Moncef Gabbouj
Papers
10
Total Citations
681
H-Index
7
About
Moncef Gabbouj is a prominent researcher whose work spans robotics, computer vision, deep learning, and autonomous systems, with particular emphasis on real-world applications that push the boundaries of intelligent machines. He is perhaps best known for his influential contributions to multi-robot search and rescue systems, where his 2020 paper on collaborative multi-robot coordination, perception, and active vision has garnered an impressive 485 citations, establishing him as a leading voice in autonomous rescue robotics. His research extends into urban 3D scene understanding, including semantic segmentation of street-level imagery and LiDAR data for autonomous vehicles and drones. Gabbouj has also advanced the field of camera calibration through deep learning approaches, including novel methods for predicting distortion parameters from single images using synthetic training data. His development of the OpenDR toolkit reflects a commitment to making high-performance, resource-efficient deep learning accessible for robotics practitioners. Additional contributions include water segmentation for unmanned surface vehicles, speech command recognition in constrained environments, and automated biological image analysis. Across these diverse domains, Gabbouj's work consistently bridges foundational research and practical deployment, making him a significant figure in applied artificial intelligence and autonomous systems research.
Research Focus
Key Achievements
Top Papers
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- 5Deep Learning for Camera Calibration and Beyond: A Survey26 citations · 2023
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