Morpheme is a joint project between Inria, CNRS and the University of Nice-Sophia Antipolis, involving the Computer Science, Signals and Systems Laboratory (I3S) (UMR 6070) and the Institute for Biology of Valrose (iBV) (CNRS/INSERM) . It has been created in 2011 as “Equipe”.

The scientific objectives of MORPHEME are to characterize and model the development and the morphological properties of biological structures from the cell to the supra-cellular scale. Being at the interface between computational science and biology, we plan to understand the morphological changes that occur during development combining in vivo imaging, image processing and computational modeling.

The morphology and topology of mesoscopic structures, indeed, do have a key influence on the functional behavior of organs. Our goal is to characterize different populations or development conditions based on the shape of cellular and supra-cellular structures, including micro-vascular networks and dendrite/axon networks. Using microscopy or tomography images, we plan to extract quantitative parameters to characterize morphometry over time and in different samples. We will then statistically analyze shapes and complex structures to identify relevant markers and define classification tools. Finally, we will propose models explaining the temporal evolution of the observed samples. With this, we hope to better understand the development of normal tissues, but also characterize at the supra-cellular level different pathologies such as the Fragile X Syndrome, Alzheimer or diabetes.

Laure Blanc Féraud has obtained the “grade de chevalier dans l'Ordre National du Mérite”.

The recent advent of an increasing number of new microscopy techniques giving access to high throughput screenings and micro or nano-metric resolutions provides a means for quantitative imaging of biological structures and phenomena. To conduct quantitative biological studies based on these new data, it is necessary to develop non-standard specific tools. This requires using a multi-disciplinary approach. We need biologists to define experiment protocols and interpret the results, but also physicists to model the sensors, computer scientists to develop algorithms and mathematicians to model the resulting information. These different expertises are combined within the Morpheme team. This generates a fecund frame for exchanging expertise, knowledge, leading to an optimal framework for the different tasks (imaging, image analysis, classification, modeling). We thus aim at providing adapted and robust tools required to describe, explain and model fundamental phenomena underlying the morphogenesis of cellular and supra-cellular biological structures. Combining experimental manipulations, in vivo imaging, image processing and computational modeling, we plan to provide methods for the quantitative analysis of the morphological changes that occur during development. This is of key importance as the morphology and topology of mesoscopic structures govern organ and cell function. Alterations in the genetic programs underlying cellular morphogenesis have been linked to a range of pathologies.

Biological questions we will focus on include:

what are the parameters and the factors controlling the establishment of ramified structures? (Are they really organize to ensure maximal coverage? How are genetical and physical constraints limiting their morphology?),

how are newly generated cells incorporated into reorganizing tissues during development? (is the relative position of cells governed by the lineage they belong to?)

Our goal is to characterize different populations or development conditions
based on the shape of cellular and supra-cellular structures, e.g. micro-vascular
networks, dendrite/axon networks, tissues from 2D, 2D+t, 3D or 3D+t images (obtained with confocal microscopy, video-microscopy, photon-microscopy or micro-tomography). We plan to extract shapes or quantitative parameters to characterize the morphometric properties of different samples. On the one hand, we will
propose numerical and biological models explaining the temporal evolution of the
sample, and on the other hand, we will statistically analyze shapes and complex
structures to identify relevant markers for classification purposes. This should
contribute to a better understanding of the development of normal tissues but
also to a characterization at the supra-cellular scale of different pathologies such
as Alzheimer, cancer, diabetes, or the Fragile X Syndrome.
In this multidisciplinary context, several challenges have to be faced. The
expertise of biologists concerning sample generation, as well as optimization of
experimental protocols and imaging conditions, is of course crucial. However,
the imaging protocols optimized for a qualitative analysis may be sub-optimal
for quantitative biology. Second, sample imaging is only a first step, as we need
to extract quantitative information. Achieving quantitative imaging remains an
open issue in biology, and requires close interactions between biologists, computer
scientists and applied mathematicians. On the one hand, experimental and imag-
ing protocols should integrate constraints from the downstream computer-assisted
analysis, yielding to a trade-off between qualitative optimized and quantitative optimized protocols. On the other hand, computer analysis should integrate constraints specific to the biological problem, from acquisition to quantitative information extraction. There is therefore a need of specificity for embedding precise
biological information for a given task. Besides, a level of generality is also desirable for addressing data from different teams acquired with different protocols
and/or sensors.
The mathematical modeling of the physics of the acquisition system will yield
higher performance reconstruction/restoration algorithms in terms of accuracy.
Therefore, physicists and computer scientists have to work together. Quantitative
information extraction also has to deal with both the complexity of the structures of interest (e.g., very dense network, small structure detection in a volume,
multiscale behavior,

**Imaging:** this includes i) definition of the studied populations (experimental
conditions) and preparation of samples, ii) definition of relevant quantitative
characteristics and optimized acquisition protocol (staining, imaging,

**Feature extraction:** this consists in detecting and delineating the biological
structures of interest from images. Embedding biological properties in the
algorithms and models is a key issue. Two main challenges are the variability,
both in shape and scale, of biological structures and the huge size of data
sets. Following features along time will allow to address morphogenesis and
structure development.

**Classification/Interpretation:** considering a database of images containing
different populations, we can infer the parameters associated with a given
model on each dataset from which the biological structure under study has
been extracted. We plan to define classification schemes for characterizing
the different populations based either on the model parameters, or on some
specific metric between the extracted structures.

**Modeling:** two aspects will be considered. This first one consists in modeling
biological phenomena such as axon growing or network topology in different contexts. One main advantage of our team is the possibility to use the
image information for calibrating and/or validating the biological models.
Calibration induces parameter inference as a main challenge. The second
aspect consists in using a prior based on biological properties for extracting relevant information from images. Here again, combining biology and
computer science expertise is a key point.

The software MAD V2.0 was deposited with the APP in November 2012. It deals with the melasma severity scoring from multi-spectral imaging.

The software MAD V2.0 was transferred to Galderma R&D.

This work was made in collaboration with Caroline Chaux from LATP (Marseille) and Roberto Cavicchioli and Luca Zanni from University of Modena (Italy).

Parameter estimation, Maximum likelihood estimation, Wavelet transforms, Deconvolution, Gradi- ent methods

We are interested in regularizing hyperparameter estimation by maximum likelihood in inverse problems with wavelet regularization. One parameter per subband is estimated by gradient ascent algorithm.We have to face with two main difficulties: i) sampling the a posteriori image distribution to compute the gradient of the objective function; ii) choosing a suited step-size to ensure good convergence properties of the gradient ascent algorithm. We first show that introducing an auxiliary variable makes the sampling feasible using classical Metropolis-Hastings algorithm and Gibbs sampler. Secondly, we propose an adaptive step-size selection and a line-search strategy to improve the gradient-based method. Good performances of the proposed approach are demonstrated on both synthetic and real data.

this work was made in collaboration with Marc Antonini (I3S), Roberto Camarero and Christophe Latry (CNES) and Yves Bobichon (TAS).

coding, denoising, wavelet transform, global rate-distortion optimization

This work concerns the study of optimal noisy source coding/denoising.
A global optimization of the problem is usually difficult to perform as the global fidelity
criterion needs to be optimized in the same time over the sets of both coding
and denoising parameters. Most of the bibliography in this domain is based on
the fact that, for a specific criterion, the global optimization problem can
be simply separated into two independent optimization problems: The noisy
image should be first optimally denoised and this denoised image should
then be optimally coded. In many applications however, the layout
of the acquisition imaging chain is fixed and can not be changed, that is a
denoising step can not be inserted before coding. For this reason, we are
concerned here with the problem of global joint optimization in the case the
denoising step is performed, as usual, after coding/decoding. In this
configuration, we showed on a simple case how to express the global distortion as a function of
the coding and denoising parameters. We presented an algorithm to minimize
this distortion to get the optimal values of these parameters.
Figure shows results of this joint optimization
algorithm, on the classical test image *Barbara*, in comparison to
the usual disjoint optimization technique, which consists in selecting
the coding and the denoising parameters such that the
coding and the denoising errors are independently minimized. On the range
of validity of the proposed model, we see that the joint optimized distortion
slightly outperforms the disjoint optimized distortion (in the presented example,
the PSNR of the reconstructed image increases of

This research takes place within the ANR DIAMOND. This work was made in collaboration with Gilles Aubert, Laboratoire J. Dieudonné (CNRS,UNS).

One of our tasks within the ANR Diamond project is the blind restoration of images coming from Confocal laser scanning microscopy (CLSM). CLSM is a powerful technique for studying biological specimens in three dimensions by optical sectioning. Nevertheless, it suffers from some artifacts. First, CLSM images are affected by a depth-variant (DV) blur due to spherical aberrations induced by refractive index mismatch between the different media composing the system as well as the specimen. Second, CLSM images are corrupted with a Poisson noise due to low illumination. Because of these intrinsic optical limitations, it is essential to remove both DV blur and noise from these images by digital processing.

In this context, we first study space-variant (SV) blur models and prove that a model where the SV point spread function (PSF) is approximated by a convex combination of a set of space-invariant (SI) PSFs is efficient and adequate to the inversion problem . Afterwards, we focus on the non-bind restoration problem and we fit a fast restoration method based on a domain decomposition technique to our DV blur model , .

Recently, we focus on the blind case. In fact, in practice it is difficult to obtain the DV PSF in spite of the existence of theoretical PSF models , because these models are dependent on some unknown acquisition parameters (e.g. the refractive index (RI) of the specimen). Therefore a blind or semi-blind restoration algorithm is needed for this system. We propose two methods for this problem : In the first method, we define a criterion to be jointly minimized w.r.t to the image and the PSF set. In this method, the intensities of each SI PSF are estimated at every voxel. Although the big number of parameters to be estimated, the method allows more freedom on the shape of the PSF which could be more or less deformed according to spherical aberration level. We provide a theoretical proof of the existence of a minimizer of the considered problem . Then, we perform the minimization by following an alternate minimization scheme, each elementary minimization is performed using the recently proposed scaled gradient projection (SGP) algorithm that has shown a fast convergence rate . Results on simulated CLSM images and comparison with another alternate scheme based on a regularized version of the Richardson–Lucy algorithm are shown in Fig. . In the second blind method, we use a Gaussian approximation of each of the SI PSFs. This presents the advantage of significantly reducing the number of parameters to be estimated but constraints the PSF shape. We prove on simulated data that the first method provides more accurate restoration result than the second one.

This research takes place within the Inria Large-scale initiative Morphogenetics.

This work was made in collaboration with Christophe Godin and Léo Guignard from Virtual Plants.

super-resolution, SPIM, morphogenesis

It it known that the analysis of axonal topologies allows biologists to study the causes of neurological diseases such as Fragile X Syndrome and Spinal Muscular Atrophy. In order to perform the morphological analysis of axons, it is first necessary to segment them. Therefore, the automatic extraction of axons is a key problem in the field of neuron axon analysis.

For this purpose, biologists label single neurons within intact adult Drosophila fly brains and acquire 3D fluorescent confocal microscopy images of their axonal trees. These images need to be segmented.

The method performance was tested on 12 real 3D images and the results quantitatively evaluated by calculating the RMSE between the tracing done by an experienced biologist and the automatic tracing obtained by our method. The good results obtained in the validation show the potential use of this technique in helping biologists for extracting axonal trees from confocal microscope images (see figures and ).

We have developped an automated algorithm for detecting dendritic spines from XRMT data. XRMT data allows imaging a large volume of tissue, and therefore a higher number of spines than laser scanning microscopy. We have shown that despite the lower image quality compared to microscopic data, we were able to extract dendritic spines. The main idea of the proposed approach is to define a mask for performing the spine detection without facing the false alarms problem as we introduce some information on spines localization. We therefore first extract the dendrites themselves and then compute the spine mask based on prior knowledge on their distance to dendrites. To extract dendrite we first compute the medial axis thanks to a multi-scale Hessian-based method. Then, we extract segments by a 3D Hough transform and reconstruct the dendrites using a conditional dilation. The spine mask is defined nerby the detected dendrites using anatomical parameters described in the literature. A point process defined on this mask provides the spine detection.

To exemplify the proposed approach, a subvolume (220 × 180 × 100) has been extracted from a XRMT volume that is given on figure . As expected, the spines appear as small objects, whose size is close to the image resolution, along the tubular structures representing dendrites. Using the localization information to detect spine is essential to prevent false alarms due to noise or to the deviation of dendrites from a cylinder model. Figure shows the detected dendrite medial axis and the obtained spine detection. The obtained results are promising and correspond to a visual inspection of the data. Forthcoming validation study will allow to better assess the quality of the detection by providing a quantitative evaluation.

This work was done in collaboration with Emmanuel Soubies and Pierre Weiss from ITAV (Toulouse)

We have proposed some improvements of the Multiple Birth and Cut algorithm (MBC) in order to extract nuclei in 2D and 3D images. We have introduced a new contrast invariant energy that is robust to degradations encountered in fluorescence microscopy (e.g. local radiometry attenuations). Another contribution of this work is a fast algorithm to determine whether two ellipses (2D) or ellipsoids (3D) intersect. Finally, we propose a new heuristic that strongly improves the convergence rates. The algorithm alternates between two birth steps. The first one consists in generating objects uniformly at random and the second one consists in perturbing the current configuration locally. Performance of this modified birth step is evaluated and examples on various image types show the wide applicability of the method in the field of bio-imaging.

Figure left shows the segmentation result on a Drosophila embryo obtained using SPIM imaging. This is a rather easy case, since nuclei shapes vary little. The images are impaired by various defects: blur, stripes and attenuation. Despite this relatively poor image quality, the segmentation results are almost perfect. The computing time is 5 minutes using a C++ implementation. The image size is 700 × 350. Figure right presents a more difficult case, where the image is highly deteriorated. Nuclei cannot be identified in the image center. Moreover, nuclei variability is important meaning that the state space size χ is large. Some nuclei are in mitosis (see e.g. top-left). In spite of these difficulties, the MBC algorithm provides acceptable results. They would allow to make statistics on the cell location and orientation, which is a major problem in biology. The computing times for this example is 30 minutes.

In this work, we have proposed an algorithm for tracking spermatozoid in a sequence of confocal images. We first detect the spermatozoids by thresholding the result of a top hat operator. The thresold is automatically estimated using Otsu's method. We then analyse the different connected components to detect overlaps between adjacent spermatozoids. Temporal neighbors are selected based on the spatial consistency of the object sets between two consecutive time. A first result is given on figure .

It is known that neuronal morphology impacts network connectivity, thus providing information on its functioning. Moreover, it allows the characterization of pathological states. Therefore, the analysis of the morphological differences between normal and pathological structures is of paramount importance.

We present a new method for comparing reconstructions of axonal trees (obtained, for example, by applying our segmentation method on confocal microscopy images of normal and mutant axonal trees) which takes into account both topological and geometrical information and is based on the Elastic Shape Analysis Framework. The method computes the geodesic between two axons in a space of tree like shapes, and the distance between the two is defined as the length of the geodesic. Moreover, our method is capable of showing how one axon transforms into the other by taking intermediate points in the geodesic.

We consider two axonal trees

where

The method performance was tested on a group of 22 (11 normal and 11 mutant) 3D images, each containing one axonal tree manually segmented by an experienced biologist from a set of real confocal microscopy images. The mean and standard deviation of the inter and intra class distances between the neurons were calculated and results suggest that the proposed method is able to distinguish between the two populations (an average interpopulation to intrapopulation distance ratio of 1:21 and 1:28 were obtained). In addition, we computed the optimum transformations between axons. An example is shown in figure . This result was obtained by taking intermediate points along the geodesic between the two trees.

*This work was made in collaboration with Franck Plouraboué and Abdelakim El Boustani from IMFT, Caroline Fonta from CerCo,
Géraldine LeDuc from ESRF, Raphael Serduc from INSERM and Tim Weitkamp from Synchrotron Soleil. *

Micro-tomography produces high resolution images of biological structures such as vascular networks. We have defined
a new approach for segmenting vascular network into pathological and normal regions from considering their
micro-vessel 3D structure only. We consider a partition of the
volume obtained by a watershed algorithm based on the distance from the nearest vessel. Each territory, defined as Local Vascular Territory (*a Local Vascular Territory (LVT) is a connected
region corresponding to the catchment bassin associated with
a vascular element. It can be obtained through the watershed
computation on the opposite distance map from the vessels
and is not connected to the sample border.
*), is characterized
by its volume and the local vascular density. The volume and
density maps are first regularized by minimizing the total variation, within a Markov Random Field framework, using a graph cut algorithm . Then, a new approach is proposed to segment the volume from
the two previous restored images using an iterative algorithm based on hypothesis testing. We consider the variables density and volume for each LVT and the populations constituted by the different classes obtained by the segmentation at a given step. Classes which are not statistically significantly different are merged using a MANOVA.
This blind segmentation provides different regions which have been interprated by expert as tumor, necrosis, tumor periphery and sane tissue .

This work was partially funded by a contract with Galderma R&D
[http://

multispectral imaging, skin, hyperpigmentation, hypothesis tests, statistical inferences

One of the steps to evaluate the efficacy of a therapeutic solution is to test it on a clinical trial involving several populations of patients. Each population receives a studied treatment and a reference treatment for the disease.

For facial hyper-pigmentation, a group of

We propose a methodology to assess the efficacy a treatment by calculating three differential criteria: the darkness, the area and the homogeneity.
The darkness measure the average intensity of the disease on a gray scaled image

The figure illustrates the differential score calculated on a patient whose pathology decreases during the clinical trial. The proposed differential score have been tested in a full clinical study and provided results that agreed with the clinical analysis. This work have been patented and published in Inria research reports , .

This work is made in collaboration with Barbara André (Mauna Kea Technologies)

The problem of automatic image (or video, or object) classification is to find a function that maps an image to a class or category among a number of predefined classes. An image can be viewed as a vector of high-dimension. In practice, it is preferable to deal with a synthetic signature of lower dimension. Therefore, the two classical steps of image classification are: image signature extraction and signature-based image classification. The classification rule can be learned from a set of training sample images manually classified by experts. This is known as supervised statistical learning where *statistical* refers to the use of samples and *supervised* refers to the sample classes being provided. We are interested in the learning aspect of the multiclass *multiclass* means “three classes or more” while the two-class case is referred to as binary classification.

Among the proposed extensions of binary classification methods to multiclass (three classes or more), the one-versus-one and one-versus-all approaches are the most popular ones. Let us suppose that there are

As an alternative to these aforementioned strategies (as well as to other, less popular ones), we developed a recursive learning strategy. A tree of SVMs is built, achieving three goals: a fair balance in the number of samples used in each binary SVM learnings, a logarithmic complexity for classification (

*This work has been done in collaboration with Caroline Medioni from iBV.*

Analyzing how growing axons correctly reach their target neurons is essential for biologists to better understand the development of a nervous system. Analysis of the properties of axon growth requires detecting axonal tips and tracking their trajectories within complex and large data sets. When performed manually, the tracking task is arduous and time-consuming. To this end, we proposed a tracking method, based on the particle filtering technique, to follow the traces of axonal tips that appear as small bright spots in the

*This work has been done in collaboration with Caroline Medioni from iBV.*

It is established in biology that axons reach their target cells in the developing nervous system by the guidance of molecular gradients. To better understand how growing axons react to the molecular cues, either attractant or repellent, we simulated the trajectories of growing axons using a mathematical model that investigates the effect of molecular gradients on the axon's growth angle. Figure shows the simulated trajectories of 50 growing axons. The initial position of axons is

Contribution of multi and hyperspectral imaging to skin pigmentation evaluation. Contract #4383.

In collaboration with Joisane Zerubia from Ayin team.

Optimization of the compression-restoration chain for satellite images.

The MORPHEME team is member of the SIGNALIFE Laboratory of Excellence.

The DADA project (Description et Analyse Dynamique de la Croissance Axonale) is a common projet with the SERPICO team from Inria Bretagne (Charles Kervrann). The goal is to develop new computational techniques to track axons during their growth. We consider 4D data obtained on a bi-photons microscope. In a longer term, we expect to model the morphological develpement of axons in different populations to characterize some disorders such as the fragile-X symdrom. (DADA).

In collaboration with the Pasteur Institute (Jean-Chritophe Olivo Marin) , the MIPS laboratory of Université de Haute Alsace (Alain Dieterlen, Bruno Colicchio) , the LIGM of Université Paris-Est (Jean-Christophe Pesquet, Caroline Chaux, Hugues Talbot), and INRA Sophia-Antipolis (Gilbert Engler).

(DIAMOND)

In collaboration with Institut de Mathématiques de Toulouse, INRA, Institut de Mécanique des Fluides de Toulouse, Laboratoire J-A Dieudonné, et IMV Technologies (PME).

The young researcher ANR project POXADRONO is in collaboration with Caroline Medioni, Hélène Bruckert, Giovanni Marchetti, Charlène Perrois and Lucile Palin from iBV. It aims at studying ARN regulation in the control of growth and axonal guidance by using a combination of live-imaging, quantitative analysis of images, bio-informatic analysis and genetic screening.

This action gathers the expertise of three Inria research teams (Virtual Plants, Morpheme, and Evasion) and other groups (RDP (ENS-CNRS–INRA, Lyon), RFD (CEA-INRA-CNRS, Grenoble)) and aimed at understanding how shape and architecture in plants are controlled by genes during development. To do so, we will study the spatio-temporal relationship between genetic regulation and plant shape utilizing recently developed imaging techniques together with molecular genetics and computational modelling. Rather than concentrating on the molecular networks, the project will study plant development across scales. In this context we will focus on the Arabidopsis flower, currently one of the best-characterised plant systems.

This project aims at studying graphs in biological context (axons, vascular networks

In collaboration with Institut de Mathématiques de Toulouse, INRA, Institut de Mécanique des Fluides de Toulouse, Laboratoire J-A Dieudonné, et IMV Technologies (PME).

Partners: Barbara André, Mauna Kea Technologies, Paris, France

Subject: Automatic classification of endomicroscopic videos

Roberto Cavicchioli, PhD student, University de Modena and Reggio Emilia. Visting period 01/04/2012 - 30/06/2012; MAEE Research grant.

Alexandre Dufour, Pasteur Institute, Unité d'Analyse d'Images Quantitative CNRS URA 2582 "Interactions et dynamique cellulaires". 3 december 2012, seminar at I3S.

Caroline Fonta, CerCo, Toulouse, 7 december 2012, seminar at iBV.

Charles Deledalle, Ceremade, Paris Dauphine, 3 august 2012, seminar at I3S.

Florence Besse was reviewer for UPMC, AFM, DFG (grant agencies).

Laure Blanc-Féraud was reviewer for IEEE Trans on Signal processing, Inverse Problems, Signal Image and Video processing (Eurasip) and the conferences IEEE ISBI, IEEE ICIP, IEEE ICASSP. She is associate editor for "Revue Traitement du Signal". She was co-organisor of the workshop on New Computational Methods in Inverse Problems - NCMIP 2012 (NCMIP) and was associate editor for the conferences : RFIA 2012, Workshop MIA 2012, Workshop NCMIP 2012, International conference IEEE ISBI 2013. She is member of the IEEE BISP (Biomedical Imaging Signal Processing) Technical Committee, member of the evaluation committee of the ANR, program blanc SIMI3, member of the scientific council of Institute INS2I of CNRS, member of bureau du comité des projets Inria SAM and supplant member of CNECA (Comité National des Enseignants Chercheurs en Agriculture). She is director of GdR ISIS of CNRS

Xavier Descombes was reviewer for the conference ISBI 2012 and the journals IEEE TMI, IEEE IP

Saima Ben Hadj was reviewer for Signal Image and Video Processing.

Eric Debreuve was member of the Program Committee of Advanced Concepts for Intelligent Vision Systems (ACIVS) 2012
and member of the Technical Program Committee of European Signal Processing Conference (EUSIPCO) 2012. He was reviewer for *IEEE* Transactions on Image Processing, *Springer* Machine Vision and Applications, *Springer* Multimedia Tools and Applications, *Lavoisier* Revue Traitement du Signal.

Grégoire Malandain was a member of the Local Organizing Committee of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI'12) in Nice. He was also a member of the review committee of International Conference on Pattern Recognition (ICPR'12) and the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI'12). He is member of the Scientific Committee of the MIA department of INRA.

License : Alejandro Mottini, Informatique générale, 24 heures équivalent TD , niveau L1, UNS, France

License : Alejandro Mottini, Introduction au web, 16 heures équivalent TD , niveau L1, UNS, France

Master : Mikael Carlavan, Traitement Numérique des Images, 10 heures équivalent TD , niveau M2, UNS, France

Master : Alejandro Mottini, Outils Mathematiques pour l'Image, 2 heures équivalent TD , niveau M2, UNS, France

Master : Xavier Descombes, Analyse d'image, 12 heures équivalent TD , niveau M2, UNS EPU, France

Master : Xavier Descombes, Traitement d'images, 6,25 heures équivalent TD , niveau M2, ISAE, France

Master : Xavier Descombes, Reconnaissance de formes et analyse de données, 6,25 heures équivalent TD , niveau M2, ISAE, France

Master : Xavier Descombes, Techniques avancées en signal et image, 5 heures équivalent TD , niveau M2, ISAE, France

Master : Xavier Descombes, Imagerie numérique, 16 heures équivalent TD , niveau M2, UNS, France

Master : Laure Blanc-Féraud, Deconvolution and denoising for confocal microscopy, 18heqTD, niveau M2, université de Nice Sophia Antipolis, France.

Master : Laure Blanc-Féraud, Traitement numérique des images, 12eqTD, niveau M2, université de Nice Sophia Antipolis, France.

Master : Laure Blanc-Féraud, Imagerie numérique, 12eqTD, niveau M2, université de Nice Sophia Antipolis, France.

Master : Eric Debreuve, Introduction to Inverse Problems in Image Processing, 28.5 Eq. TD, Niveau M2, Université de Nice-Sophia Antipolis, France.

Master: Eric Debreuve, Basics of Image Processing, 17.5 Eq. TD, Niveau M2, Université de Nice-Sophia Antipolis, France.

Master: Alexis Zubiolo, Digital Image Processing, 10h Eq. TD, Université de Nice-Sophia Antipolis, France.

Licence: Alexis Zubiolo, Computer Science, Introduction to Computer Science, 19h Eq. TD, Université de Nice-Sophia Antipolis, France.

HdR : Florence Besse, Régulation des ARNms et Morphogenèse axonale chez la drosophile, soutenue le 19 octobre 2012.

PhD : Sylvain Prigent, Apport de l'imagerie multi et hyperspectrale pour l'évaluation de la pigmentation de la peau, UNS, soutenue le 11 novembre 2012, Xavier Descombes (advisor), Josiane Zeruria, Inria CRI-SAM (co-advisor)

PhD in progress : Alejandro Mottini, Métriques de graphes pour la caractérisation des axones, depuis octobre 2011, Xavier Descombes (advisor), Florence Besse (co-supervisor).

PhD in progress : Mikale Carlavan, Optimization of the compression-restoration chain for satellite images, Laure Blanc-Féraud (advisor) M. Antonini, I3S (co-advisor).

PhD in progress, Saima Ben Hadj, Blind restoration of space variant 3D confocal microscopic images, Laure Blanc-Féraud (advisor).

PhD in progress, Roberto Cavicchioli, fast gradient method for hyperparameter estimation in wavelet regularization inverse problems in imaging, Laure Blanc-Féraud ( co-advisor during 3 months ).

PhD in progress, Alexis Zubiolo, Statistical Machine Learning for Automatic Cell Classification, Eric Debreuve (advisor).

HDR : Florence Besse, referee of a Habilitation committee at Univ. Paris 11

PhD : Florence Besse, referee of a PhD committee committee at UMPMC, Villefranche sur mer

PhD : Xavier Descombes, referee of the PhD thesis committee of Sylvain Prigent, UNS

PhD : Xavier Descombes, reviewer of the PhD thesis committee of Marcello Pereyra, ENSEEIHT

PhD : Xavier Descombes, reviewer of the PhD thesis committee of Pauline Julian, ENSEEIHT

PhD : Xavier Descombes, reviewer of the PhD thesis committee of Guillaume Zinck, Univ. Bordeaux 1

PhD : Laure Blanc-Féraud, referee of the PhD committee of Raphaël Soulard, XLIM.

HDR : Laure Blanc-Féraud, reviewer of the Habilitation of Thomas Rodet, University Paris Sud.

HDR : Laure Blanc-Féraud, reviewer of the Habilitation of Jérôme Gilles, ENS Cachan.

PhD : Grégoire Malandain, reviewer of the PhD thesis committee of V. Bismuth (Paris-Est University)

PhD : Grégoire Malandain, reviewer of the PhD thesis committee of P. Chassignet (École Polytechnique)

PhD : Grégoire Malandain, reviewer of the PhD thesis committee of C. Person (Lorraine University),

PhD : Grégoire Malandain, reviewer of the PhD thesis committee of G. Pizaine (Telecom ParisTech),

HDR : Grégoire Malandain, reviewer of the Habilitation committee of J. Debayle (Saint-Étienne University),

PhD : Grégoire Malandain, referee of the medicine thesis of M. Laffon (Nice University).

Xavier Descombes has given a conference at lycée René Char (Avignon) within the program “Science au Lycée”

Xavier Descombes has given a seminar at “journée Traitement d'images” organized by Optitec at LSI Luminy (Marseille)

Xavier Descombes has given a seminar at "Matinale des Pôles" organized by the foundation of Sophia Antipolis