1 code implementation • Neurocomputing 2023 • Atif Anwer, Samia Ainouz, Naufal M. Saad, Syed Saad Azhar Ali, Fabrice Meriaudeau
Once trained, SHMGAN is able to generate specular-free images from a single RGB image as input; without requiring any additional external labels.
no code implementations • 18 Jan 2023 • Zongwei Wu, Guillaume Allibert, Fabrice Meriaudeau, Chao Ma, Cédric Demonceaux
In this paper, from a new perspective, we propose a novel Hierarchical Depth Awareness network (HiDAnet) for RGB-D saliency detection.
1 code implementation • MDPI Sensing and Imaging 2022 • Atif Anwer, Samia Ainouz, Mohamad Naufal Mohamad Saad, Syed Saad Azhar Ali, Fabrice Meriaudeau
Specular highlights detection and removal in images is a fundamental yet non-trivial problem of interest.
1 code implementation • 24 Jun 2022 • Sophia Bano, Alessandro Casella, Francisco Vasconcelos, Abdul Qayyum, Abdesslam Benzinou, Moona Mazher, Fabrice Meriaudeau, Chiara Lena, Ilaria Anita Cintorrino, Gaia Romana De Paolis, Jessica Biagioli, Daria Grechishnikova, Jing Jiao, Bizhe Bai, Yanyan Qiao, Binod Bhattarai, Rebati Raman Gaire, Ronast Subedi, Eduard Vazquez, Szymon Płotka, Aneta Lisowska, Arkadiusz Sitek, George Attilakos, Ruwan Wimalasundera, Anna L David, Dario Paladini, Jan Deprest, Elena De Momi, Leonardo S Mattos, Sara Moccia, Danail Stoyanov
For this challenge, we released a dataset of 2060 images, pixel-annotated for vessels, tool, fetus and background classes, from 18 in-vivo TTTS fetoscopy procedures and 18 short video clips.
no code implementations • 15 Jun 2022 • Cyprien Ruffino, Rachel Blin, Samia Ainouz, Gilles Gasso, Romain Hérault, Fabrice Meriaudeau, Stéphane Canu
Polarimetric imaging, along with deep learning, has shown improved performances on different tasks including scene analysis.
no code implementations • 9 Aug 2021 • Alain Lalande, Zhihao Chen, Thibaut Pommier, Thomas Decourselle, Abdul Qayyum, Michel Salomon, Dominique Ginhac, Youssef Skandarani, Arnaud Boucher, Khawla Brahim, Marleen de Bruijne, Robin Camarasa, Teresa M. Correia, Xue Feng, Kibrom B. Girum, Anja Hennemuth, Markus Huellebrand, Raabid Hussain, Matthias Ivantsits, Jun Ma, Craig Meyer, Rishabh Sharma, Jixi Shi, Nikolaos V. Tsekos, Marta Varela, Xiyue Wang, Sen yang, Hannu Zhang, Yichi Zhang, Yuncheng Zhou, Xiahai Zhuang, Raphael Couturier, Fabrice Meriaudeau
The publicly available database consists of 150 exams divided into 50 cases with normal MRI after injection of a contrast agent and 100 cases with myocardial infarction (and then with a hyperenhanced area on DE-MRI), whatever their inclusion in the cardiac emergency department.
no code implementations • 3 Jun 2021 • Marc Blanchon, Désiré Sidibé, Olivier Morel, Ralph Seulin, Fabrice Meriaudeau
Autonomous robotics is critically affected by the robustness of its scene understanding algorithms.
no code implementations • 15 Jul 2020 • Marc Blanchon, Désiré Sidibé, Olivier Morel, Ralph Seulin, Daniel Braun, Fabrice Meriaudeau
Monocular depth estimation is a recurring subject in the field of computer vision.
no code implementations • MIDL 2019 • Abdul Qayyum, Alain Lalande, Thomas Decourselle, Thibaut Pommier, Alexandre Cochet, Fabrice Meriaudeau
The proposed model could be used for the automatic segmentation of myocardial border that is a very important step for accurate quantification of no-reflow, myocardial infarction, myocarditis, and hypertrophic cardiomyopathy, among others.
no code implementations • 22 May 2020 • Marc Blanchon, Olivier Morel, Fabrice Meriaudeau, Ralph Seulin, Désiré Sidibé
Robotics applications in urban environments are subject to obstacles that exhibit specular reflections hampering autonomous navigation.
no code implementations • 2 Oct 2019 • Rachel Blin, Samia Ainouz, Stéphane Canu, Fabrice Meriaudeau
The efficiency of the proposed method is mostly due to the high power of the polarimetry to discriminate any object by its reflective properties and on the use of deep neural networks for object detection.
no code implementations • 16 Sep 2019 • Emna Rejaibi, Ali Komaty, Fabrice Meriaudeau, Said Agrebi, Alice Othmani
The proposed approach outperforms the state-of-art approaches on the DAIC-WOZ database with an overall accuracy of 76. 27% and a root mean square error of 0. 4 in assessing depression, while a root mean square error of 0. 168 is achieved in predicting the depression severity levels.