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6. SHREC 2024: Recognition of dynamic hand motions molding clay.

Author

Ben Veldhuijzen, Remco C. Veltkamp, Omar Ikne, Benjamin Allaert, Hazem Wannous, Marco Emporio, Andrea Giachetti 0001, Joseph J. LaViola, Ruiwen He, Halim Benhabiles, Adnane Cabani, Anthony Fleury, Karim Hammoudi, Konstantinos Gavalas, Christoforos Vlachos, Athanasios Papanikolaou, Ioannis Romanelis, Vlassis Fotis, Gerasimos Arvanitis, Konstantinos Moustakas, Martin Hanik, Esfandiar Nava-Yazdani, and Christoph von Tycowicz

Published
2024
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11. SHREC 2021: Surface-based Protein Domains Retrieval.

Author

Florent Langenfeld, Tunde Aderinwale, Charles Christoffer, Woong-Hee Shin, Genki Terashi, Xiao Wang 0004, Daisuke Kihara, Halim Benhabiles, Karim Hammoudi, Adnane Cabani, Féryal Windal, Mahmoud Melkemi, Ekpo Otu, Reyer Zwiggelaar, David Hunter, Yonghuai Liu, Léa Sirugue, Huu-Nghia H. Nguyen, Tuan-Duy H. Nguyen, Vinh-Thuyen Nguyen-Truong, Danh Le, Hai-Dang Nguyen, Minh-Triet Tran, and Matthieu Montès

Published
2021
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13. Protein Shape Retrieval Contest.

Author

Florent Langenfeld, Apostolos Axenopoulos, Halim Benhabiles, Petros Daras, Andrea Giachetti 0001, Xusi Han, Karim Hammoudi, Daisuke Kihara, Tuan Manh Lai, Haiguang Liu, Mahmoud Melkemi, Stelios K. Mylonas, Genki Terashi, Yufan Wang, Féryal Windal, and Matthieu Montès

Published
2019
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16. SHREC 2020: Multi-domain protein shape retrieval challenge.

Author

Florent Langenfeld, Yuxu Peng, Yu-Kun Lai, Paul L. Rosin, Tunde Aderinwale, Genki Terashi, Charles Christoffer, Daisuke Kihara, Halim Benhabiles, Karim Hammoudi, Adnane Cabani, Féryal Windal, Mahmoud Melkemi, Andrea Giachetti 0001, Stelios K. Mylonas, Apostolos Axenopoulos, Petros Daras, Ekpo Otu, and Matthieu Montès

Published
2020
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31. SuperpixelGridMasks Data Augmentation:Application to Precision Health and Other Real-world Data

Author

Karim Hammoudi, Adnane Cabani, Bouthaina Slika, Halim Benhabiles, Fadi Dornaika, Mahmoud Melkemi, Institut de Recherche en Informatique Mathématiques Automatique Signal - IRIMAS - UR 7499 [IRIMAS], École Supérieure d’Ingénieurs en Génie Électrique [ESIGELEC], Euskal Herriko Unibertsitatea [Guipúzcoa] [EHU], University of the Basque Country/Euskal Herriko Unibertsitatea [UPV/EHU], Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN], Bio-Micro-Electro-Mechanical Systems - IEMN [BIOMEMS - IEMN], JUNIA [JUNIA], Institut de Recherche en Informatique Mathématiques Automatique Signal - IRIMAS - UR 7499 (IRIMAS), Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA)), École Supérieure d’Ingénieurs en Génie Électrique (ESIGELEC), Euskal Herriko Unibertsitatea [Guipúzcoa] (EHU), Universidad del Pais Vasco / Euskal Herriko Unibertsitatea [Espagne] (UPV/EHU), University of the Basque Country/Euskal Herriko Unibertsitatea (UPV/EHU), Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), Université catholique de Lille (UCL)-Université catholique de Lille (UCL), Bio-Micro-Electro-Mechanical Systems - IEMN (BIOMEMS - IEMN), Université catholique de Lille (UCL)-Université catholique de Lille (UCL)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), JUNIA (JUNIA), and Université catholique de Lille (UCL)

Subjects
[SPI]Engineering Sciences [physics], Artificial Intelligence, Health Informatics, Computer Science Applications, Information Systems
Abstract

ressources projet: https://github.com/hammoudiproject/SuperpixelGridMasksdatasets used for the article: 1 Dataset Chest X-Ray Images: https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia2 A PASCAL VOC dataset: http://host.robots.ox.ac.uk/pascal/VOC/databases.html#VOC2005_1; International audience; A novel approach of data augmentation based on irregular superpixel decomposition is proposed. This approach called SuperpixelGridMasks permits to extend original image datasets that are required by training stages of machine learning-related analysis architectures towards increasing their performances. Three variants named SuperpixelGridCut, SuperpixelGridMean, and SuperpixelGridMix are presented. These grid-based methods produce a new style of image transformations using the dropping and fusing of information. Extensive experiments using various image classification models as well as precision health and surrounding real-world datasets show that baseline performances can be significantly outperformed using our methods. The comparative study also shows that our methods can overpass the performances of other data augmentations. SuperpixelGridCut, SuperpixelGridMean, and SuperpixelGridMix codes are publicly available at https://github.com/hammoudiproject/SuperpixelGridMasks.

Published
2023
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34. A CNN-based methodology for cow heat analysis from endoscopic images

Author

Agathe Decherf, Dominique Collard, Christophe Audebert, Ruiwen He, Abdelmalik Taleb-Ahmed, Feryal Windal, Halim Benhabiles, Gaël Even, Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), Université catholique de Lille (UCL)-Université catholique de Lille (UCL), Bio-Micro-Electro-Mechanical Systems - IEMN (BIOMEMS - IEMN), Université catholique de Lille (UCL)-Université catholique de Lille (UCL)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), JUNIA (JUNIA), Université catholique de Lille (UCL), Gènes Diffusion [Douai], Laboratory for Integrated Micro Mechatronics Systems (LIMMS), The University of Tokyo (UTokyo)-Centre National de la Recherche Scientifique (CNRS), COMmunications NUMériques - IEMN (COMNUM - IEMN), INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), This project has been funded by the FEDER European program, JUNIA French Engineering school and Genes Diffusion French company., Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), and Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)

Subjects
Endoscopic image, Computer science, Machine vision, Context (language use), Artificial insemination, 02 engineering and technology, Machine learning, computer.software_genre, 030218 nuclear medicine & medical imaging, Upsampling, [SPI]Engineering Sciences [physics], 03 medical and health sciences, 0302 clinical medicine, Artificial Intelligence, 0202 electrical engineering, electronic engineering, information engineering, Android (operating system), Quantization (image processing), 2. Zero hunger, business.industry, Response time, Deep learning, Android CNN optimization, Software deployment, Cow heat, 020201 artificial intelligence & image processing, Artificial intelligence, State (computer science), business, computer
Abstract

International audience; In cattle farming, the artificial insemination technique is a biotechnology that brings to farmers a wide range of benefits namely health security, genetic gain and economic costs. The main condition for the success of artificial insemination within cattle is the heat (or estrus) detection. In this context, several cow heat detection systems have been recently proposed in the literature to assist the farmer in this task. Nevertheless, they are mainly based on the analysis of the physical behavior of the cow which may be affected by several factors related to its health and its environment. In this paper, we present a new vision system for cow heat detection which is based on the analysis of the genital tract of the cow. The main core of our system is a CNN model that has been designed and tailored for analyzing endoscopic images collected using an innovative insemination technology named Eye breed. The conducted experiments on two datasets namely our own dataset and a public dataset show the high accuracy of our CNN model (more than 97% for both datasets) outperforming 19 methods from the state of the art. Moreover, we propose an optimized version of our model for an Android deployment by exploiting several techniques namely quantization, GPU acceleration and video downsampling. The conducted tests on a smart-phone shows that our heat detection system has a response time of a few seconds.

Published
2021
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35. A Hierarchical Deep Learning Framework for Nuclei 3D Reconstruction from Microscopic Stack-Images of 3D Cancer Cell Culture

Author

Tarek Maylaa, Feryal Windal, Halim Benhabiles, Gregory Maubon, Nathalie Maubon, Elodie Vandenhaute, Dominique Collard, Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), Université catholique de Lille (UCL)-Université catholique de Lille (UCL), Bio-Micro-Electro-Mechanical Systems - IEMN (BIOMEMS - IEMN), Université catholique de Lille (UCL)-Université catholique de Lille (UCL)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), JUNIA (JUNIA), Université catholique de Lille (UCL), HCS-Pharma Loos [Lille] (HCS-PL), Laboratory for Integrated Micro Mechatronics Systems (LIMMS), The University of Tokyo (UTokyo)-Centre National de la Recherche Scientifique (CNRS), AcknowledgementsThe authors would like to thank Mr. T. Delobelle, Mr. T. Dumont and Mr. R. Vanhee for their contribution in the implementation of the 3D reconstruction experimentations., FundingThis study was part of a Ph.D. thesis project funded by the European Regional Development Fund., Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN], Bio-Micro-Electro-Mechanical Systems - IEMN [BIOMEMS - IEMN], JUNIA [JUNIA], and Laboratory for Integrated Micro Mechatronics Systems [LIMMS]

Subjects
3D cell culture, Confocal microscopy, [PHYS]Physics [physics], [SPI]Engineering Sciences [physics], Segmentation, z-stack images, Deep learning, Object detection, 3D reconstruction
Abstract

International audience; AbstractIn this article, we propose a hierarchical deep learning framework for the nuclei 3D reconstruction from a stack of microscopic images representing 3D cancer cell culture. The framework goes through three successive stages namely: at the slice level of the stack (i) the spheroid detection and (ii) their nuclei segmentation then at the stack level (iii) the nuclei 3D reconstruction. For this purpose, we prepared a dataset of bright-field microscopic images acquired from 3D cultures of HeLa cells and manually annotated by the experts for both tasks (spheroids detection and nuclei segmentation). Two CNN models namely, YOLOv5x and U-Net-VGG19 have been trained and validated on our dataset for the detection and the segmentation tasks, respectively. For the 3D reconstruction task, the delaunay triangulation technique has been adopted by exploiting point cloud clusters that represent the segmented nuclei in the stack. Our framework offers to the biologists an efficient assisting tool for quantifying the number of spheroids and analyzing the morphology of their nuclei. The conducted experiments on our generated dataset show the promising results obtained by our framework with notably an average precision of 0.892 and 0.76 on the spheroids detection and nuclei segmentation respectively. Moreover, our 3D reconstruction technique shows visually a consistant representation of nuclei in term of volumetery and shape

Published
2022
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36. An evaluation of computational learning-based methods for the segmentation of nuclei in cervical cancer cells from microscopic images

Author

Tarek Maylaa, Feryal Windal, Halim Benhabiles, Gregory Maubon, Nathalie Maubon, Elodie Vandenhaute, Dominique Collard, Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), Université catholique de Lille (UCL)-Université catholique de Lille (UCL), Bio-Micro-Electro-Mechanical Systems - IEMN (BIOMEMS - IEMN), Université catholique de Lille (UCL)-Université catholique de Lille (UCL)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), JUNIA (JUNIA), Université catholique de Lille (UCL), HCS-Pharma Loos [Lille] (HCS-PL), Laboratory for Integrated Micro Mechatronics Systems (LIMMS), The University of Tokyo (UTokyo)-Centre National de la Recherche Scientifique (CNRS), and FEDER HCS Pharma

Subjects
BIOMIMESYS, [PHYS]Physics [physics], High Content Screening, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Uterine Cervical Neoplasms, General Medicine, Majority Voting, Cellular Structures, Machine Learning, [SPI]Engineering Sciences [physics], Segmentation, Drug Discovery, Image Processing, Computer-Assisted, Humans, Molecular Medicine, Z-Stack, Female, Metrics, Algorithms
Abstract

Background: The manual segmentation of cellular structures on Z-stack microscopic images is time-consuming and often inaccurate, highlighting the need to develop auto-segmentation tools to facilitate this process. Objective: This study aimed to compare the performance of three different machine learning archi-tectures, including random forest (RF), AdaBoost, and multi-layer perceptron (MLP), for the auto-segmentation of nuclei in proliferating cervical cancer cells on Z-Stack cellular microscopy prolif-eration images provided by the HCS Pharma. The impact of using post-processing techniques, such as the StarDist plugin and majority voting, was also evaluated. Methods: The RF, AdaBoost, and MLP algorithms were used to auto-segment the nuclei of cervi-cal cancer cells on microscopic images at different Z-stack positions. Post-processing techniques were then applied to each algorithm. The performance of all algorithms was compared by an expert to globally generated ground truth by calculating the accuracy detection rate, the Dice coefficient, and the Jaccard index. Results: RF achieved the best accuracy, followed by the AdaBoost and then the MLP. All algo-rithms achieved good pixel classifications except in regions whereby the nuclei overlapped. The majority voting and StarDist plugin improved the accuracy of the segmentation but did not resolve the nuclei overlap issue. The Z-Stack analysis revealed similar segmentation results to the Z-stack layer used to train the image. However, a worse performance was noted for segmentations per-formed on different Z-stack positions, which were not used to train the algorithms. Conclusion: All machine learning architectures provided a good segmentation of nuclei in cervical cancer cells but did not resolve the problem of overlapping nuclei and Z-stack segmentation. Fur-ther research should therefore evaluate the combined segmentation techniques and deep learning architectures to resolve these issues.

Published
2022
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39. Surface-based protein domains retrieval methods from a SHREC2021 challenge

Author

Florent Langenfeld, Tunde Aderinwale, Charles Christoffer, Woong-Hee Shin, Genki Terashi, Xiao Wang, Daisuke Kihara, Halim Benhabiles, Karim Hammoudi, Adnane Cabani, Feryal Windal, Mahmoud Melkemi, Ekpo Otu, Reyer Zwiggelaar, David Hunter, Yonghuai Liu, Léa Sirugue, Huu-Nghia H. Nguyen, Tuan-Duy H. Nguyen, Vinh-Thuyen Nguyen-Truong, Danh Le, Hai-Dang Nguyen, Minh-Triet Tran, Matthieu Montès, Laboratoire Génomique, bioinformatique et chimie moléculaire (GBCM), Conservatoire National des Arts et Métiers [CNAM] (CNAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM), Department of Computer Science [Purdue], Purdue University [West Lafayette], Suncheon National University [Suncheon, Corée du Sud], Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), Université catholique de Lille (UCL)-Université catholique de Lille (UCL), Bio-Micro-Electro-Mechanical Systems - IEMN (BIOMEMS - IEMN), Université catholique de Lille (UCL)-Université catholique de Lille (UCL)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), JUNIA (JUNIA), Université catholique de Lille (UCL), Institut de Recherche en Informatique Mathématiques Automatique Signal - IRIMAS - UR 7499 (IRIMAS), Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA)), Université de Strasbourg (UNISTRA), École Supérieure d’Ingénieurs en Génie Électrique (ESIGELEC), Aberystwyth University, Edge Hill University, Vietnam National University - Ho Chi Minh City (VNU-HCM), and Léa Sirugue, Matthieu Montès and Florent Langenfeld are supported by the European Research Council Executive Agency under the research grant number 640,283. Daisuke Kihara acknowledges supports from the National Institutes of Health (R01GM133840, R01GM123055) and the National Science Foundation (DBI2003635, CMMI1825941, and MCB1925643). Charles Christoffer is supported by NIGMS-funded pre–doctoral fellowship (T32 GM132024). Huu-Nghia H. Nguyen, Tuan-Duy H. Nguyen, Vinh-Thuyen Nguyen-Truong, Danh Le, Hai-Dang Nguyen, and Minh-Triet Tran are supported by National University Ho Chi Minh City (VNU-HCM) (DS2020-42-01).

Subjects
Models, Molecular, Static Electricity, Proteins, Ligands, Computer Graphics and Computer-Aided Design, Article, Proteins surface, [SPI]Engineering Sciences [physics], SHREC2021, Protein Domains, Materials Chemistry, Physical and Theoretical Chemistry, Spectroscopy, 2000 MSC: 92-08
Abstract

publication dans une revue suite à la communication hal-03467479 (SHREC 2021: surface-based protein domains retrieval); International audience; Proteins are essential to nearly all cellular mechanism and the effectors of the cells activities. As such, they often interact through their surface with other proteins or other cellular ligands such as ions or organic molecules. The evolution generates plenty of different proteins, with unique abilities, but also proteins with related functions hence similar 3D surface properties (shape, physico-chemical properties, …). The protein surfaces are therefore of primary importance for their activity. In the present work, we assess the ability of different methods to detect such similarities based on the geometry of the protein surfaces (described as 3D meshes), using either their shape only, or their shape and the electrostatic potential (a biologically relevant property of proteins surface). Five different groups participated in this contest using the shape-only dataset, and one group extended its pre-existing method to handle the electrostatic potential. Our comparative study reveals both the ability of the methods to detect related proteins and their difficulties to distinguish between highly related proteins. Our study allows also to analyze the putative influence of electrostatic information in addition to the one of protein shapes alone. Finally, the discussion permits to expose the results with respect to ones obtained in the previous contests for the extended method. The source codes of each presented method have been made available online.

Published
2022
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40. Deep Learning on Chest X-ray Images to Detect and Evaluate Pneumonia Cases at the Era of COVID-19

Author

Arnaud Scherpereel, Fadi Dornaika, Ignacio Arganda-Carreras, Mahmoud Melkemi, Karim Hammoudi, Dominique Collard, Halim Benhabiles, Hammoudi, Karim, Institut de Recherche en Informatique Mathématiques Automatique Signal (IRIMAS), Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA)), Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), Université catholique de Lille (UCL)-Université catholique de Lille (UCL), Bio-Micro-Electro-Mechanical Systems - IEMN (BIOMEMS - IEMN), Université catholique de Lille (UCL)-Université catholique de Lille (UCL)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), JUNIA (JUNIA), Université catholique de Lille (UCL), Universidad del Pais Vasco / Euskal Herriko Unibertsitatea [Espagne] (UPV/EHU), Ikerbasque - Basque Foundation for Science, University of the Basque Country/Euskal Herriko Unibertsitatea (UPV/EHU), Laboratory for Integrated Micro Mechatronics Systems (LIMMS), The University of Tokyo (UTokyo)-Centre National de la Recherche Scientifique (CNRS), Thérapies Laser Assistées par l'Image pour l'Oncologie - U 1189 (ONCO-THAI), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), and Basque Foundation for Science (Ikerbasque)

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, J.3, 020205 medical informatics, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, Medicine (miscellaneous), 02 engineering and technology, Disease, medicine.disease_cause, Health informatics, Machine Learning (cs.LG), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Health Information Management, Pandemic, 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-BT]Computer Science [cs]/Biotechnology, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM], Coronavirus, Image and Video Processing (eess.IV), Health scoring system, Coronavirus disease, 3. Good health, [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Viral pneumonia, Chest X-ray images (CXR), Radiography, Thoracic, Algorithms, Information Systems, [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], medicine.medical_specialty, Pneumonia, Viral, Image & Signal Processing, Health Informatics, Context (language use), X-ray, Deep Learning, FOS: Electrical engineering, electronic engineering, information engineering, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, medicine, Humans, pneumonia, image detection, Intensive care medicine, I.2.6, business.industry, X-Rays, Pneumonia detection, COVID-19, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Electrical Engineering and Systems Science - Image and Video Processing, I.4.9, medicine.disease, radiology, Pneumonia, [INFO.INFO-BT] Computer Science [cs]/Biotechnology, Infectious disease (medical specialty), Neural Networks, Computer, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], business
Abstract

Coronavirus disease 2019 (COVID-19) is an infectious disease with first symptoms similar to the flu. COVID-19 appeared first in China and very quickly spreads to the rest of the world, causing then the 2019-20 coronavirus pandemic. In many cases, this disease causes pneumonia. Since pulmonary infections can be observed through radiography images, this paper investigates deep learning methods for automatically analyzing query chest X-ray images with the hope to bring precision tools to health professionals towards screening the COVID-19 and diagnosing confirmed patients. In this context, training datasets, deep learning architectures and analysis strategies have been experimented from publicly open sets of chest X-ray images. Tailored deep learning models are proposed to detect pneumonia infection cases, notably viral cases. It is assumed that viral pneumonia cases detected during an epidemic COVID-19 context have a high probability to presume COVID-19 infections. Moreover, easy-to-apply health indicators are proposed for estimating infection status and predicting patient status from the detected pneumonia cases. Experimental results show possibilities of training deep learning models over publicly open sets of chest X-ray images towards screening viral pneumonia. Chest X-ray test images of COVID-19 infected patients are successfully diagnosed through detection models retained for their performances. The efficiency of proposed health indicators is highlighted through simulated scenarios of patients presenting infections and health problems by combining real and synthetic health data., Comment: 6 pages

Published
2021
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42. MaskedFace-Net – A dataset of correctly/incorrectly masked face images in the context of COVID-19

Author

Karim Hammoudi, Adnane Cabani, Mahmoud Melkemi, Halim Benhabiles, Institut de Recherche en Systèmes Electroniques Embarqués (IRSEEM), Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Normandie Université (NU)-École Supérieure d’Ingénieurs en Génie Électrique (ESIGELEC), Laboratoire International associé sur les phénomènes Critiques et Supercritiques en électronique fonctionnelle, acoustique et fluidique (LIA LICS/LEMAC), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF), Bio-Micro-Electro-Mechanical Systems - IEMN [BIOMEMS - IEMN], JUNIA [JUNIA], Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN], École Supérieure d’Ingénieurs en Génie Électrique [ESIGELEC], Institut de Recherche en Informatique Mathématiques Automatique Signal - IRIMAS - UR 7499 [IRIMAS], Bio-Micro-Electro-Mechanical Systems - IEMN (BIOMEMS - IEMN), Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), JUNIA (JUNIA), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), École Supérieure d’Ingénieurs en Génie Électrique (ESIGELEC), Institut de Recherche en Informatique Mathématiques Automatique Signal - IRIMAS - UR 7499 (IRIMAS), Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA)), Université de Strasbourg (UNISTRA), Université catholique de Lille (UCL)-Université catholique de Lille (UCL), Université catholique de Lille (UCL)-Université catholique de Lille (UCL)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), and Université catholique de Lille (UCL)

Subjects
FOS: Computer and information sciences, Masked face dataset, Smart health, 020205 medical informatics, Computer science, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, Medicine (miscellaneous), Health Informatics, Context (language use), 02 engineering and technology, Image editing, computer.software_genre, 01 natural sciences, Article, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Crowds, Health Information Management, FOS: Electrical engineering, electronic engineering, information engineering, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, 0202 electrical engineering, electronic engineering, information engineering, Net (polyhedron), Hardware_INTEGRATEDCIRCUITS, [INFO]Computer Science [cs], Face detection, ComputingMilieux_MISCELLANEOUS, Vulnerability (computing), Public health, business.industry, Feature matching, Deep learning, Image and Video Processing (eess.IV), 010401 analytical chemistry, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], COVID-19, Pattern recognition, Electrical Engineering and Systems Science - Image and Video Processing, 0104 chemical sciences, Computer Science Applications, Realistic image synthesis, Health education, Virus protection, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Face (geometry), Artificial intelligence, business, computer, Information Systems
Abstract

Wearing face masks appears as a solution for limiting the spread of COVID-19. In this context, efficient recognition systems are expected for checking that people faces are masked in regulated areas. Hence, a large dataset of masked faces is necessary for training deep learning models towards detecting people wearing masks and those not wearing masks. Currently, there are no available large dataset of masked face images that permits to check if faces are correctly masked or not. Indeed, many people are not correctly wearing their masks due to bad practices, bad behaviors or vulnerability of individuals (e.g., children, old people). For these reasons, several mask wearing campaigns intend to sensitize people about this problem and good practices. In this sense, this work proposes an image editing approach and three types of masked face detection dataset; namely, the Correctly Masked Face Dataset (CMFD), the Incorrectly Masked Face Dataset (IMFD) and their combination for the global masked face detection (MaskedFace-Net). Realistic masked face datasets are proposed with a twofold objective: i) detecting people having their faces masked or not masked, ii) detecting faces having their masks correctly worn or incorrectly worn (e.g.; at airport portals or in crowds). To the best of our knowledge, no large dataset of masked faces provides such a granularity of classification towards mask wearing analysis. Moreover, this work globally presents the applied mask-to-face deformable model for permitting the generation of other masked face images, notably with specific masks. Our datasets of masked faces (137,016 images) are available at https://github.com/cabani/MaskedFace-Net. The dataset of face images Flickr-Faces-HQ3 (FFHQ), publicly made available online by NVIDIA Corporation, has been used for generating MaskedFace-Net.

Published
2021
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43. Deep Learning-Based Classification of Pancreatic Adenocarcinoma from Fine Needle Aspiration/Biopsy Microscopic Images

Author

Feryal Windal, Thomas Lambin, Emmanuelle Leteurtre, Abdelhakim Azzouz, Dominique Collard, Romain Gerard, Meryem Tardivel, Oriane Karleskind, Halim Benhabiles, Antonino Bongiovanni, Bio-Micro-Electro-Mechanical Systems - IEMN (BIOMEMS - IEMN), Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), Université catholique de Lille (UCL)-Université catholique de Lille (UCL)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), Université catholique de Lille (UCL)-Université catholique de Lille (UCL), JUNIA (JUNIA), Université catholique de Lille (UCL), Institut de Pathologie [CHU Lille], Pôle de Biologie Pathologie Génétique [CHU Lille], Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), Plateformes Lilloises en Biologie et Santé - UAR 2014 - US 41 (PLBS), Institut Pasteur de Lille, Réseau International des Instituts Pasteur (RIIP)-Réseau International des Instituts Pasteur (RIIP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-Centre National de la Recherche Scientifique (CNRS), Hospices Civils de Lyon (HCL), 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), and Cancer Heterogeneity, Plasticity and Resistance to Therapies - UMR 9020 - U 1277 (CANTHER)

Subjects
medicine.medical_specialty, medicine.diagnostic_test, business.industry, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], medicine.disease, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Fine-needle aspiration, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Biopsy, medicine, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Adenocarcinoma, Radiology, business, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2021
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44. A generalized deep learning-based framework for assistance to the human malaria diagnosis from microscopic images

Author

Ziheng Yang, Karim Hammoudi, Ruiwen He, Feryal Windal, Halim Benhabiles, Dominique Collard, Department of Genetics, Evolution and Environment, University College of London [London] (UCL), Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), Université catholique de Lille (UCL)-Université catholique de Lille (UCL), JUNIA (JUNIA), Université catholique de Lille (UCL), Bio-Micro-Electro-Mechanical Systems - IEMN (BIOMEMS - IEMN), Université catholique de Lille (UCL)-Université catholique de Lille (UCL)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), Institut de Recherche en Informatique Mathématiques Automatique Signal (IRIMAS), Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA)), Laboratory for Integrated Micro Mechatronics Systems (LIMMS), The University of Tokyo (UTokyo)-Centre National de la Recherche Scientifique (CNRS), This project has received funding from the Interreg 2 Seas programme 2014-2020 co-funded by the European Regional Development Fund under subsidy contract No. 2S05-043 H4DC., and Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA)

Subjects
Computer science, Machine learning, computer.software_genre, Plasmodium, World health, 03 medical and health sciences, [SPI]Engineering Sciences [physics], 0302 clinical medicine, Artificial Intelligence, parasitic diseases, medicine, Computational Science and Engineering, 030304 developmental biology, 0303 health sciences, biology, business.industry, Deep learning, Plasmodium parasite, biology.organism_classification, medicine.disease, 3. Good health, Blood smear, Infectious disease (medical specialty), 030220 oncology & carcinogenesis, Artificial intelligence, business, computer, Software, Malaria
Abstract

International audience; Malaria is an infectious disease caused by Plasmodium parasites and is potentially human life-threatening. Children under 5 years old are the most vulnerable group with approximately one death every two minutes, accounting for more than 65% of all malaria deaths. The World Health Organization (WHO) encourages the research of appropriate methods to treat malaria through rapid and economical diagnostic. In this paper, we present a deep learning-based framework for diagnosing human malaria infection from microscopic images of thin blood smears. The framework is based on a direct segmentation and classification approach which relies on the analysis of the parasite itself. The framework permits to segment the Plasmodium parasite in the images and to predict its species among four dominant classes: P. Falciparum, P. Malaria, P. Ovale, and P. Vivax. A high potential of generalization with a competitive performance of our framework on inter-class data is demonstrated through an experimental study considering several datasets. Our source code is publicly available on https://github.com/Benhabiles-JUNIA/MalariaNet.

Published
2021
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45. Validating the CorrectWearing of Protection Mask by Taking a Selfie: Design of a Mobile Application 'CheckYourMask' to Limit the Spread of COVID-19

Author

Adnane Cabani, Halim Benhabiles, Karim Hammoudi, Mahmoud Melkemi, Institut de Recherche en Informatique Mathématiques Automatique Signal (IRIMAS), Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA)), Institut de Recherche en Systèmes Electroniques Embarqués (IRSEEM), Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Normandie Université (NU)-École Supérieure d’Ingénieurs en Génie Électrique (ESIGELEC), Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF), Bio-Micro-Electro-Mechanical Systems - IEMN (BIOMEMS - IEMN), and Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)

Subjects
2019-20 coronavirus outbreak, Coronavirus disease 2019 (COVID-19), Exploit, public health system, Computer science, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), coronavirus, 02 engineering and technology, Computer security, computer.software_genre, 03 medical and health sciences, [SPI]Engineering Sciences [physics], Pandemic, 0202 electrical engineering, electronic engineering, information engineering, health education, Face protection masks, mobile health, 030304 developmental biology, 0303 health sciences, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], COVID-19, 020206 networking & telecommunications, public health support, Key features, epidemic prevention and control, 3. Good health, Computer Science Applications, Modeling and Simulation, e-health, [INFO.INFO-ES]Computer Science [cs]/Embedded Systems, Selfie, m-health, computer, Software
Abstract

International audience; In a context of a virus that is transmissive by sputtering, wearing masks appear necessary to protect the wearer and to limit the propagation of the disease. Currently, we are facing the 2019–2020 coronavirus pandemic. Coronavirus disease 2019 (COVID-19) is an infectious disease with first symptoms similar to the flu. The symptom of COVID-19 was reported first in China and very quickly spreads to the rest of the world. The COVID-19 contagiousness is known to be high by comparison with the flu. In this paper, we propose a design of a mobile application for permitting everyone having a smartphone and being able to take a picture to verify that his/her protection mask is correctly positioned on his/her face. Such application can be particularly useful for people using face protection mask for the first time and notably for children and old people. The designed method exploits Haar-like feature descriptors to detect key features of the face and a decision-making algorithm is applied. Experimental results show the potential of this method in the validation of the correct mask wearing. To the best of our knowledge, our work is the only one that currently proposes a mobile application design “CheckYourMask” for validating the correct wearing of protection mask.

Published
2020
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46. Interreg 2 seas Project: Health For Dairy Cows, H4DC

Author

Isabelle Vuylsteke, Maud Roblin, Gary K. Robinson, Martine Dellevoet, Halim Benhabiles, Feryal Windal, Janine Roemen, Christopher J. Warren, Helene Leruste, Evi Canniere, Caroline Deweer, Anne Barbier Bourgeois, Ourida Hammouma, Cláudia A. Ribeiro, Pedro Pinto, and Alexis Vlandas

Subjects
General Materials Science
Abstract

Cryptosporidium spp. are microbial parasites that infect the gastrointestinal tract of humans and many animals, causing cryptosporidiosis, a disease characterised by acute watery diarrhoea. In dairy farms, C. parvum is the most common species in calves, leading to high mortality rate, stunted growth, and consequently high economic losses. Trade between farms and breeding centres is a major risk factor in the spread of such parasites, posing a threat to other farms worldwide as well as to human health. This problem is aggravated by the lack of good breeding practices, efficient detection tools, and lack of effective anti-cryptosporidial drugs. To address cryptosporidiosis in dairy farms, we have established the ‘Health For Dairy Cows (H4DC)’ consortium in order to tackle some of the aforementioned issues. Herein, we will present preliminary data from a 3-step strategy: 1)Dissemination of pilot farms in France, Belgium, The Netherlands and England. This collaboration will serve to test the effect of new husbandry practices in the occurrence of Cryptosporidium, which will aim to decrease Cryptosporidium incidence and ultimately decrease the economic burden of cryptosporidiosis. 2)These pilot farms will later be used as testing-grounds of the low-cost and easy-to-use in-situ C. parvum detection tool that will be developed during this project. 3)Development of a cell-based drug-screening system which will be used to screen various drugs and compounds for anti-Cryptosporidium activity. Finally, data from these findings will be used to establish model and strategies in order to transfer the developed technologies to both farmers and biotech/pharmaceutical companies.

Published
2020
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47. SHREC2020 track: Multi-domain protein shape retrieval challenge

Author

Florent Langenfeld, David Hunter, Matthieu Montes, Karim Hammoudi, Daisuke Kihara, Feryal Windal, Yu-Kun Lai, Ekpo Otu, Paul L. Rosin, Stelios K. Mylonas, Petros Daras, Apostolos Axenopoulos, Halim Benhabiles, Reyer Zwiggelaar, Andrea Giachetti, Charles Christoffer, Adnane Cabani, Tunde Aderinwale, Yuxu Peng, Yonghuai Liu, Mahmoud Melkemi, Genki Terashi, Cardiff Univ, Sch Chem, Cardiff CF10 3XQ, S Glam, Wales, Purdue University [West Lafayette], Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF), Bio-Micro-Electro-Mechanical Systems - IEMN (BIOMEMS - IEMN), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF), Institut de Recherche en Informatique Mathématiques Automatique Signal (IRIMAS), Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA)), Université de Strasbourg (UNISTRA), Institut de Recherche en Systèmes Electroniques Embarqués (IRSEEM), Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Normandie Université (NU)-École Supérieure d’Ingénieurs en Génie Électrique (ESIGELEC), Clinica Oculistica, Università degli Studi di Verona, Aberystwyth Univ, Inst Biol Environm & Rural Sci, Aberystwyth SY23 3EB, Dyfed, Wales, Young teachers growth plan project - Changsha University of Science Technology [2019QJCZ014], ATXN1-MED15 PPI project - GSRT Hellenic Foundation for Research and Innovation, European Research Council Executive Agency [640283], Laboratoire Génomique, bioinformatique et chimie moléculaire (GBCM), Conservatoire National des Arts et Métiers [CNAM] (CNAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM), School of Information Science and Engineering [Changsha], Central South University [Changsha], School of Computer Sciences & Informatics [Cardiff], Cardiff University, Università degli studi di Verona = University of Verona (UNIVR), Centre for Research and Technology Hellas (CERTH), Aberystwyth University, and Edge Hill University

Subjects
Computer science, 3D shape analysis, 02 engineering and technology, Computational biology, Domain (software engineering), 3D shape descriptor, [SPI]Engineering Sciences [physics], Protein structure, Species level, 0202 electrical engineering, electronic engineering, information engineering, Protein structure comparison, Protein shape, business.industry, Specific function, 3D shape matching, General Engineering, 020207 software engineering, Modular design, Computer Graphics and Computer-Aided Design, Human-Computer Interaction, Multi domain, SHREC, 3D shape retrieval, 020201 artificial intelligence & image processing, business, Scope (computer science)
Abstract

[#17491] article suite à une conférence orale: 13th EG Euroworkshop on 3D object retrieval, 3DOR 2020, Graz, Austria, september 4-5, 2020; International audience; Proteins are natural modular objects usually composed of several domains, each domain bearing a specific function that is mediated through its surface, which is accessible to vicinal molecules. This draws attention to an understudied characteristic of protein structures: surface, that is mostly unexploited by protein structure comparison methods. In the present work, we evaluated the performance of six shape comparison methods, among which three are based on machine learning, to distinguish between 588 multi-domain proteins and to recreate the evolutionary relationships at the protein and species levels of the SCOPe database. The six groups that participated in the challenge submitted a total of 15 sets of results. We observed that the performance of all the methods significantly decreases at the species level, suggesting that shape-only protein comparison is challenging for closely related proteins. Even if the dataset is limited in size (only 588 proteins are considered whereas more than 160,000 protein structures are experimentally solved), we think that this work provides useful insights into the current shape comparison methods performance, and highlights possible limitations to large-scale applications due to the computational cost. (C) 2020 The Author(s). Published by Elsevier Ltd.

Published
2020
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48. Image-based Ciphering of Video Streams and Object Recognition for Urban and Vehicular Surveillance Services

Author

Audrey Queudet, Safwan El Assad, Halim Benhabiles, Karim Hammoudi, Feryal Windal, Mohammed Abu Taha, Mahmoud Melkemi, Institut de Recherche en Informatique Mathématiques Automatique Signal (IRIMAS), Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA)), Palestine Polytechnic University (PPU), Institut Supérieur de l'Electronique et du Numérique - Lille (ISEN-Lille), Institut supérieur de l'électronique et du numérique (ISEN), Institut d'Électronique et des Technologies du numéRique (IETR), Université de Nantes (UN)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Laboratoire des Sciences du Numérique de Nantes (LS2N), Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Institut supérieur de l'électronique et du numérique (ISEN)-Université catholique de Lille (UCL), Université de Nantes (UN)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), and Nantes Université (NU)-Université de Rennes 1 (UR1)

Subjects
[SPI.OTHER]Engineering Sciences [physics]/Other, Surveillance Systems, Computer science, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Image Ci- phering, 050801 communication & media studies, 02 engineering and technology, Image Analysis, Image (mathematics), Remote computing, [SPI]Engineering Sciences [physics], 0508 media and communications, Image transfer, Server, 0202 electrical engineering, electronic engineering, information engineering, Computer vision, Pseudorandom number generator, Object Recognition, business.industry, 05 social sciences, Cognitive neuroscience of visual object recognition, Machine Learning, [SPI.TRON]Engineering Sciences [physics]/Electronics, Real-time Video Services, 020201 artificial intelligence & image processing, Artificial intelligence, business, Image based
Abstract

International audience; Nowadays, urban and vehicular surveillance systems are col-lecting large amounts of image data for feeding recognition systems e.g.towards proposing localization or navigation services. In many cases,these image data cannot directly be processed in situ by the acquisitionsystems in reason of their low computational capabilities. The acquiredimages are transferred to remote computing servers through various com-puter networks, then analyzed in details towards object recognition. Theobjective of this paper is twofold i) presenting image-based cypheringmethods that can eciently be applied for securing the image transferagainst consequences of image interceptions (e.g.; man-in-the-middle at-tacks) ii) presenting generic image-based analysis techniques that can beexploited for object recognition. Experimental results show end-to-endimage-based solutions for fostering developments of surveillance systemsand services in urban and vehicular environments.

Published
2019

49. Developing A Vision-based Adaptive Parking Space Management System

Author

Abhishek Jandial, Joseph Mouzna, Karim Hammoudi, Fadi Dornaika, Halim Benhabiles, Laboratoire de Mathématiques Informatique et Applications (LMIA), and Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))

Subjects
Control and Optimization, Parking guidance and information, Vision based, Vision, Computer Networks and Communications, Computer science, 020208 electrical & electronic engineering, Adaptive Parking Space, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 020206 networking & telecommunications, 02 engineering and technology, GeneralLiterature_MISCELLANEOUS, Computer Science Applications, Transport engineering, Human–computer interaction, 11. Sustainability, Management system, 0202 electrical engineering, electronic engineering, information engineering, Parking space, Electrical and Electronic Engineering, Management System
Abstract

International audience; This paper presents a vision-based monitoring system for developing low-cost and contactless Parking Management Services (PMS). Additionally, this paper describes a flexible and adaptive parking space monitoring system. More precisely, this system can be exploited for detecting parking space occupancies. Experimental results have been conducted by using a webcam that is connected to a conventional micro-computer in order to detect available car parking slots. The system can be installed either at the top of a street pole or a building and can be oriented in direction of a targeted car parking.

Published
2016
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50. A Transfer Learning Exploited for Indexing Protein Structures from 3D Point Clouds

Author

Feryal Windal, Adnane Cabani, Mahmoud Melkemi, Halim Benhabiles, and Karim Hammoudi

Subjects
0301 basic medicine, 03 medical and health sciences, 030104 developmental biology, Protein structure, Computer science, Search engine indexing, Protein structure analysis, Protein Data Bank (RCSB PDB), Point cloud, Data mining, Transfer of learning, computer.software_genre, computer
Abstract

In this paper, we propose a transfer learning-based methodology that can be exploited for indexing protein structures from associated 3D point clouds. Such a methodology can be particularly useful for biologists that are searching automated solutions to find family members of a query protein or even to label new structures by directly using input raw 3D point clouds. Comparative study and performance evaluation show the efficiency and the potential of the proposed methodology.

Published
2019
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