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Events

First International Workshop on “Real-Time Implementation and Lightweight GNNs for Conventional and Event-based Cameras”, RT-GNNs 2025  in conjunction with IEEE ICIP 2025, Anchorage, Alaska, September 2025. (More information) 

First International Workshop on “Graph Learning and Graph Signal Processing Algorithms in Computer Vision", G2SP-CV 2024 in conjunction with ICPR 2024, Kolkata, India, December 2024. (More information)

Motivation Interests

Preservation and Protection of Fauna and Flora. (More information)

Research Interests

My research interests in computer vision consists mainly in the detection of moving objects in challenging surface (ground and sea), air and space environments. Sensors used are either   passive sensors (i.e cameras)  or  active sensors (i.e radars) providing data images. More specifically, passive sensors are cameras in visible spectrum, IR cameras, multi-spectral cameras, hyper-spectral cameras, light-field cameras and event cameras. Active sensors are SAR, PolSAR, and LiDAR.

For detection of moving foreground objects in surface and air environments, my research focus on background subtraction (Background Subtraction Research). In this research, I investigated particularly the application of different mathematical concepts (statistical, fuzzy and Dempster-Schafer theories), machine learning concepts (reconstructive and discriminative subspace learning models, robust PCA and deep neural networks), and signal processing concepts (Graph Signal Processing) in the field of video surveillance.

Statistical, fuzzy and Dempster-Schafer theories allow to deal with imprecision, uncertainty and incompletness in the data images due the challenges (dynamic backgrounds, illumination changes to cite a few).  Representation learning allows to deal with pertubations in the data images. Deep neural networks allow to have scene-specific or agnostic learning of scenes. Graph signal processing allows to reduce the required labeled data compared to deep neural networks.

My work also concerns full exhaustive surveys on mathematical and machine learning tools used in foreground/background separation. Furthermore, I investigated the field of decomposition into low-rank and additives matrices for background/foreground separation (DLAM Research), the field of decomposition into low-rank and additives tensors for background/foreground separation (DLAT research), and the field of decomposition into sparse and additive matrices for background/foreground separation (DSAM research).

My research concerns also robust texture features and feature selection for background/foreground separation (Features Research).

Keywords (Theory): Crisp concepts, Statistical concepts, Fuzzy concepts, Dempster-Schafer Theory, Robust Principal Component Analysis, Deep Neural Networks, Graph Neural Networks, Graph Signal Processing.

Keywords (Computer Vision Applications) : Background Subtraction, Target Detection, Moving Object Detection, LBP features.

Keywords (Sound Applications) : Anomaly Sound Detection, Sound Clustering.

Applications Interests: My applications interests concerns mainly visual and acoustic monitoring surveillance in challenging surfaces (ground and sea), air and space environments for environmental preservation.  In this context, acoustic and visual sensors are applied for anomaly sound detection and computer vision, respectively. Traffic forecasting can also optimized the traffic reducing the impact of humans in the nature.

Keywords : Passive Vision Monitoring (PVM), Passive Acoustic Monitoring (PAM), Active Vision Monitoring, Active Acoustic Monitoring, Environmental Preservation.

Recent Publications

M. Kapoor, W. Prummel, J. Giraldo, B. Subudhi, A. Zakharova, T. Bouwmans, A. Bansal, “Graph-based Moving Object Segmentation for Underwater Videos using Semi-supervised Learning”, Computer Vision and Image Understanding, 2025. 

My recent publications are available on Academia, ResearchGate, Researchr, ORCID, Publons, Scopus, GoogleScholar, and PublicationList.