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18 results on '"Département lmage et Traitement Information (IMT Atlantique - ITI)"'

1. Pure Versus Hybrid Transformers For Multi-Modal Brain Tumor Segmentation: A Comparative Study

Author

Andrade-Miranda, Gustavo, Jaouen, Vincent, Bourbonne, Vincent, Lucia, François, Visvikis, Dimitris, Conze, Pierre-Henri, Conze, Pierre-Henri, Laboratoire de Traitement de l'Information Medicale (LaTIM), Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut Brestois Santé Agro Matière (IBSAM), Université de Brest (UBO), Département lmage et Traitement Information (IMT Atlantique - ITI), IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), and Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)

Subjects
[INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience

Published
2022

2. Coarse-to-Fine Registration of Airborne LiDAR Data and Optical Imagery on Urban Scenes

Author

Thanh Huy Nguyen, Sylvie Daniel, Didier Gueriot, Christophe Sintes, Jean-Marc Le Caillec, Université Laval [Québec] (ULaval), Lab-STICC_IMTA_CID_PRASYS, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT), Centre de Recherche en Géomatique [Laval] (CRG), Département lmage et Traitement Information (IMT Atlantique - ITI), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), and Lab-STICC_IMTA_CID_TOMS

Subjects
FOS: Computer and information sciences, heterogeneous sensors, QC801-809, Computer Vision and Pattern Recognition (cs.CV), Image and Video Processing (eess.IV), Geophysics. Cosmic physics, Computer Science - Computer Vision and Pattern Recognition, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], mutual information (MI), Electrical Engineering and Systems Science - Image and Video Processing, GeneralLiterature_MISCELLANEOUS, coarse-to-fine, Ocean engineering, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], FOS: Electrical engineering, electronic engineering, information engineering, airborne LiDAR, Aerial imagery, building extraction, TC1501-1800
Abstract

Applications based on synergistic integration of optical imagery and LiDAR data are receiving a growing interest from the remote sensing community. However, a misaligned integration between these datasets may fail to fully profit the potential of both sensors. In this regard, an optimum fusion of optical imagery and LiDAR data requires an accurate registration. This is a complex problem since a versatile solution is still missing, especially when considering the context where data are collected at different times, from different platforms, under different acquisition configurations. This paper presents a coarse-to-fine registration method of aerial/satellite optical imagery with airborne LiDAR data acquired in such context. Firstly, a coarse registration involves extracting and matching of buildings from LiDAR data and optical imagery. Then, a Mutual Information-based fine registration is carried out. It involves a super-resolution approach applied to LiDAR data, and a local approach of transformation model estimation. The proposed method succeeds at overcoming the challenges associated with the aforementioned difficult context. Considering the experimented airborne LiDAR (2011) and orthorectified aerial imagery (2016) datasets, their spatial shift is reduced by 48.15% after the proposed coarse registration. Moreover, the incompatibility of size and spatial resolution is addressed by the mentioned super-resolution. Finally, a high accuracy of dataset alignment is also achieved, highlighted by a 40-cm error based on a check-point assessment and a 64-cm error based on a check-pair-line assessment. These promising results enable further research for a complete versatile fusion methodology between airborne LiDAR and optical imagery data in this challenging context., Comment: 20 pages, 15 figures. Accepted to be published in IEEE JSTARS

Published
2020

3. Possibilistic Similarity Measures for Data Science and Machine Learning Applications

Author

Sonda Ammar Bouhamed, Amal Charfi, Wassim Bouchaala, Basel Solaiman, Imene Khanfir Kallel, Eloi Bosse, Nabil Derbel, Département de Génie Électrique de Sfax [ENIS] (CEM Lab - ENIS), École Nationale d'Ingénieurs de Sfax | National School of Engineers of Sfax (ENIS), Département lmage et Traitement Information (IMT Atlantique - ITI), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), and Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)

Subjects
0209 industrial biotechnology, measures of specificity, General Computer Science, Computer science, Context (language use), 02 engineering and technology, Similarity measure, Machine learning, computer.software_genre, Measure (mathematics), Set (abstract data type), 020901 industrial engineering & automation, Text mining, Similarity (network science), 0202 electrical engineering, electronic engineering, information engineering, Entropy (information theory), General Materials Science, Electrical and Electronic Engineering, uncertainty, distance, similarity, learning, business.industry, General Engineering, possibility distributions, Classification, Pattern recognition (psychology), 020201 artificial intelligence & image processing, Artificial intelligence, lcsh:Electrical engineering. Electronics. Nuclear engineering, business, entropy, computer, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, lcsh:TK1-9971
Abstract

SPECIAL SECTION ON MACHINE LEARNING DESIGNS, IMPLEMENTATIONS AND TECHNIQUES; International audience; Measuring similarity is of a great interest in many research areas such as in data sciences, machine learning, pattern recognition, text analysis and information retrieval to name a few. Literature has shown that possibility is an attractive notion in the context of distinguishability assessment and can lead to very efficient and computationally inexpensive learning schemes. This paper focuses on determining the similarity between two possibility distributions. A review of existing similarity measures within the possi-bilistic framework is presented first. Then, similarity measures are analyzed with respect to their capacity to satisfy a set of required properties that a similarity measure should own. Most of the existing possibilistic similarity measures produce undesirable outcomes since they generally depend on the application context. A new similarity measure, called InfoSpecificity, is introduced and the similarity measures are categorized into three main methods: morphic-based, amorphic-based and hybrid. Two experiments are being conducted using four benchmark databases. The aim of the experiments is to compare the efficiency of the possibilistic similarity measures when applied to real data. Empirical experiments have shown good results for the hybrid methods, particularly with the InfoSpecificity measure. In general, the hybrid methods outperform the other two categories when evaluated on small-size samples, i.e., poor-data context (or poor-informed environment) where possibility theory can be used at the greatest benefit.

Published
2020

4. Authenticating IDS autoencoders using multipath neural networks

Author

Marc-Oliver Pahl, Gouenou Coatrieux, Raphael M.J.I. Larsen, Fontaine, Nathalie, Département lmage et Traitement Information (IMT Atlantique - ITI), IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Laboratoire de Traitement de l'Information Medicale (LaTIM), Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut Brestois Santé Agro Matière (IBSAM), Université de Brest (UBO), Département Systèmes Réseaux, Cybersécurité et Droit du numérique (IMT Atlantique - SRCD), and This work was supported by the Cyber CNI Chair and IMT Atlantique, Bretagne-Pays de la Loire

Subjects
Sensor systems, Artificial neural networks, Artificial neural network, Computer science, business.industry, Measurement uncertainty, ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS, Integrated circuits, Pattern recognition, [INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR], Security, Intrusion detection, Artificial intelligence, Sensor phenomena and characterization, business, Multipath propagation, [INFO.INFO-CR] Computer Science [cs]/Cryptography and Security [cs.CR]
Abstract

International audience; An Intrusion Detection System (IDS) is a core element for securing critical systems. An IDS can use signatures of known attacks, or an anomaly detection model for detecting unknown attacks. Attacking an IDS is often the entry point of an attack against a critical system. Consequently, the security of IDSs themselves is imperative. To secure model-based IDSs, we propose a method to authenticate the anomaly detection model. The anomaly detection model is an autoencoder for which we only have access to input-output pairs. Inputs consist of time windows of values from sensors and actuators of an Industrial Control System. Our method is based on a multipath Neural Network (NN) classifier, a newly proposed deep learning technique. The idea is to characterize errors of an IDS's autoencoder by using a multipath NN's confidence measure c. We use the Wilcoxon-Mann-Whitney (WMW) test to detect a change in the distribution of the summary variable c, indicating that the autoencoder is not working properly. We compare our method to two baselines. They consist in using other summary variables for the WMW test. We assess the performance of these three methods using simulated data. Among others, our analysis shows that: 1) both baselines are oblivious to some autoencoder spoofing attacks while 2) the WMW test on a multipath NN's confidence measure enables detecting eventually any autoencoder spoofing attack.

Published
2021

5. End-to-End Learning of Variational Models and Solvers for the Resolution of Interpolation Problems

Author

Ronan Fablet, Lucas Drumetz, François Rousseau, Unknown, User, Département Mathematical and Electrical Engineering (IMT Atlantique - MEE), IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Equipe Observations Signal & Environnement (Lab-STICC_OSE), Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT), Département lmage et Traitement Information (IMT Atlantique - ITI), Laboratoire de Traitement de l'Information Medicale (LaTIM), Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut Brestois Santé Agro Matière (IBSAM), Université de Brest (UBO), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), and Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique)

Subjects
Artificial neural network, inverse problems, Automatic differentiation, Computer science, Inpainting, [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], end-to-end, Inverse problem, Solver, Regularization (mathematics), regularization, missing data, Generative model, variational models, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], optimizer learning, Applied mathematics, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, Interpolation
Abstract

International audience; Variational models are among the state-of-the-art formulations for the resolution of ill-posed inverse problems. Following recent advances in learning-based variational settings, we investigate the end-to-end learning of variational models, more precisely of the regularization term given some observation model, jointly to the associated solver, so that we can optimize the reconstruction performance. In the proposed end-to-end setting, both the variational cost and the gradient-based solver are stated as neural networks using automatic differentiation for the latter. We consider an application to inverse problems with incomplete datasets (image inpainting and multivariate time series interpolation). We experimentally illustrate that this framework can lead to a significant gain in terms of reconstruction performance, including w.r.t. the direct minimization of the variational formulation derived from the known generative model.

Published
2021

6. Morphological-based microcalcification detection using adaptive thresholding and structural similarity indices

Author

Basel Solaiman, Mohamed Ali Mahjoub, Asma Touil, Karim Kalti, Pierre-Henri Conze, Département lmage et Traitement Information (IMT Atlantique - ITI), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Laboratory of Advanced Technology and Intelligent Systems (LATIS), Ecole Nationale d'Ingénieurs de Sousse (ENISo), Laboratoire de Traitement de l'Information Medicale (LaTIM), Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut Brestois Santé Agro Matière (IBSAM), and Université de Brest (UBO)

Subjects
Computer science, business.industry, Structural similarity, Pattern recognition, Mathematical morphology, Thresholding, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, breast cancer, 0302 clinical medicine, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, medicine, mathematical morphology, Semantic representation, structural similarity index, Artificial intelligence, Microcalcification, medicine.symptom, microcalcifications, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, 030217 neurology & neurosurgery
Abstract

International audience; In this paper, we propose a new morphological-based method for automatic detection of microcalcifications in digitized mammograms. It uses various structuring elements to deal with the diversity of microcalcification characteristics. The obtained morphological maps are converted to a continuous suspicion map (SM) based on the structural similarity index (SSIM). This new semantic representation map is then locally analyzed, using superpixels, to automatically estimate adaptive threshold values and finally identify potential microcalcification areas. The proposed method was evaluated using the publicly-available INBreast database. Experimental results show the benefits gained in terms of improving microcalcification detection performances compared to some state-of-the-art methods.

Published
2020

7. Temporal resolution enhancement of dynamic MRI sequences within a motion-based framework

Author

François Rousseau, Bhushan Borotikar, Karim Makki, D. Ben Salem, M. Garetier, Sylvain Brochard, Département lmage et Traitement Information (IMT Atlantique - ITI), IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Laboratoire de Traitement de l'Information Medicale (LaTIM), Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut Brestois Santé Agro Matière (IBSAM), Université de Brest (UBO), Service de Radiologie [Brest] (HIA - Radio), Hôpital d'Instruction des armées, CHRU Brest - Service de médecine physique et de réadaptation (CHU Brest - Service de médecine physique ), Centre Hospitalier Régional Universitaire de Brest (CHRU Brest), Hôpital de la Cavale Blanche - CHRU Brest (CHU - BREST ), This work was supported by Région Bretagne, Chaire d’excellence INSERM-IMT Atlantique, Fondation de l’Avenir, and Fondation Motrice. Code: https://github.com/rousseau/dynMRI, Rousseau, François, IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), and Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique)

Subjects
[SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Computer science, Dynamic MRI, Physics::Medical Physics, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, 010103 numerical & computational mathematics, Iterative reconstruction, Tracking (particle physics), intensity-based re- gistration, 01 natural sciences, 010305 fluids & plasmas, Motion, Motion artifacts, 0103 physical sciences, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, medicine, Humans, eigenvalue, Computer vision, Matrix analysis, 0101 mathematics, Child, ComputingMilieux_MISCELLANEOUS, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, diffeomorphisms, medicine.diagnostic_test, business.industry, Magnetic resonance imaging, matrix analysis, tracking, Image Enhancement, Magnetic Resonance Imaging, [SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging, Temporal resolution, Dynamic contrast-enhanced MRI, Trajectory, Artificial intelligence, Artifacts, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Algorithms, Ankle Joint, Interpolation
Abstract

Dynamic MRI has made it possible to non-invasively capture the moving human joints in vivo. Real-time Fast Field Echo (FFE) sequences have the potential to reduce the effect of motion artifacts by acquiring the image data within a few milliseconds. However, the short acquisition times affect the temporal resolution of the acquired sequences. In this paper, we propose a post-processing technique to reconstruct the missing frames of the sequence given the reduced amount of acquired data, which leads to recover the entire joint trajectory outside the MR scanner. To do this, we generalize the Log-Euclidean polyrigid registration framework to deal with dynamic three-dimensional articulated structures by adding the time as fourth dimension : we first estimate the rigid motion of each bone from the acquired data using linear intensity-based registration. Then, we fuse these local transformations to compute the non-linear joint deformations between successive images using a spatio-temporal log-euclidean polyrigid framework. The idea is to reconstruct the missing time frames by interpolating the realistic joint deformation fields in the domain of matrix logarithms assuming the motion to be consistent over a short period of time. The algorithm has been applied and validated using dynamic data from five children performing passive ankle dorsi-plantar flexion.

Published
2019

8. Learning Differential Transport Operators for the Joint Super-Resolution of Sea Surface Tracers and Prediction of Subgrid-Scale Features

Author

Lucas Drumetz, B. Chapron, François Rousseau, J. Le Sommer, Ronan Fablet, J. M. Molines, Lab-STICC_IMTA_CID_TOMS, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT), Département Signal et Communications (IMT Atlantique - SC), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Institut des Géosciences de l’Environnement (IGE), Institut de Recherche pour le Développement (IRD)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Laboratoire de Traitement de l'Information Medicale (LaTIM), Institut National de la Santé et de la Recherche Médicale (INSERM)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-Université de Brest (UBO)-Institut Brestois Santé Agro Matière (IBSAM), Université de Brest (UBO), and Département lmage et Traitement Information (IMT Atlantique - ITI)

Subjects
010504 meteorology & atmospheric sciences, Artificial neural network, Computer science, business.industry, Anomaly (natural sciences), Deep learning, Sampling (statistics), 02 engineering and technology, Variance (accounting), Sea-surface height, 01 natural sciences, Sea surface temperature, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Artificial intelligence, business, Scale (map), [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Algorithm, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences
Abstract

This work deals with data-driven and learning-based approaches to fill space-time sampling gaps in the observation of sea surface tracers such as Sea Surface Height (SSH), Sea Surface temperature (SST), Ocean Colour,… More precisely, we jointly address field super-resolution and the prediction of subgrid-scale features, which is novel to our knowledge. From a methodological point of view, we consider deep learning architectures with a view to learning geophysically-sound differential operators (i.e. trasnport operators). Based on an Observing System Simulation Experiment representative of SWOT fast sampling phase using NATL60 simulation data, we illustrate the relevance of the proposed methodological framework which reconstructs more than 90% of the variance of the high-resolution SSH anomaly field and above 90% of the subgrid-scale variance of this anomaly. We also illustrate significant gain w.r.t. other baseline neural network architectures and further discuss the relevance of the reported contribution for other tracer fields and case studies.

Published
2019

9. Marine Bird Detection Based on Deep Learning using High-Resolution Aerial Images

Author

Frederic Maussang, Alexis Chevallier, Rene Garello, Lynda Ben Boudaoud, Département lmage et Traitement Information (IMT Atlantique - ITI), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Lab-STICC_IMTA_CID_TOMS, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT), and Setec in vivo (Entreprise)

Subjects
0106 biological sciences, business.industry, Computer science, Deep learning, Supervised learning, Pattern recognition, 02 engineering and technology, Real image, 010603 evolutionary biology, 01 natural sciences, Convolutional neural network, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Artificial intelligence, Architecture, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Feature learning, Classifier (UML), ComputingMilieux_MISCELLANEOUS, Test data
Abstract

The overarching goal of this paper is to find an automatic bird detection and counting method on aerial images of the ocean. Most of the existing works in the literature are based on heuristic handcrafted feature design, which in most cases affect the effectiveness (the accuracy of classification) and the efficiency (spending much time). In this paper, we propose a method built on a systematic feature learning based classification adopting a new deep Convolutional Neural Network (CNN) architecture. Through this architecture, the feature learning is automated from a multi-dimensional raw input images, by a training step leveraging the JONATHAN dataset via supervised learning. Performances evaluation show that the CNN based architecture classifier achieves an accuracy of 95% on the JONATHAN test data and the overall detection method achieves a classification rate (true positives: birds) of 98% on real images.

Published
2019

10. Simultaneous Super-Resolution and Segmentation Using a Generative Adversarial Network: Application to Neonatal Brain MRI

Author

Carlos Tor-Díez, Hélène Meunier, Chi-Hieu Pham, Nicolas Passat, François Rousseau, Ronan Fablet, Nathalie Bednarek, Laboratoire de Traitement de l'Information Medicale (LaTIM), Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut Brestois Santé Agro Matière (IBSAM), Université de Brest (UBO), Département lmage et Traitement Information (IMT Atlantique - ITI), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Service de médecine néonatale et réanimation pédiatrique, CHU de Reims, Centre de Recherche en Sciences et Technologies de l'Information et de la Communication - EA 3804 (CRESTIC), Université de Reims Champagne-Ardenne (URCA), Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT), Département Signal et Communications (IMT Atlantique - SC), ANR-15-CE23-0009,MAIA,Analyse multiphysiques fondée sur l'imagerie pour la compréhension du développement cérébral des prématurés(2015), Institut National de la Santé et de la Recherche Médicale (INSERM)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-Université de Brest (UBO)-Institut Brestois Santé Agro Matière (IBSAM), Institut Mines-Télécom [Paris] (IMT)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL), Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-IMT Atlantique (IMT Atlantique), IMT Atlantique (IMT Atlantique), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique), Passat, Nicolas, and Analyse multiphysiques fondée sur l'imagerie pour la compréhension du développement cérébral des prématurés - - MAIA2015 - ANR-15-CE23-0009 - AAPG2015 - VALID

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Computer science, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, super-resolution, Iterative reconstruction, 3D generative adversarial networks, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 030218 nuclear medicine & medical imaging, 03 medical and health sciences, [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 0302 clinical medicine, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Neonatal brain, Segmentation, neonatal brain MRI, business.industry, segmentation, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Pattern recognition, Superresolution, Artificial intelligence, business, Generative adversarial network, 030217 neurology & neurosurgery, Interpolation
Abstract

International audience; The analysis of clinical neonatal brain MRI remains challenging due to low anisotropic resolution of the data. In most pipelines, images are first re-sampled using interpolation or single image super-resolution techniques and then segmented using (semi-)automated approaches. Image reconstruction and segmentation are then performed separately. In this paper, we propose an end-to-end generative adversarial network for simultaneous high-resolution reconstruction and segmentation of brain MRI data. This joint approach is first assessed on the simulated low-resolution images of the high-resolution neonatal dHCP dataset. Then, the learned model is used to enhance and segment real clinical low-resolution images. Results demonstrate the potential of our proposed method with respect to practical medical applications.

Published
2019

11. An adaptive semantic dimensionality reduction approach for hyperspectral imagery classification

Author

Rawaa Hamdi, Imed Riadh Farah, Akrem Sellami, Laboratoire de recherche en Génie Logiciel, Applications distribuées, Systèmes décisionnels et Imagerie intelligente [Manouba] (RIADI), École Nationale des Sciences de l'Informatique [Manouba] (ENSI), Université de la Manouba [Tunisie] (UMA)-Université de la Manouba [Tunisie] (UMA), Département lmage et Traitement Information (IMT Atlantique - ITI), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), and Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)

Subjects
Covariance matrices, Computer science, Principal component analysis, 0211 other engineering and technologies, 02 engineering and technology, Reduction (complexity), 0202 electrical engineering, electronic engineering, information engineering, Training, 021101 geological & geomatics engineering, Support vector machines, business.industry, Dimensionality reduction, Hyperspectral imaging, Pattern recognition, Mutual information, Spectral bands, 15. Life on land, Semantics, Support vector machine, 020201 artificial intelligence & image processing, Artificial intelligence, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Curse of dimensionality
Abstract

International audience; Hyperspectral imagery (HSI) is widely used for several fields of remote sensing such as agriculture, land cover monitoring, and deforestation. However, the HSI classification is a challenge task due to the large number of spectral bands, unavailability of training samples, and the high correlation inter-bands. To address these challenges, we propose in this work a semantic reduction dimensionality approach based on the principal component analysis (PCA) and mutual information-based band selection (MI). Firstly, we project the original HSI using PCA to obtain a novel subspace with lower dimensions. Using the obtained components, a set of rules can be generated to find the relevant spectral bands based on score contribution coefficient. Moreover, the mutual information (MI) is used to select the spectral bands that contain a higher information based on the entropy criterion. We propose then to exploit the selected bands for HSI classification using SVM technique. Experiment results demonstrate that our proposed approach is effective and perform for HSI classification compared to other dimensionality reduction approaches.

Published
2018

12. Battery-free force sensor for instrumented knee implant

Author

Shaban Almouahed, Chafiaa Hamitouche, Eric Stindel, Philippe Poignet, Conception et commande de robots pour la manipulation (DEXTER), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Département lmage et Traitement Information (IMT Atlantique - ITI), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Laboratoire de Traitement de l'Information Medicale (LaTIM), Institut National de la Santé et de la Recherche Médicale (INSERM)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-Université de Brest (UBO)-Institut Brestois Santé Agro Matière (IBSAM), Université de Brest (UBO), Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), and Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut Brestois Santé Agro Matière (IBSAM)

Subjects
Battery (electricity), Radio transmitter design, Battery-Free Force Sensor, 030222 orthopedics, 0209 industrial biotechnology, Instrumented Knee Implant, Computer science, business.industry, Electrical engineering, [SPI.MECA.BIOM]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Biomechanics [physics.med-ph], 02 engineering and technology, Piezoelectricity, Power (physics), 03 medical and health sciences, 020901 industrial engineering & automation, 0302 clinical medicine, Radio frequency, Electric power, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Energy harvesting, Electronic circuit
Abstract

International audience; Energy harvesting inside human body is crucial for powering implantable long-term biomedical devices. In this paper, the performance of a piezoelectric energy harvester embedded within custom-designed knee implant in powering RF transmitter was evaluated through simulations. This power harvester is composed of four piezoelectric generators along with four off-the-shelf power conditioning circuits. It can harvest electric power from dynamic forces dissipated inside knee implant during walking. The results demonstrate the possibility to deliver a constant electric power of 59.4mW during a simulated walking cycle. This conditioned power can operate an off-the-shelf ultra-low power RF transmitter. This may eliminate the need for rechargeable batteries to power instrumented knee implants. The proposed piezoelectric generator along with the ultra-miniature power conditioning and telemetry circuits may be integrated within the knee implant without increasing its physical size or changing its geometrical shape significantly with respect to the conventional one. Therefore, its dynamic functionality and mechanical longevity may not be affected.

Published
2017

13. On the use of a FHSS modulated signal in multi-users underwater detection context

Author

Rene Garello, Frederic Maussang, Benjamin Ollivier, Lab-STICC_IMTA_CID_TOMS, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), Institut Mines-Télécom [Paris] (IMT)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-Institut Mines-Télécom [Paris] (IMT)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL), Département lmage et Traitement Information (IMT Atlantique - ITI), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), and Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)

Subjects
Underwater positioning, 020301 aerospace & aeronautics, Engineering, business.industry, Transmitter, Context (language use), 02 engineering and technology, PSK modulation, Correlation function (quantum field theory), Grid, Signal, Spread spectrum, Time of arrival, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, 0203 mechanical engineering, Correlation function, Modulation (music), Electronic engineering, Time of Arrival (TOA), FHSS, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; In order to detect and locate a receiver relative to a grid of transmitters in an underwater environment, it is necessary to detect each signal emitted by each transmitter and estimate its Time Of Arrival (TOA) and estimate the position through triangulation. The choice of the signals and their modulation is then crutial to have a good estimation of the TOA and reduce false alarms. This paper focuses on FHSS (Frequency Hopped Spread Spectrum) modulations and compares the performances of several codes, by using a criterion proposed in this paper. It proposes finally to adapt the best FHSS signal in a multi-users context.

Published
2017

14. Fuzzy hypergraph of concepts for semantic annotation of remotely sensed images

Author

Khitem Amiri, Imed Riadh Farah, Mohamed Farah, Laboratoire de recherche en Génie Logiciel, Applications distribuées, Systèmes décisionnels et Imagerie intelligente [Manouba] (RIADI), École Nationale des Sciences de l'Informatique [Manouba] (ENSI), Université de la Manouba [Tunisie] (UMA)-Université de la Manouba [Tunisie] (UMA), Département lmage et Traitement Information (IMT Atlantique - ITI), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), and Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)

Subjects
Information retrieval, Computer science, business.industry, Big data, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 0102 computer and information sciences, 02 engineering and technology, Ontology (information science), Semantics, 01 natural sciences, Image edge detection, Annotation, Automatic image annotation, 010201 computation theory & mathematics, Computer Science::Computer Vision and Pattern Recognition, Ontologies, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Representation (mathematics), business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Image retrieval, Semantic gap
Abstract

International audience; Annotation of images is largely studied in the literature and used in many application fields such as in image interpretation, indexation and retrieval. Manually annotating images gives valuable information on the semantic content of images, but is no longer acceptable when dealing with real corpora of images, especially in the era of big data. Content-based approaches had known great success to deal with large datasets, using low-level features such as color, texture, and shape, which are easy to compute automatically. Nonetheless, they suffer from the well known semantic gap problem, since they produce semantically very limited representations of images. In this paper, we propose a semantic image annotation approach that simultaneously handles contextual, spatial and spectral information of the image. We consider a predefined remotely sensed ontology and develop an annotation process that produces semantically rich hypergraphs representing objects in scenes, as well as their spatial and spectral attributes. We apply our approach to build a hypergraph corresponding to the Jasper Ridge AVIRIS image, showing the promising use of such representation in remote sensing.

Published
2017

15. Prediction of land use/land cover change methods: A study

Author

Houcine Essid, Imed Riadh Farah, Oumayma Bounouh, Laboratoire de recherche en Génie Logiciel, Applications distribuées, Systèmes décisionnels et Imagerie intelligente [Manouba] (RIADI), École Nationale des Sciences de l'Informatique [Manouba] (ENSI), Université de la Manouba [Tunisie] (UMA)-Université de la Manouba [Tunisie] (UMA), Département lmage et Traitement Information (IMT Atlantique - ITI), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), and Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)

Subjects
Support vector machines, 010504 meteorology & atmospheric sciences, Cover (telecommunications), Land use, Process (engineering), Computer science, Biological system modeling, Land cover, Remote sensing, 010501 environmental sciences, 15. Life on land, 01 natural sciences, Data science, Predictive models, Support vector machine, Satellite, Land use, land-use change and forestry, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Predictive modelling, 0105 earth and related environmental sciences
Abstract

International audience; Prediction of Land use and cover change using remotely sensed imagery has attracted huge attention. From several decades, multiple researchers have investigated different approaches. The complex nature of the land use change process, due to human-nature interactions and the singularities of satellite images, demands a well-studied approach. Yet, a synthesis document is needed to provide a synthetic director paper combining the proposed and/or used models, their advantages and drawbacks. Hence, such studies are required to face the huge demands of land cover changes prediction needs. Therefore, this paper presents a review of prediction models used for land cover change variability purposes. A classification scheme is proposed to enable better specification of current forecasting models.

Published
2017

16. Brain MRI super-resolution using deep 3D convolutional networks

Author

Chi-Hieu Pham, François Rousseau, Ronan Fablet, Aurelien Ducournau, Département lmage et Traitement Information (IMT Atlantique - ITI), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Laboratoire de Traitement de l'Information Medicale (LaTIM), Institut National de la Santé et de la Recherche Médicale (INSERM)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-Université de Brest (UBO)-Institut Brestois Santé Agro Matière (IBSAM), Université de Brest (UBO), Département Signal et Communications (IMT Atlantique - SC), Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), Institut Mines-Télécom [Paris] (IMT)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL), Lab-STICC_IMTA_CID_TOMS, Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-Institut Mines-Télécom [Paris] (IMT)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut Brestois Santé Agro Matière (IBSAM), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT), and Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique)

Subjects
medicine.diagnostic_test, Artificial neural network, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Computer science, business.industry, Magnetic resonance imaging, 02 engineering and technology, Real-time MRI, Superresolution, Convolutional neural network, 030218 nuclear medicine & medical imaging, Image (mathematics), 03 medical and health sciences, 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, Brain mri, medicine, 020201 artificial intelligence & image processing, Computer vision, Artificial intelligence, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Image resolution, Image restoration
Abstract

International audience; Example-based single image super-resolution (SR) has recently shown outcomes with high reconstruction performance. Several methods based on neural networks have successfully introduced techniques into SR problem. In this paper, we propose a three-dimensional (3D) convolutional neural network to generate high-resolution (HR) brain image from its input low-resolution (LR) with the help of patches of other HR brain images. Our work demonstrates the need of fitting data and network parameters for 3D brain MRI.

Published
2017

17. Generating data sets as inputs of reference for cyber security issues and industrial control systems

Author

Yvon Kermarrec, Benjamin Coste, Pedro Merino Laso, Thomas Becmeur, Xavier Boudvin, Gael Heno, David Brosset, Chaire cyberdéfense systèmes navals (Ecole Navale, IMT-Atlantique, THALES, DCNS), Institut de Recherche de l'Ecole Navale (IRENAV), Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM), Lab-STICC_TB_CID_SFIIS, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), Département lmage et Traitement Information (IMT Atlantique - ITI), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT), Département Systèmes Réseaux, Cybersécurité et Droit du numérique (IMT Atlantique - SRCD), Lab-STICC_IMTA_CID_IRIS, Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Département Logique des Usages, Sciences sociales et Sciences de l'Information (IMT Atlantique - LUSSI), Lab-STICC_IMTA_CID_DECIDE, Institut Mines-Télécom [Paris] (IMT)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL), and Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-Institut Mines-Télécom [Paris] (IMT)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique)

Subjects
Computer science, 010401 analytical chemistry, Context, ComputerApplications_COMPUTERSINOTHERSYSTEMS, 020206 networking & telecommunications, Context (language use), Integrated circuits, 02 engineering and technology, Industrial control system, Computer security, computer.software_genre, 01 natural sciences, Automata, 0104 chemical sciences, Automaton, [INFO.INFO-CY]Computer Science [cs]/Computers and Society [cs.CY], Order (exchange), Marine vehicles, General partnership, 0202 electrical engineering, electronic engineering, information engineering, Engines, computer, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; In this paper, we present a platform we have designed and built in order to generate data and scenario traces that can serve as inputs and references when evaluating our algorithms for detecting cyber security intrusions. Our context is related to civilian and military ships and our research is performed within a strong partnership with industry and academy. As obtaining actual and accurate data sets are not straightforward, we have chosen do generate realistic datasets with a platform we have designed and built.

Published
2017

18. Analysis of Quality Measurements to Categorize Anomalies in Sensor Systems

Author

David Brosset, John Puentes, Pedro Merino Laso, Lab-STICC_IMTA_CID_DECIDE, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT), Département lmage et Traitement Information (IMT Atlantique - ITI), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Chaire cyberdéfense systèmes navals (Ecole Navale, IMT-Atlantique, THALES, DCNS), Institut de Recherche de l'Ecole Navale (IRENAV), Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Arts et Métiers Sciences et Technologies, and HESAM Université (HESAM)-HESAM Université (HESAM)

Subjects
Computer science, media_common.quotation_subject, 02 engineering and technology, computer.software_genre, Cyber-physical system, 020204 information systems, [INFO.INFO-AU]Computer Science [cs]/Automatic Control Engineering, 0202 electrical engineering, electronic engineering, information engineering, Anomaly categorization, Quality (business), Information quality, media_common, Sensor, Data quality, 020206 networking & telecommunications, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, [INFO.INFO-PF]Computer Science [cs]/Performance [cs.PF], Categorization, Anomaly detection, Data mining, Focus (optics), computer, Wireless sensor network, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; Sensor networks are becoming ubiquitous, enabling to improve decision-making and reducing human interaction by means of automatic or semi-automatic responses. However, due to deterioration or induced effects, sensors measures can be affected and produce anomalies that could alter decision-making. Most of the existing methods to identify sensors irregularities focus basically on detecting and discarding anomalous values, without looking for complementary information to understand generated anomalies. This paper presents an approach to obtain such complementary information by categorizing sensor anomalies, based on multidimensional quality assessment. It consists of two processing stages: an evaluation of data and information streams to estimate data quality imperfections and information quality dimensions; followed by the determination of agreement limits, compliant with normal states, to identify and categorize anomalies. The case study of discrete and analog sensors system installed in a simulator training platform of fuel tanks is presented, to illustrate an application of the proposed approach, considering 13 experimentally evaluated anomalies.

Published
2017

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