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From LRDE
(Redirected from Main Page)The LRDE is a research laboratory under the tutelage of EPITA, Graduate School of Computer Science.
Our main areas of expertise are « Image processing and pattern recognition » and « Automata and verification » with a transverse research axis « Performance and genericity ».
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
