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1. A Featureless Approach to 3D Polyhedral Building Modeling from Aerial Images

Author

Karim Hammoudi and Fadi Dornaika

Subjects
3D building reconstruction, 3D city modeling, 3D polyhedral building model, aerial images, featureless image registration, Differential Evolution algorithm, Chemical technology, TP1-1185
Abstract

This paper presents a model-based approach for reconstructing 3D polyhedral building models from aerial images. The proposed approach exploits some geometric and photometric properties resulting from the perspective projection of planar structures. Data are provided by calibrated aerial images. The novelty of the approach lies in its featurelessness and in its use of direct optimization based on image rawbrightness. The proposed framework avoids feature extraction and matching. The 3D polyhedral model is directly estimated by optimizing an objective function that combines an image-based dissimilarity measure and a gradient score over several aerial images. The optimization process is carried out by the Differential Evolution algorithm. The proposed approach is intended to provide more accurate 3D reconstruction than feature-based approaches. Fast 3D model rectification and updating can take advantage of the proposed method. Several results and evaluations of performance from real and synthetic images show the feasibility and robustness of the proposed approach.

Published
2010
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2. 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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3. A Synergistic Approach for Recovering Occlusion-Free Textured 3D Maps of Urban Facades from Heterogeneous Cartographic Data

Author

Karim Hammoudi, Fadi Dornaika, Bahman Soheilian, Bruno Vallet, John McDonald, and Nicolas Paparoditis

Subjects
Electronics, TK7800-8360, Electronic computers. Computer science, QA75.5-76.95
Abstract

In this paper we present a practical approach for generating an occlusion-free textured 3D map of urban facades by the synergistic use of terrestrial images, 3D point clouds and area-based information. Particularly in dense urban environments, the high presence of urban objects in front of the facades causes significant difficulties for several stages in computational building modeling. Major challenges lie on the one hand in extracting complete 3D facade quadrilateral delimitations and on the other hand in generating occlusion-free facade textures. For these reasons, we describe a straightforward approach for completing and recovering facade geometry and textures by exploiting the data complementarity of terrestrial multi-source imagery and area-based information.

Published
2013
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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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10. 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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38. 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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39. 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. 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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44. 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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45. Towards semantic segmentation of orthophoto images using graph-based community identification

Author

Fadi Dornaika, Abdelmalik Moujahid, Yassine Ruichek, and Karim Hammoudi

Subjects
0209 industrial biotechnology, Covariance matrix, business.industry, Quantization (signal processing), ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Orthophoto, Pattern recognition, 02 engineering and technology, Spectral clustering, Matrix (mathematics), 020901 industrial engineering & automation, Artificial Intelligence, Computer Science::Computer Vision and Pattern Recognition, Histogram, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Segmentation, Artificial intelligence, Laplacian matrix, business, Software, Mathematics
Abstract

We present an unsupervised framework that automatically detects objects of interest in images by formulating the general problem of semantic segmentation as community detection problem in graphs. The framework broadly follows a four-step procedure. First, we perform an over-segmentation of the original image using the well-known statistical region merging algorithm which presents the advantage of not requiring any quantization or colour space transformations. Second, we compute the feature descriptors of the resulting segmented regions. For encoding colour and other textural information, each region is described by an hybrid descriptor based on colour histograms and covariance matrix descriptor. Third, from the set of descriptors we construct different weighted graphs using various graph construction algorithms. Finally, the resulting graphs are then divided into groups or communities using a community detection algorithm based on spectral modularity maximization. This algorithm makes use of the eigenspectrum of matrices such as the graph Laplacian matrix and the modularity matrix which are more likely to reveal the community structure of the graph. Experiments conducted on large orthophotos depicting several zones in the region of Belfort city situated on the north-eastern of France provide promising results. The proposed framework can be used by semi-automatic approaches to handle the challenging problems of scene parsing.

Published
2017
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46. 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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47. 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

48. 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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49. 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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50. A Comparative Study of 2 Resolution-Level LBP Descriptors and Compact Versions for Visual Analysis

Author

Oussama Taoufik, Feryal Windal, Karim Hammoudi, Mahmoud Melkemi, Fadi Dornaika, and Halim Benhabiles

Subjects
Local binary patterns, Computer science, business.industry, Computation, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Pattern recognition, Object (computer science), Image (mathematics), Set (abstract data type), Categorization, Computer Science::Computer Vision and Pattern Recognition, Artificial intelligence, business, Resolution (algebra)
Abstract

We describe computation methods of Local Binary Pattern (LBP) descriptors as well as of 2-Resolution-level LBP. Both classical and compact versions of these descriptors are illustrated and evaluated with image sets of varied natures. Each considered image set is associated to a contextual study case which has an applicative objective for visual analysis and object categorization. A comparative study shows performances and potentialities of each presented method.

Published
2018
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