I’m a full professor in Computer Science at the LIMOS lab of Clermont-Auvergne University and CNRS.
I’m Vice dean of Clermont-Auvergne-INP (education and training) and Assistant director of LIMOS.
My research interests are centered on data analysis, viewed from both a methodological and an applicative point of view. This include image and video processing, mesh processing, machine and deep learning.
I mainly teach at ISIMA, an engineering school in computer science of Clermont-Auvergne INP.
Clic on the topics to select the projects
Microarray images push to their limits classical analysis methods. New approaches are thus needed to ensure accurate data extraction from these images
Analysis of thermal videos of volcanoes
Machine Learning in Brain Data Processing
Use of Hidden Markov Models and non stationarity to process temporal satellite Images.
Automatic tool for topographic analysis able to compute 2D maps from the 3D anatomic MRI.
Development of a TMS simulator.
3D shape retrieval using kernels on graphs.
First step of our longstanding collaboration with the University of Bergen, in Norway.
General framework for the fusion of Anatomical and Functional Images.
Unsupervised fuzzy classification scheme for brain tissue segmentation
Decomposition of surfaces into quadrangle regions.
Aggregation between a MR image and information resulting from expert knowledge.
Mapping a surface onto a piece of the plane, using low resolution acquisitions.
Quantification of brain tissues using multispectral MR images fusion.
Tracking of objects in videos using MCMC and Ensemble methods
Variability Analysis of the solar corona and the monitoring of its traditional regions
Decomposition of surfaces into quadrangle regions.
Tiling image with cylinders using n-loops.
Segmentation of muscle and fat compartments from MR images of thighs
Combined, voxel and surface based, topology correction method.
I am (or have been) a teaching instructor for the following courses :
| Introduction to deep Learning Keras and TensorFlow | Neural networks and MLP | CNN |
| Autoencoders | RNN | Transfer Learning |
| Recommandation systems | Generative Adversarial Networks | Applications |
| Introduction to Machine Learning and scikit-learn | Linear models | Classification and clustering |
| Ensemble methods | Kernel methods | Model selection and manifold learning |
| Hidden Markov Models | Applications | |
| First and second order statistics | Linear dimension reduction algorithms | Clustering and classification |
| Introduction to linear systems | Numerical stability | Least squares and orthogonal transformations |
| Eigen analysis | Positive definite matrices | Introduction to optimisation in finite dimension |
| Introduction to CImg | Spatial filtering | Filtering in the frequency domain |
| Diffusion filtering | Optical flow | Hough transform |
| Clustering and classification in image processing | Active contours | Image Segmentation |
I am (or have been) a teaching instructor for the following courses :
| Introduction to deep Learning Keras and TensorFlow | Neural networks and MLP | CNN |
| Autoencoders | RNN | Transfer Learning |
| Recommandation systems | Generative Adversarial Networks | Applications |
| Introduction to Machine Learning and scikit-learn | Linear models | Classification and clustering |
| Ensemble methods | Kernel methods | Model selection and manifold learning |
| Hidden Markov Models | Applications | |
| First and second order statistics | Linear dimension reduction algorithms | Clustering and classification |
| Introduction to linear systems | Numerical stability | Least squares and orthogonal transformations |
| Eigen analysis | Positive definite matrices | Introduction to optimisation in finite dimension |
| Introduction to CImg | Spatial filtering | Filtering in the frequency domain |
| Diffusion filtering | Optical flow | Hough transform |
| Clustering and classification in image processing | Active contours | Image Segmentation |
