigates the benefits of using a superresolution
approach for fMRI sequences in order to obtain high-quality activation
maps based on low-resolution acquisitions. We propose a protocol to
acquire low-resolution images, shifted in the slice direction, so that
they can be used to generate superresolution images. Adopting a
variational framework, the superresolution images are defiend as the
minimizers of objective functions. We focus on edge preserving
regularized objective functions because of their ability to preserve
details and edges. We show that applying regularization only in the
slice direction leads more pertinent solutions than 3-dimensional
regularization. Moreover, it leads to a considerably easier
optimization problem. The latter point is crucial since we have to
process long fMRI sequences. The solutions—the sought high
resoltion images—are calculated based on a half-quadratic
reformulation of the objective function which allows fast minimization
schemes to be implemented. Our acquisition protocol and processing
technique are tested both on simulated and real functional MRI
datasets
Continuing on the volumic approach for the M/EEG problem, an important milestone has been achieved this year with the validation of the adjoint state approach for the EEG problem. This allows for a very fast computation of the gradient of the potential function with respect to the source distribution. This gradient being validated a first simple version of the inverse problem (using a fixed step gradient descent and without regularization) has been implemented and tested and already shows interesting results. A more sophisticated approach using a conjugate gradient, an optimal step and some regularisation constraints is being explored.
The forward electro-encephalography (EEG) problem involves finding a potential
Following the recent breakthrough in the forward MEG/EEG problem described in , we propose and compare two methods for localizing cortex activity from EEG/MEG measurements.
In our BEM formulation, electrical activity of the brain is assumed to be
mainly concentrated on the surface of the cortex, this can be biologicaly justified for a large domain of
studies of the brain. Thus, sources are modelized by dipole distribution (or dipole density) all over the
surface of the cortex. The arising forward problem is then linear. The inverse problem is solved as a
constrained reconstruction problem adressed by a PDE. This PDE contains two terms: a data-driven
one (least square) and a regularization one. The last implies surface laplacian and can be treated in two
ways: firstly in deriving numerical schemes over a triangulated surface, it allows to keep on working
on the mesh, where the data-driven term is defined. Secondly, we can embed the mesh into a grid and
use a well-studied level set method developed by Bertalmio, Sheng, Osher, and Sapiro; this brings simple, flexible and
well-known numerical schemes. Regularization type is a critical point in data-driven recontruction.
Whereas the minimal norm constraint may deliver over-scattered source of activity, isotropic filtering
force electrical activity to have low spatial gradients. Finaly, anisotropic filtering seek minimal total
variation solutions, this implies well-delimited active zones on the cortex (typically piecewise
constant)
We have transferred the work we have been doing in this area during the last
two years to our Leuven partners in the
This work presents a biologically motivated model for low and mid-level vision tasks, as well as its interpretations in computer vision terms. As a starting point we briefly present the biologically plausible model of image segmentation developed by Stephen Grossberg and his collaborators during the last two decades, that has served as the backbone of many researchers' work. Subsequently we describe a novel version of this model with a simpler architecture but superior performance to the original system using nonlinear recurrent neural dynamics. This model integrates multi-scale contour, surface and saliency information in an efficient way, and results in smooth surfaces and thin edge maps, without any posterior edge thinning or some sophisticated thresholding process. When applied to both synthetic and true images this model gives satisfactory results, favorably comparable to those of classical computer vision algorithms. Analogies between the functions performed by this system and some commonly used techniques for low- and mid-level computer vision tasks are presented; by interpreting the network as minimizing a cost functional, links with the variational approach to computer vision are established.
Regarding biological visual classification, recent series of experiments have
enlighten that data classification can be realized in the human visual cortex
with latencies of about 100 ms, which, considering the visual pathways
latencies, is only compatible with a very specific processing architecture,.
Surprisingly enough, this experimental evidence is in coherence with
algorithms derived from the statistical learning theory, following the work of
Vapnik. The present contribution develops this idea and experiments its
performances using a tiny sign language recognition
experiment.
Considering the biological or artificial control of a trajectory generation,
we also propose a biologically plausible model based on harmonic potentials,
using a related framework.
Another aspect of this study aims at proposing an implementation of
regularization mechanisms compatible with biological operators. More
precisely, cortical maps code vectorial parametric quantities, computed by
network of neurons. In computer vision, similar quantities are efficiently
computed using implementations of partial differential equations which define
regularization processes, allowing to obtain well-defined estimations of these
quantities.
Rotoscoto is a
This three year grant started in 2001 from the French Ministery of Research is geared towards the study of the direct and inverse problems in Electro- and Magneto-Encephalography. The project is coordinated by INRIA and the participants are the Cermics in Marne-la-Vallée (ENPC), the "La Timone" hospital in Marseille and the Technologic University in Compiègne. INRIA's participants are the projects Estime, Gamma, Ondes and Odyssée.
This three year grant has been funded in 2003. Its main purpose is to make progresses toward a virtual meta-sensor combining the advantages of the various non-invasive sources of information about the brain activity. This involves manipulating and linking the information provided by some very large heterogenous data sets such as MEG and EEG or various types of MRI images.
Thanks to a financial support of the
More precisely, a double link is being developed: on one hand, statistics provide tools to evaluate and analyze the Thorpe model performances and on the other hand, this model is an interesting front-end for algorithms derived from the statistical theory. The present contribution develops this idea and experiments its performances using a tiny sign language recognition experiment.
In a second part of the project, we are considering motion sequences.
The goal of this
We consider the comparative study of visual process integration within either a biological system, i.e. the parieto-ventral and parieto-dorsal pathways of the cortical visual system in the primate or an artificial system. Both systems deliver an estimation of [where], that is to say the motion and structure of the observed scene and of [what], i.e. the perceptual grouping and labeling of objects in the scene. Within this framework, the function and behavior of adaptive feedback mechanisms is a key point and on the leading edge of biological studies.
Within the scope of the 3rd topics of this ACI, this project consists in three steps:
(a) A systematic analysis of existing results in neuro-science,
(b) an interpretation of these results from the viewpoint of the variational approach widely used in computer vision
(c) a specification of a simulation tool of parts of the visual cortex, the actual development of this simulator being the goal of a second phase after this pre-project.
The contributors are: ENS Cachan (CMLA), INT (ARTEMIS), IMAG TIMC (GMCAO), INRIA (Epidaure, Odyssée), INSA (CREATIS), IRISA (Projet Vista), Louis Pasteur University (LSIIT), Paris V University (MAP5) and Paris XIII University (LAGA).
Thomas Brox, from Department of Mathematics and Computer Science, Saarland University and Iasonas Kokkinos from National University of Athens spent 4 months each in our lab in 2002/2003.
Duration: 2000-2003
Our partners in this project can be found on the projects'
Preprocessing of Magnetic resonance data (motion compensation, resolution increase), and registration of intra- and inter subject data.
The development of new techniques for generating maps of cortical activity.
The study of functional connectivity between active cortical areas.
The comparison between visual perception in man and non-human primates.
Our partners are :
INSIGHT2+ deals with 3D shape and material properties for recognition. This European project is funded under the Information Society Technologies (IST) Programme and its duration is from Sept. 2001 to September 2004. The objectives of the project are to
Restore the cross fertilization between biological vision (neuroscience and psychophysics) and computer vision.
Study the coding of pictorial cues for 3D shape and of material properties in areas TE and V4.
Search for grouping of properties in either area and relate to connectivity.
Study the perception of 3D shape defined by pictorial cues and of material properties.
Develop mathematical theories necessary for implementation of pictorial cue and material processing in computers.
Implementation of the theory to create a flexible system.
The partners are :
The challenge addressed in this project is to build a vision system that can be used in a wider variety of fields and that is re-usable by introducing self-adaptation at the level of perception and by making explicit the knowledge base at the level of reasoning, and thereby enabling the knowledge base to be changed. In order to make these ideas concrete CogViSys aims at developing a virtual commentator which is able to translate visual information into a textual description.
R. Deriche has been in charge of the International Affairs for the
INRIA Sophia Research Unit since 1996, see the corresponding
T. Viéville has been, with B. Mourrain, in charge of the
R. Deriche is a member of the advisory board of
R&D Plan Régional Textile Habillement de la région Nord . He is also
an expert member in the framework of the national project Réseau
National de Recherche en Télécommunications. . R. Deriche is a
member of the editorial board of the Journal
Project Leaders Committee of I3S and is a
member of the Expert Commission CS 61 Génie informatique,
automatique et traitement du signal at Nice Sophia-Antipolis
University. R. Deriche has been involved in many PhD thesis committees
as chairman, reviewer or examiner.
Olivier Faugeras is a member of the Institut de France, Académie des Sciences. He is co-editor in chief of the "International Journal of Computer Vision" (IJCV). He has been invited to a number of companies scientific advisory boards such as RealViZ and VISIONIQ. He chairs the scientific board of the "Institut Français du Pétrole" (IFP).
P. Kornprobst is a member of the "Comité de Centre" and the "Comité Local de Formation Permanente" of the Sophia-Antipolis INRIA Research Unit.
Théo Papadopoulo is part of the "Expert Commission CS 27" at Nice-Sophia Antipolis University. He is also a member of several committees of the Sophia-Antipolis INRIA Research Unit such as the "laboratory committee", of the "Software development committee", of the "educational committee" and of the "computer system users committee" of the Sophia research unit.
T. Viéville helps the
Nice-Sophia Antipolis University - Image and Vision track:
Rachid Deriche teaches the Advanced Techniques in Image Processing and Vision course,
Olivier Faugeras teaches the Computer and biological Vision course,
Théodore Papadopoulo teaches the 3D Vision course.
Thierry Viéville teaches the biological and computer
models of Motion perception course.
Each course includes 15 hours of lectures. Rachid Deriche and Olivier Faugeras are members of the scientific committee of this DEA. Rachid Deriche is in charge of the internships program.
Ecole Normale Supérieure Cachan - Ecole Normale Supérieure Ulm - ENST - École Polytechnique - Paris-Nord University - Paris-Dauphine University - Paris 5 university - ECP - ENPC.
Olivier Faugeras teaches the
Théodore Papadopoulo teaches the
Paris Sud University - Centre Scientifique d'Orsay - Paris I University - Panthéon Sorbonne in collaboration with INRIA.
Rachid Deriche teaches jointly with Hervé Delingette (Epidaure project) the
Modélisation
Géométrique et Physique à partir d'images pour la réalité augmentée et
virtuelle course (21 hours).
Jointly with Ecole Normale Supérieure Paris, Ecole Normale Supérieure Cachan, Paris VI , Paris VII and Paris XI Universities, E.N.S.T.and École Polytechnique, INRIA and ENSTA.
Rachid Deriche teaches the
Pierre Kornprobst teaches the traitement d'images course at Nice University.
Renaud Keriven teaches the Computer Vision course at the Marne la Vallée University.
Paris VI, VII, IX, XI, XIII Universities and Ecole Normale Supérieure: Renaud Keriven teaches the Computer Vision course.
École d'Ingénieurs - third year - Option: Traitement et Applications de l'Image. Rachid Deriche teaches the Computer Vision and Dynamic Images courses.
Maureen Clerc is in charge of and teaches part of two courses in the curriculum: Mathematics and Vision, and Frequency analysis and applications.
Renaud Keriven is in charge of the main Computer Science course and of the two specialisation modules "Modéliser/Programmer/Simuler" and "Vision et Traitement d'Images".
Rachid Deriche teaches Computer Vision at the École des Mines de Paris and at ENSTA.
Thierry Viéville teaches a course on Symbolic Computation.
Thierry Viéville teaches Real-Time Motion Perception.
Rachid Deriche and Théo Papadopoulo were local co-chairs of the
Ninth International Conference on Computer Vision
Rachid Deriche is program co-chair of the 2nd IEEE Workshop
He is a member of several program committees such as
He has been invited by Facultad de Ingeniería, Montevideo (Uruguay) in march 2003. He gave several lectures on PDE's and Level-Sets in Image Processing and Computer Vision at the Departamento de Control y Electrónica Industrial, Instituto de Ingeniería Eléctrica. He visited differents labs. and discussed with several members of the team lead by Prof. G. Randall. He initiated a close collaboration with this group and the venue of Pablo Cancela for 4 months was planned during that time.
He has been invited by Ecole Nationale des Sciences de
l'Informatique in Tunis (Tunisia) late january 2003. He stayed
at the ENSI school for a week and gave a series of lectures in geometrical
approaches in computer vision and image processing.
He gave an invited talk on November 6th, 2003 during the
SAGEM Workshop on Image Processing.
Olivier Faugeras has been a member of the Conference Board of the
European Conference on Computer Vision, ever since he started the Conference
in 1990. He is a member of the advisory board of
Renaud Keriven has been a member of the Program Committee of the International Conference on Computer Vision 2003. He gave a talk at the Institut Gaspard Monge seminar in May 2003.
Thierry Viéville was an invited Speaker at the "Journées thématique
Support Vector Machines et méthodes à noyau", ENST, Paris
Frédéric Abad, Contributions à la synthèse de nouvelles vues
à partir de photographies, Nice-Sophia Antipolis University, June 2003.
Jacques Bride, Méthodes directes pour le recalage d'images :
efficacité et robustesse, Nice-Sophia Antipolis University, March 2003.
Quentin Delamarre, Suivi du mouvement d'objets articulés dans des
séquences d'images vidéo, Nice-Sophia Antipolis University, December 2003.
Bertrand Thirion, Analyse de donne'es d' IRM fonctionnelle:
statistiques, information et dynamique, Nice-Sophia Antipolis University, October 2003.
Geoffray Adde, "Probleme inverse en MEG", ENPC; Place: Odyssée ENPC, Marne.
Guillaume Charpiat, "Statistiques de courbes et d'images", ENS; Place: Odyssée ENS, Paris.
Thomas Deneux, "Modélisation et IRMf", ENS; Place: Odyssée ENS, Paris.
Olivier Juan, "Rotoscopie et applications à la réalite virtuelle", ENPC; Place: Odyssée ENPC, Marne.
Fabien Lejeune, "Intégration d'informations 3D et Applications", ENPC;Place: Odyssée ENPC, Marne.
Christophe Lenglet, "Processing and Analysis of Diffusion Tensor Magnetic Resonance Images", Nice-Sophia Antipolis University; Place: Sophia-Antipolis.
Lucero Lopez-Pérez, "Image Processing and PDE's on Non Flat Manifolds", Nice-Sophia Antipolis University; Place: INRIA Sophia-Antipolis.
Jean-Philippe Pons, "Méthodes variationnelles et reconstruction spatio-temporelle", ENPC; Place : INRIA Sophia-Antipolis
Emmanuel Prados, "Application de la théorie des solutions de viscosité au problème du calcul de la forme tridimensionnelle à partir d'une image", Nice-Sophia Antipolis University; Place: INRIA Sophia-Antipolis.
Mikaël Rousson, "Multi-valued Image segmentation and Integration ", Nice-Sophia Antipolis University; Place: INRIA Sophia-Antipolis.
Nicolas Wotawa, "IRMf pour la rétinotopie et l'analyse de la perception du mouvement", Nice-Sophia Antipolis University; Place: INRIA Sophia-Antipolis.
Frédéric Demoors, "Inverse EEG Problem", DEA STIC, Nice Sophia-Antipolis University, Location : Odyssée INRIA Sophia-Antipolis, from April 1st, 2003 to September 30th 2003, funded from the ACI DirInv.
Sophie Di Martino, "Half Quadratic Regularization and Superesolution in MRI", DEA STIC, Nice Sophia-Antipolis University, Location : Odyssée INRIA Sophia-Antipolis, from April 1st, 2003 to September 30th 2003.
Christophe Lenglet, "Diffusion Magnetic Resonance Imaging : Brain Connectivity Mapping, Modelization and Estimation of the Conductivity Tensor", DEA MVA Cachan, Location : Odyssée INRIA Sophia-Antipolis, from April 1st, 2003 to September 30th 2003.