In accordance with the decisions taken by the French government concerning the Coronavirus, LRDE is closed to the public from Monday, 16 March 2020.
All LRDE members can be reached by email.
The LRDE is a research laboratory under the tutelage of EPITA, Graduate School of Computer Science.
Building on its solid scientific production and academic collaborations, the laboratory has industrial contracts, conducts internal research projects and participates in collaborative academic research projects.
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- Publication Euler Well-Composedness in Combinatorial Image Analysis: Proceedings of the 20th International Workshop, IWCIA 2020, Novi Sad, Serbia, July 16–18, 2020 — 21 July 2020
- Publication A 4D Counter-Example Showing that DWCness Does Not Imply CWCness in n-D in Combinatorial Image Analysis: Proceedings of the 20th International Workshop, IWCIA 2020, Novi Sad, Serbia, July 16–18, 2020 — 21 July 2020
- Julie Rivet defends her PhD thesis "Non-iterative methods for image improvement in digital holography of the retina" at EPITA at 2 pm. — 17 July 2020
- Didier Verna defends his Habilitation thesis at EPITA at 2 pm. — 10 July 2020
- Publication (Dynamic (Programming Paradigms)) ;; Performance and Expressivity — 10 July 2020
- Publication Practical “Paritizing” of Emerson-Lei Automata in Proceedings of the 18th International Symposium on Automated Technology for Verification and Analysis (ATVA'20) — 7 July 2020
- Publication Improving swarming using genetic algorithms in Innovations in Systems and Software Engineering: a NASA journal (ISSE) — 2 June 2020
- Publication A New Minimum Barrier Distance for Multivariate Images with Applications to Salient Object Detection, Shortest Path Finding, and Segmentation in Computer Vision and Image Understanding — 2 June 2020
- EPITA presents a webinar with Microsoft at Explor'IA on Artificial Intelligence and Medical Image Analysis. — 2 June 2020
In this webinar, Nicolas Boutry from LRDE presents how to segment with Convolutional Neural Networks (CNN's) white and grey matters in multi-modal MRI 3D brain images of 6-months year old children. His demonstration is based on a dataset from the iSeg2017 challenge.
- Publication Using Separated Inputs for Multimodal Brain Tumor Segmentation with 3D U-Net-like Architectures in Proceedings of the 4th International Workshop, BrainLes 2019, Held in Conjunction with MICCAI 2019 — 1 June 2020