Professor, Polytech Annecy-Chambéry.
Email : alexandre.benoit –@– univ-smb.fr
Phone : +33(0) 4 50 09 65 22 – télécopie : +33(0) 4 50 09 65 22
Office : A223
Postal address : LISTIC – Polytech Annecy Chambéry, BP 80439, 74944 Annecy le Vieux Cedex, France
Position / responsabilities.
- Permanent LISTIC Lab. member, professor position
- Main teaching activities at Polytech Annecy Chambéry, Université Savoie Mont Blanc.
Member of the « Apprentissage, Fusion et Télédétection (AFuTé) thematic » with strong connexions with ReGaRD thematics on distributed systems, HPC, data and ethics,
- Image, video and more generally data understanding : from data preprocessing, features extraction to concept recognition (objects, action, etc.) to classical 1D sensors.
- Heterogeneous Data Fusion : associating multimodal inputs, images, contextual indicators, and others to make the decision process more reliable.
- Sensor time series in general, from image sequences (multimedia, satellite remote sensing, astrophysics imaging) to classical sensors analysis.
- Current methodological approaches rely on deep learning methods with some specific directions:
- Frugal models, low computational cost by design: light architectures efficient in terms of training efficacy as well as reduced inference time.
- Explainable models, with two approaches:
- physical aware models design : by introducing physical knowledge in the model structure or by introducing optimization criteria related to the physical phenomenon to be considered.
- developing XAI visualization techniques to help explain and understand model behaviors and predictions.
- Physically informed models, ongoing activity
- Federated learning, distributed and privacy preserving machine learning, ongoing activity.
Video clustering Classification Retina modelDeep Learning Semi-supervised learning 3D convolutional neural network V1 cortex model Semantic Segmentation Real-time retina Human vision Remote sensingSpectral Clustering 2D metrics 3DOpenInterface Deep learning Attention level, Pairwise constraints Graph Cut
- A brief overview on these slides
- Current projects:
- Oil slicks detection and classification.
- Keywords : deep Learning, SAR data analysis, heterogeneous data fusion, anomaly detection, semantic segmentation, classification.
- Industrial partner : Total
- Gamma rays classification and parameters estimation.
- Keywords : deep Learning, astrophysics, stereoscopy, classification, regression.
- Academic partner: LAPP
- Industrial partner : Orobix
- Oil slicks detection and classification.
- Related to my research activities:
- Image and data processing with tools such as Matlab, and Python, OpenCV
- Deep larning with Tensorflow.
- Target students : PhD, Master and Bachelor degree and employed engineers.
- General teaching activities:
- Programming : scripting and object programming with C/C++/Java/Python/PHP, and others.
- Modeling (UML, etc.)
- Experience highlights :
- image processing and machine learning to PhD students and Master 2 at University Savoie Mont Blanc.
- machine learning at ESEN engineering school at Tunis (Tunisia)
- machine learning at Renault compagny (Paris, Versailles)
- Introducing machine learning in digital processes in the industry.
- Data pre-processing, features extraction and decision making for classification, regression and other tasks for image, video and time series understanding.
- Experience highlights :
- Machine Learning and Deep Learning at Renault (Paris, Versailles)
- Deep Learning at Total company(Paris)
- Machine Learning at AboutGoods Company and Inventhys (Annecy)
- Machine Learning with startups (Heliocity, Skiply and so on) along implications into the IDEFICS project
PhD thesis co-supervising :
- 2018-2021 Emna Amri
Topic : Automatic Oil Slicks detection and classification from Remote Sensing imaging using Deep Learning.
Funding : Total company.
- 2017-2020 Michael Dell’Aiera
Topic : Semi supervised Deep neural networks for gamma rays analysis.
Funding : H2020 ESCAPE project and LAPP lab collaboration.
- 2017-2020 Mickael Jacquemont
Topic : Deep neural networks for the 3D analysis of gamma rays.
Funding : H2020 ASTERICS project and the USMB fondation.
- 2015-2019 Amina Ben Hamida
Topic : Indexing of large Remote sensing images.
Funding : French research ministry.
- 2014-2019 Rizlène Raoui
Topic : Automatic Sales receipt understanding using Deep Learing.
Funding : CIFRE with AboutGoods Compagny.
- 2013-2016 Nicolas Voiron
Topic : Automating clustering of multimedia data bases for exploration.
Funding : USMB
- 2010-2013 Sabin Tiberius Strat
Topic : Automating understanding of image sequences using a collaborative approach
Funding : French research ministry
- 2015 8th International Workshop on the Analysis of Multitemporal Remote Sensing Images, July 22-24, 2015 Annecy, France
- 2012 10th Workshop on Content-Based Multimedia Indexing (CBMI2012), 27-29 juin 2012, Annecy le Vieux, France.
- 2011 7th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT 2011) July 18-22, 2011, jointly with Les Rencontres Francophones sur la Logique Floue et ses Applications (LFA 2011, aka French Days on Fuzzy Logic and Applications) in Aix-les-Bains, France.
IEEE Transactions on Image Processing ; Elsevier Computer Vision and Image Understanding ; Elsevier Signal Processing :Image Communication ; Multimedia Tools and APplications (MTAP) ; International Journal of Neural Systems ; MDPI Open Access Journal – Remote Sensing ; Hindawi – JournalOfRobotics ; ISPRS Journal of Photogrammetry and Remote Sensing ; JSTARS – GRSS | IEEE | Geoscience & Remote Sensing Society ; Integrated Computer-Aided Engineering
Main ones : IEEE International Conference on Image Processing ; International Workshop on Content-Based Multimedia Indexing ; JURSE – Joint Urban Remote Sensing Event ;
2018 – 2019 co coordination of the MAIM at GDR ISIS.
2015 – 2017 co coordination of the IRIM at GDR ISIS..
- a bio-inspired retina model for image pre-processing. contributed to the OpenCV library (C++/Python):
- Deep Learning frameworks and related tools :
- pyTorch based framework to conduct versioned experiments, codes&docs. This is developed and applied in the context of a phD focused on gamma rays detection and analysis in the context of the internationnal Cherenkov Telescope Array project. The proposed framework can be applied to any image processing problem.
- pyTorch indexed convolutions than enable any kind of kernels and pixel sampling grids : doc. This is applied to hexagonal images processing in the Cherenkov Telescope Array project.
- Tensorflow based framework to conduct versioned experiments, codes&docs. Several examples provided for image segmentation, curves regression, GAN, etc.
- caffe based deep learning model for hyperspectral images analysis using lightweight 3D convolutions based networks codes, paper