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Yannick Berthoumieu is a Professor at the Université de Bordeaux , affiliated with the IMS Bordeaux laboratory. He leads the MOTIVE team under the Signal and Image Processing research group. His work spans Signal and Image Processing , Machine Learning , and Remote Sensing , with a focus on SAR image analysis, generative models, and geometric learning.
دانشیار
Romain Raveaux is an Associate Professor at the LIFAT Computer Science Laboratory, University of Tours, affiliated with Polytech Tours. His research focuses on Image Analysis, Machine Learning, Structural Pattern Recognition, Graph Matching, Graph Neural Networks, Discrete Optimization, Reinforcement Learning, and Transfer Learning . Email: romain.raveaux@gmail.com , romain.raveaux@laposte.net Address: 64 av. Jean Portalis, Tours, France, 37200 Phone: +33 (0)2 47 36 14 27 Research Interests Graph Matching and Neural Networks Discrete Optimization for Pattern Recognition Transfer Learning in Graph-Based Models Historical Document Analysis Scientific Trends His recent work bridges Graph Neural Networks with Mixed-Integer Programming , focusing on Image Semantic Segmentation and Graph Cycle Detection . Earlier studies emphasize Genetic Algorithms for graph classification and Graph Edit Distance optimization in pattern recognition.
Mohamed Najim is a Professor at IMS Bordeaux (Laboratoire de l'intégration, du matériau au système) affiliated with the University of Bordeaux. He is a member of the Signal and Image Processing research group within the MOTIVE team, where he conducts cutting-edge research in multidimensional signal processing and image analysis. His work spans theoretical developments in signal modeling and practical applications in speech enhancement, image colorization, and communication systems. Professor Najim's research interests focus on advanced signal processing techniques, with particular expertise in autoregressive modeling, Kalman filtering, generative adversarial networks, and multidimensional system analysis. His work bridges theoretical signal processing with practical applications in image processing, speech enhancement, and wireless communications. He has made significant contributions to the development of novel algorithms for texture analysis, channel modeling, and noise reduction in various signal processing contexts. The analysis of his publication record spanning over 25 years reveals a consistent research trajectory focused on fundamental signal processing techniques with expanding applications into modern deep learning approaches. His recent work demonstrates a clear progression from traditional signal processing methods toward integrating machine learning techniques, particularly evident in his 2023 SPDGAN paper which combines manifold learning with generative adversarial networks for image colorization. Throughout his career, Professor Najim has maintained strong theoretical foundations while adapting to emerging technologies in the field. Mohamed Najim has supervised numerous research projects and collaborated extensively with colleagues across institutions. His work shows consistent funding support through participation in various research programs focused on signal processing applications. He has maintained active research collaborations with institutions including CNRS and HESAM University. Professor Najim conducts his research within the IMS laboratory, a leading research center for integration from materials to systems. The laboratory provides state-of-the-art facilities for signal processing research, including specialized computing resources for image processing and speech analysis. His work within the MOTIVE team focuses on developing innovative approaches to complex signal processing challenges across multiple application domains.
Ludovic MACAIRE serves as Professor and Team Leader of the Color Imaging group at University of Lille, based in Room S1.45 at ESPRIT Scientific City with contact number 03 20 33 40 45. His research focuses on: Automatic image analysis methods for color/multispectral information in natural scenes Texture descriptor extraction from raw sensor images Color texture feature design and semi-supervised feature space selection Object recognition under uncontrolled conditions via visible/NIR reflectance analysis Sub-pixel scale motion estimation of vibrating objects Tensor modeling of multichannel images (future work) Primary application domains include precision agriculture (weed detection) and structural integrity monitoring through modal analysis. Professor MACAIRE has supervised doctoral research on critical topics: Edge detection in CFA images (Aberkane, 2017) Modal characterization of mechanical structures via video motion analysis (Marinel, 2023) Dimensional measurement of metallic objects (Fasogbon, 2016) Multispectral texture attributes for precision agriculture (Amziane, 2022) Finite element modeling for high-power machine diagnostics (Bacchus, 2016) Demosaicing and classification of multispectral images (Mihoubi, 2018) Multispectral video analysis for deformable texture recognition (Zotto) He leads the Color Imaging team within CRIStAL laboratory, driving innovation in computer vision methodologies and real-world system implementations.
دانشیار
Alice POREBSKI is an active Associate Professor (Maître de Conférences) at Université du Littoral Côte d'Opale, where she is affiliated with LISIC (Laboratoire d'Informatique Signal et Image de la Côte d'Opale). Her research focuses on computer vision and image processing with particular emphasis on texture analysis and classification techniques across color and hyperspectral imaging domains. Dr. POREBSKI's research interests center on feature selection methodologies for texture classification, with extensive work on Local Binary Patterns (LBP), histogram analysis, and multi-color space approaches. Her recent work has evolved toward environmental applications, particularly the detection and classification of marine plastic debris using hyperspectral imaging systems mounted on aquatic drones. She has developed specialized CNN architectures optimized for spectral-spatial feature extraction in environmental monitoring contexts. Her research bridges fundamental computer vision techniques with practical environmental applications, demonstrating how texture analysis methods can address real-world pollution challenges. Her publication record shows a clear progression from foundational texture analysis techniques to applied environmental monitoring systems. Early work focused on optimizing LBP parameters and multi-color space analysis for standard texture classification tasks. More recently, her research has centered on hyperspectral imaging for marine pollution detection, with significant contributions to benchmark datasets and compact feature representation methods specifically designed for drone-based remote sensing applications. This evolution demonstrates both technical depth in image processing and strategic application to pressing environmental issues. Dr. POREBSKI has made substantial contributions to the development of feature selection methodologies that enable efficient texture classification while reducing computational complexity. Her work on clustering-based sequential selection approaches and novel LBP variants has advanced the field of texture analysis, particularly for high-dimensional data like hyperspectral imagery. Through collaborations with researchers including Nicolas Vandenbroucke and Adam El Bergui, she has developed complete systems from theoretical foundations to practical implementations for environmental monitoring.
Nicolas Vandenbroucke is a researcher affiliated with the Université du Littoral Côte d'Opale. His work focuses on color and hyperspectral imaging, texture classification, and biomedical applications. Research Interests Color image representation and segmentation Hyperspectral texture descriptors for pattern recognition Dimensionality reduction via feature selection Biomedical imaging for multiple sclerosis diagnosis Machine vision applications in environmental monitoring Selected Publications Recent work on lightweight CNN models for marine plastic debris classification (2025) Hyperspectral imaging systems for aquatic drone-based waste detection (2024) Advancements in Local Binary Pattern algorithms for texture analysis (2023) Comparative studies of color vs. hyperspectral imaging (2022) Clustering-based feature selection for high-dimensional data (2021) Technical Contributions Development of remote hyperspectral imaging systems Design of compact descriptors for color texture classification Optimized hierarchical warping for medical diagnostics LBP histogram selection scores for supervised learning
Christian Germain is a Professor of Computer Science at Bordeaux Sciences Agro, an engineering school specializing in agronomy. He focuses on information technologies and their applications to agriculture and environmental science, conducting research in image analysis at the IMS laboratory. His work spans remote sensing, embedded agricultural imaging, and digital tool development for vineyards. Key Roles: Co-holder of the AgroTIC business chair (29 corporate sponsors), Scientific Director of DigiLab (open platform for wine-growing experiments). Research Themes: Remote sensing, agricultural imaging systems, covariance pooling in machine learning, and texture analysis for material science. His recent publications highlight collaborations with industry and academic partners, emphasizing applications in vineyard health monitoring, carbon composite modeling, and vine disease detection. Germain’s team utilizes CNNs, Gaussian mixture models, and SAR imaging techniques to advance agricultural and materials engineering. He has contributed to international conferences and journals, integrating computational methods with real-world agricultural challenges, including proximal sensing for crop management and 3D microstructure simulation.
Mohamed Alimoussa is a Postdoctoral Researcher at the National Institute of Applied Sciences of Toulouse since June 2025, working in the MICS (Metrology, Identification, Control and Surveillance) group at Espace Clément Ader. His research focuses on multi-instrumentation methods for drone pose estimation to characterize deformations of kite-sails in maritime propulsion systems using sensor fusion and computer vision. He holds a PhD from the University of the Littoral Opal Coast (2020-2024) where he developed compact hybrid descriptors for texture classification in color and hyperspectral imaging. His educational background includes advanced work in feature selection, dimensionality reduction, and GPU-accelerated image processing algorithms. Dr. Alimoussa's research spans drone navigation, sensor fusion (visual odometry, RTK GPS, laser rangefinders), SLAM algorithms, and Digital Image Correlation for mechanical deformation measurement. His work bridges computer vision with mechanical engineering, emphasizing robust real-world applications in non-structured outdoor environments and industrial metrology. His publication record shows consistent innovation in texture analysis and feature engineering, evolving from foundational work on color texture descriptors to current applications in drone-based metrology. Recent publications demonstrate increasing focus on multi-sensor fusion systems and robustness against environmental variables like lighting changes and rapid motion. He actively co-supervises Master's students and interns in texture classification projects while participating in the ANR-funded ESKIF project (JCJC 2024). His experimental work involves collaborations with LMGC (University of Montpellier) and Beyond the Sea for coastal validation trials. As a core member of the MICS research group at Espace Clément Ader, he contributes to metrology systems development and participates in workshops on drone applications for mechanical measurement, maintaining strong industry-academia partnerships for experimental validation.
