Mohamed Chérif - AI/ML expert

Publications rescentes
Journaux
• CHIARONI, Florent, ********, Mohamed-Cherif, HUEBER, Nicolas, et al. Self-supervised learning
for autonomous vehicles perception: A conciliation between analytical and learning methods. IEEE
Signal Processing Magazine, 2020, vol. 38, no 1, p. 31-41.
• CHIARONI, Florent, KHODABANDELOU, Ghazaleh, ********, Mohamed-Cherif, et al. Counterexamples generation from a positive unlabeled image dataset. Pattern Recognition, 2020, vol. 107, p.
107527.
• Hajri, Hatem et ********, Mohamed-Cherif. Real time lidar and radar high-level fusion for obstacle
detection and tracking with evaluation on a ground truth. International Journal of Mechanical and
Mechatronics Engineering Vol:12, No:8, 2018
• Gruyer, D., Magnier, V., ********, M. A., & Bresson, G. (2017). Real-time architecture for obstacle detection, tracking and filtering: An issue for the autonomous driving. J. of Intelligent Computing, 8(2),
33-48.
• Dogan, E., ********, M. C., Deborne, R., Delhomme, P., Kemeny, A., & Perrin, J. (2017). Transition of
control in a partially automated vehicle: Effects of anticipation and non-driving-related task involvement. Transportation research part F: traffic psychology and behaviour, 46, 205-215.
• ********, Mohamed Chérif. "Un nouvel algorithme de classification spatiale." Revue Modulad (numéro
42) 2009.

Conférences
• D. Gruyer and M. ********, "Multi-Layer Laser Scanner Strategy for Obstacle Detection and Tracking,"
2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA),
2019, pp. 1-8, doi: 10.1109/AICCSA47632.2019.9035243.
• Hamideche, S. A., Chiaroni, F., & ********, M. C. (2019). Self-supervised classification of dynamic
obstacles using the temporal information provided by videos. arXiv preprint arXiv:1910.09094. (submitted)
• Florent Chiaroni, Mohamed-Cherif ********, Nicolas Hueber, Frédéric Dufaux: Hallucinating A Cleanly
Labeled Augmented Dataset from A Noisy Labeled Dataset Using GAN. ICIP 2019: 3616-3620
• Chiaroni, F., ********, M. C., Hueber, N., & Dufaux, F. Learning with a generative adversarial network
from a positive unlabeled dataset for image classification. In 2018 25th IEEE International Conference
on Image Processing (ICIP) (pp. 1368-1372). IEEE.
• Chiaroni, F., ********, M. C., Dufaux, F., & Hueber, N. Classification d’images en apprenant sur des
échantillons positifs et non labélisés avec un réseau antagoniste génératif. CNIA & RJCIA 2018, 35.
• Chiaroni, F., ********, M. C., Hueber, N., & Dufaux, F. D-GAN: Divergent generative adversarial network for positive unlabeled learning and counter-examples generation. In 2017 24th IEEE International Conference on Image Processing (ICIP) (pp. 1368-1372). IEEE.
• Revilloud, M., Gruyer, D., & ********, M. C. (2016, May). A new multi-agent approach for lane detection
and tracking. In 2016 IEEE International Conference on Robotics and Automation (ICRA) (pp. 3147-
3153). IEEE.
• Bresson, G., ********, M. C., Gruyer, D., Revilloud, M., & Alsayed, Z. (2016, November). A cooperative
fusion architecture for robust localization: Application to autonomous driving. In 2016 IEEE 19th
international conference on intelligent transportation systems (ITSC) (pp. 859-866). IEEE.
• Hajri, Hatem et ********, Mohamed-Cherif. Real time lidar and radar high-level fusion for obstacle
detection and tracking with evaluation on a ground truth. 20th International Conference on Automation, Robotics and Applications Lisbon sept 24-25, 2018 (Best paper award)
• Hajri, H., Doucet, E., Revilloud, M., Halit, L., Lusetti, B., & ********, M. C. (2018). Automatic generation
of ground truth for the evaluation of obstacle detection and tracking techniques. IV 2018 - Madrid
• Hammi, B., ********, M. C., & Khatoun, R. (2016, July). Clustering methods comparison: Application
to source based detection of botclouds. In 2016 International Conference on Security of Smart Cities,
Industrial Control System and Communications (SSIC) (pp. 1-7). IEEE.
• ********, M.C,. "Topological structures learning using the Delaunay triangulation and cascade support
vector machines." (ICML - FEAST 2014).
• Revilloud, M., Gruyer, D., & ********, M. C. (2016, November). A lane marker estimation method for
improving lane detection. In 2016 IEEE 19th International Conference on Intelligent Transportation
Systems (ITSC) (pp. 289-295). IEEE.

Brevets
• REVILLOUD, Marc, ********, Mohamed Cherif, et GRUYER, Dominique. Image Processing Method
For Recognizing Ground Marking And System For Detecting Ground Marking. U.S. Patent Application No 16/301,061, 20 juin 2019.
• REVILLOUD, Marc, ********, Mohamed Cherif, et GRUYER, Dominique. Procédé de traitement
d’image pour la reconnaissance de marquage au sol et système pour la détection du marquage au
sol. Priority to FR1654322A