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42 results on '"Karim Hammoudi"'

1. Personalized Shares in Visual Cryptography

Author

Karim Hammoudi and Mahmoud Melkemi

Subjects
visual cryptography, visual representation, visual communication, visual effect, graphic design, pictogram, pixel art, negative space, texture mapping, visualization, Photography, TR1-1050, Computer applications to medicine. Medical informatics, R858-859.7, Electronic computers. Computer science, QA75.5-76.95
Abstract

This article deals with visual cryptography. It consists of hiding a message in two key images (also called shares). The decryption of the message is obtained through human vision by superposition of the shares. In existing methods, the surface of key images is not visually pleasant and is not exploited for communicating textual or pictorial information. Presently, we propose a pictogram-based visual cryptography technique, which generates shares textured with customizable and aesthetic rendering. Moreover, robustness characteristics of this technique to the automated decoding of the secret message are presented. Experimental results show concrete personalized shares and their applicative potentials for security and creative domains.

Published
2018
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4. 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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8. 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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11. 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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14. 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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28. 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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30. 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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31. 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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32. 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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33. 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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34. 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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35. 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

36. 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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37. Computing Multi-purpose Image-Based Descriptors for Object Detection: Powerfulness of LBP and Its Variants

Author

Karim Hammoudi, Tran Duy Anh Phan, Mahmoud Melkemi, Fadi Dornaika, and Oussama Taoufik

Subjects
Local binary patterns, business.industry, Computer science, 05 social sciences, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 020206 networking & telecommunications, Pattern recognition, Context (language use), Hamming distance, 02 engineering and technology, Image properties, Object detection, Image (mathematics), 0202 electrical engineering, electronic engineering, information engineering, 0501 psychology and cognitive sciences, Artificial intelligence, business, Image based, 050104 developmental & child psychology
Abstract

In this paper, we present some powerful methods for computing multi-purpose image-based descriptors toward their exploitations in object detection and recognition applications. Image-based descriptors characterize image properties for constituting machine learning-based recognition systems. In this context, we present the principle for computing image-based descriptors using the local binary pattern (LBP) method. Such a method is multi-purpose in the sense that it can be efficiently exploited for the automated recognition of objects of varied natures (e.g., vehicles of traffic-monitoring images, cells of medical images). Then, we propose three variants of LBP, named Mean-LBP, \(\lambda \)-2RLBP, and C2R-LBP. The two latter ones use Hamming distance. Experimental results show that our method can overpass performances of discussed LBP methods notably under realistic conditions of use.

Published
2018
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38. Towards a Model of Car Parking Assistance System Using Camera Networks: Slot Analysis and Communication Management

Author

Mahmoud Melkemi, Adnane Cabani, Karim Hammoudi, Feryal Windal, Halim Benhabiles, Laboratoire de Mathématiques Informatique et Applications (LMIA), Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA)), Pôle Instrumentation, Informatique et Systèmes, 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)-Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Normandie Université (NU)-École Supérieure d’Ingénieurs en Génie Électrique (ESIGELEC), Laboratoire d'Informatique Fondamentale de Lille (LIFL), Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS), Institut Supérieur de l'Electronique et du Numérique - Lille (ISEN-Lille), Institut supérieur de l'électronique et du numérique (ISEN), Laboratoire de Mathématiques Informatique et Applications [UHA] (LMIA), and Institut supérieur de l'électronique et du numérique (ISEN)-Université catholique de Lille (UCL)

Subjects
Parking lot monitoring, Computer science, Hand crafted image features, Real-time computing, 020207 software engineering, Vehicle Routing, 02 engineering and technology, Communications management, Camera network, Order (business), LBP, 11. Sustainability, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Optimal path finding, [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], Car parking
Abstract

International audience; Nowadays, finding an available parking slot in urban environment is become more and more fastidious. In this paper, we present a model of car parking assistance system by particularly using camera networks. Such a system notably includes two research-related milestones, namely the visual detection and the communication of parking information. Hence, this paper presents a computer vision workflow permitting to detect available parking slots. Then, a strategy is proposed in order to optimally communicate to the drivers the locations of the detected available parking slots. Finally, experimental results and evaluations show the feasibility and the potential of the proposed model.

Published
2018
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39. Analyzing and managing the slot occupancy of car parking by exploiting vision-based urban surveillance networks

Author

Mahmoud Melkemi, Joel J. P. C. Rodrigues, Halim Benhabiles, Sofiane Hamrioui, Karim Hammoudi, Fadi Dornaika, Méthodes d'Analyses pour le Traitement d'Images et la Stéréorestitution (MATIS), Laboratoire des Sciences et Technologies de l'Information Géographique (LaSTIG), École nationale des sciences géographiques (ENSG), Institut National de l'Information Géographique et Forestière [IGN] (IGN)-Institut National de l'Information Géographique et Forestière [IGN] (IGN)-École nationale des sciences géographiques (ENSG), Institut National de l'Information Géographique et Forestière [IGN] (IGN)-Institut National de l'Information Géographique et Forestière [IGN] (IGN), Institut d'Informatique et de Mathématiques Appliquées de Grenoble (IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Informatique Fondamentale de Lille (LIFL), Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS), Modeling, localization, recognition and interpretation in computer vision (MOVI), Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble (GRAVIR - IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Grenoble (INPG)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Grenoble (INPG)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA)), Simula Research Laboratory [Lysaker] (SRL), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, Méthodes d'Analyses pour le Traitement d'Images et la Stéréorestitution ( MATIS ), Institut National de l'Information Géographique et Forestière [IGN] ( IGN ), Institut d'Informatique et de Mathématiques Appliquées de Grenoble ( IMAG ), Université Joseph Fourier - Grenoble 1 ( UJF ) -Institut National Polytechnique de Grenoble ( INPG ) -Centre National de la Recherche Scientifique ( CNRS ), Laboratoire d'Informatique Fondamentale de Lille ( LIFL ), Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique ( Inria ) -Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique ( CNRS ), Modeling, localization, recognition and interpretation in computer vision ( MOVI ), Graphisme, Vision et Robotique ( GRAVIR - IMAG ), Université Joseph Fourier - Grenoble 1 ( UJF ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National Polytechnique de Grenoble ( INPG ) -Centre National de la Recherche Scientifique ( CNRS ) -Université Joseph Fourier - Grenoble 1 ( UJF ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National Polytechnique de Grenoble ( INPG ) -Centre National de la Recherche Scientifique ( CNRS ) -Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique ( Inria ) -Centre National de la Recherche Scientifique ( CNRS ), Université de Haute-Alsace (UHA) Mulhouse - Colmar ( Université de Haute-Alsace (UHA) ), and Simula Research Laboratory [Lysaker] ( SRL )

Subjects
Engineering, [ INFO ] Computer Science [cs], Occupancy, Exploit, Parking guidance and information, business.industry, 020206 networking & telecommunications, Advanced driver assistance systems, 02 engineering and technology, Computer security, computer.software_genre, Transport engineering, Broadcasting (networking), Work (electrical), Robustness (computer science), 0202 electrical engineering, electronic engineering, information engineering, Wireless, 020201 artificial intelligence & image processing, [INFO]Computer Science [cs], business, computer, ComputingMilieux_MISCELLANEOUS
Abstract

In this paper, we present an approach for the analysis of parking slots by exploiting urban surveillance networks. This work has been developed in a project which aims to facilitate to the car drivers the access to available car parking slots. This paper describes a framework that exploits video streams provided by urban networks of cameras. The objective is to remotely determine the occupancy status of parking slots. Besides, the paper presents a case study and a discussion about a broadcasting strategy that could be used to share the positions of identified vacant parking slots with drivers searching an available parking slot.

Published
2017
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40. Self-driven and direct spatio-temporal mechanisms for the vision-based parking slot surveillance

Author

Abhishek Jandial, Karim Hammoudi, Fadi Dornaika, Joseph Mouzna, and Halim Benhabiles

Subjects
Brightness, Geography, Parking guidance and information, Vision based, Robustness (computer science), Real-time computing, Novelty, Estimator, Operational capabilities, Self driven, Simulation
Abstract

A vision-based and straightforward parking space surveillance system is presented. In particular, the novelty of the approach lies in the use of self-driven and direct dissimilarity computing mechanisms based on image raw brightness. Notably, the parking occupancy is estimated in real-time by applying fast computed estimators that optimize a global dissimilarity measure over slot-centered parking images. Experimental results show the operational capabilities and the efficiency of the proposed system mechanisms.

Published
2016
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41. Design, Implementation and Simulation of an Experimental Processing Architecture for Enhancing Real-time Video Services by Combining VANET, Cloud Computing System and Onboard Navigation System

Author

Fadi Dornaika, Mohamed Kasraoui, Q. Cai, Karthik Bandi, Karim Hammoudi, Karan Radhakrishnan, S. Liu, Nabil Ajam, Méthodes d'Analyses pour le Traitement d'Images et la Stéréorestitution (MATIS), Laboratoire des Sciences et Technologies de l'Information Géographique (LaSTIG), École nationale des sciences géographiques (ENSG), Institut National de l'Information Géographique et Forestière [IGN] (IGN)-Institut National de l'Information Géographique et Forestière [IGN] (IGN)-École nationale des sciences géographiques (ENSG), Institut National de l'Information Géographique et Forestière [IGN] (IGN)-Institut National de l'Information Géographique et Forestière [IGN] (IGN), Pôle Instrumentation, Informatique et Systèmes, 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)-Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Normandie Université (NU)-École Supérieure d’Ingénieurs en Génie Électrique (ESIGELEC), and Universidad del Pais Vasco / Euskal Herriko Unibertsitatea [Espagne] (UPV/EHU)

Subjects
Service (systems architecture), Vehicular ad hoc network, Computer science, business.industry, Real-time computing, Navigation system, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 020206 networking & telecommunications, Cloud computing, 02 engineering and technology, Track (rail transport), [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], Work (electrical), 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Architecture, business, Wireless sensor network, ComputingMilieux_MISCELLANEOUS
Abstract

In this paper, we propose a design for novel and experimental cloud computing systems. The proposed system aims at enhancing computational, communicational and annalistic capabilities of road navigation services by merging several independent technologies, namely vision-based embedded navigation systems, prominent Cloud Computing Systems (CCSs) and Vehicular Ad-hoc NETwork (VANET). This work presents our initial investigations by describing the design of a global generic system. The designed system has been experimented with various scenarios of video-based road services. Moreover, the associated architecture has been implemented on a small-scale simulator of an in-vehicle embedded system. The implemented architecture has been experimented in the case of a simulated road service to aid the police agency. The goal of this service is to recognize and track searched individuals and vehicles in a real-time monitoring system remotely connected to moving cars. The presented work demonstrates the potential of our system for efficiently enhancing and diversifying real-time video services in road environments.

Published
2015
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