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Events

First International Workshop on “AI-based All-Weather Surveillance System", AWSS 2024 in conjunction with ACCV 2024, Hanoi, Vietnam, December 2024. (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, October 2024. (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 Signal Processing.

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

Applications Interests: My applications interests concerns mainly visual and acoustic surveillance in challenging surface (ground and sea), air and space environments.  

Keywords : Passive Vision and Acoustic Monitoring for Environmental Preservation.

Recent Publications

J. Castro-Correa, J. Giraldo, A. Mondal, M. Badiey, T. Bouwmans, F. Malliaros, "Time-Varying Signals Recovery Via Graph Neural Networks", IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023, Rhodes Island, Greece, June 2023. 

W. Prummel, J. Giraldo, A. Zakharova, T. Bouwmans, "Inductive Graph Neural Networks for Moving Object Segmentation", IEEE International Conference on Image Processing, ICIP 2023, Kuala Lumpur, Malaysia, October 2023. 

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