Profil

Ennadifi Elias

Université de Mons - UMONS > Faculté Polytechnique > Service Information, Signal et Intelligence artificielle

ORCID
0000-0002-3000-4956
Principaux co-auteurs référencés
GOSSELIN, Bernard  (8)
Carlier, Alexis (4)
Dandrifosse, Sébastien (4)
Laraba, Sohaib  (4)
Mercatoris, Benoît (4)
Principaux mots-clés référencés
counting (3); YOLOv5 (3); Deep Learning (2); DeepMAC (2); Detection (2);
Principales disciplines référencées
Sciences informatiques (6)
Ingénierie électrique & électronique (2)

La plus téléchargée
19 téléchargements
Mokhtari, M. E. A., Mancas, M., Vandenbulcke, V., Ennadifi, E., Laraba, S., Tazir, M., & Gosselin, B. (2023). Efficient Action Recognition for Drones: A Comparative Study of Lightweight and Traditional Models [Paper presentation]. European Test and Telemetry Conference, Toulouse, France. https://hdl.handle.net/20.500.12907/46053

La plus citée

15 citations (Scopus®)

Dandrifosse, S.* , Ennadifi, E.* , Carlier, A., Gosselin, B., Dumont, B., & Mercatoris, B. (2022). Deep Learning for Wheat Ear Segmentation and Ear Density Measurement: From Heading to Maturity. Computers and Electronics in Agriculture. https://hdl.handle.net/20.500.12907/43085

Ennadifi, E., Ravet, T., Mancas, M., Mokhtari, M. E. A., & Gosselin, B. (2023). Enhancing VR Gaming Experience using Computational Attention Models and Eye-Tracking. ACM International Conference on Interactive Media Experiences (IMX).
Peer reviewed

Mokhtari, M. E. A., Mancas, M., Vandenbulcke, V., Ennadifi, E., Laraba, S., Tazir, M., & Gosselin, B. (2023). Efficient Action Recognition for Drones: A Comparative Study of Lightweight and Traditional Models [Paper presentation]. European Test and Telemetry Conference, Toulouse, France.

Mokhtari, M. E. A., Vandenbulcke, V., Laraba, S., Mancas, M., Ennadifi, E., Tazir, M., & Gosselin, B. (2022). Semi-synthetic Data for Automatic Drone Shadow Detection. ESANN.
Peer reviewed

Ennadifi, E., Dandrifosse, S., Mokhtari, M. E. A., Carlier, A., Laraba, S., Mercatoris, B., & Gosselin, B. (2022). Local Unsupervised Wheat Head Segmentation. ICCP 2022.
Peer reviewed

Dandrifosse, S.* , Ennadifi, E.* , Carlier, A., Gosselin, B., Dumont, B., & Mercatoris, B. (2022). Deep Learning for Wheat Ear Segmentation and Ear Density Measurement: From Heading to Maturity. Computers and Electronics in Agriculture.
Peer reviewed vérifié par ORBi
* Ces auteurs ont contribué de façon équivalente à la publication.

Dandrifosse, S., Ennadifi, E., Carlier, A., Gosselin, B., Dumont, B., & Mercatoris, B. (2022). Contrasted-Fertilization Wheat Ear Dataset 2020. doi:10.5281/zenodo.5709821

Dandrifosse, S., Ennadifi, E., Carlier, A., Gosselin, B., Dumont, B., & Mercatoris, B. (2022). Effect of the sun on the measurement of wheat ear density by deep learning. The 15th International Conference on Precision Agriculture.
Peer reviewed

Ennadifi, E., Laraba, S., Vincke, D., Mercatoris, B., & Gosselin, B. (2020). Wheat Diseases Classification and Localization Using Convolutional Neural Networks and GradCAM Visualization. IEEE ISCV2020.
Peer reviewed

Contacter ORBi UMONS