Discrete mathematics
Graphs, trees, hierarchies, topology of discrete structures, and algorithmic foundations used in image analysis and data science.
Teaching
Courses and supervision
I have taught at engineering and Master's level across discrete mathematics, computer science, mathematical morphology, image processing, signal processing, and artificial intelligence. This page keeps the current teaching profile visible while preserving useful archived resources from the previous website.
Areas
Teaching themes
Computer science
Engineering and Master-level courses including operating systems, compilers, algorithms, and practical programming foundations.
Image processing and morphology
Mathematical morphology, connected operators, segmentation, filtering, shape analysis, and graph-based models for visual data.
Artificial intelligence
AI and deep learning courses connected to computer vision, feature spaces, interpretability, and data-driven image analysis.
Signal processing
Signal and image processing courses for engineering students, with links to biomedical imaging and visual data analysis.
Research supervision
Project, Master, PhD, and HDR supervision across mathematical morphology, graph methods, topology, and applied imaging.
Books
Teaching references
Systèmes d'exploitation - Synthèse de cours & exercices corrigéswith Bart Lamiroy and Hugues Talbot, Pearson Education France, 2006A compact course-and-exercises book for operating-system foundations, connected to the long-running computer-science teaching at ESIEE.
Archive
Course resources from the previous site
Operating systems and compilation
Core ESIEE material around operating-system concepts, compilation, exercises, and practical foundations. The operating-systems book remains the main reference for this part of the teaching archive.
- Operating systems
- Compilation
- Exercises
IA et Deep Learning
A hands-on course built around Keras notebooks in Google Colab or Kaggle, with material on neural networks, convolutional networks, overfitting, project reports, and explainable AI.
- Keras labs
- Colab / Kaggle
- Explainable AI
Image analysis and processing
Engineering course material on image formation, enhancement, transforms, filtering, segmentation, and connected-geodesic approaches, with practical material based on PinkDev.
- Enhancement
- Filtering
- Segmentation
Image-processing programming tutorials
The first sessions introduce practical image operators with image differences and blob measurements. The second sessions implement a Canny edge detector, with emphasis on hysteresis thresholding.
- PinkDev
- Blob analysis
- Canny detector
Mathematical morphology
Master-level material from Université Gustave Eiffel on dilations and erosions, openings and closings, greyscale morphology, the shaping framework, practical sessions, and projects.
- Morphological operators
- Greyscale morphology
- Shaping
ISBS imaging projects
Third-year imaging projects for bio-engineering students, mixing applied image analysis, project briefs, validation material, and medical or industrial imaging case studies.
- Project briefs
- Validation data
- Applied imaging
Higra tutorial
A practical tutorial on hierarchical graph analysis, usable online in Google Colab or locally with Python. The notebooks cover connected filters and hierarchical segmentation.
- Hierarchical graphs
- Connected filters
- Segmentation