George Bebis
Papers
12
Total Citations
453
H-Index
10
About
George Bebis is a leading figure in visual computing and intelligent robotics, whose research bridges computer vision, machine learning, and autonomous systems. His most influential work centers on intent recognition in human-robot interaction, where he pioneered the use of Hidden Markov Models to enable autonomous mobile robots to understand human intentions—a capability essential for collaborative and safety-critical scenarios. This foundational contribution, detailed in his 2008 paper with 176 citations, has shaped subsequent research in cognitive robotics. Bebis has also made significant advances in 3D perception, as evidenced by his comprehensive 2024 survey on deep learning for point cloud classification and semantic segmentation (71 citations), and in robust horizon line detection for robot localization, developing innovative edge-less approaches that overcome traditional instability. As the founding editor of the *Advances in Visual Computing* symposium series, he has fostered a vibrant interdisciplinary community. With multiple highly-cited works spanning intent architectures, visual geo-localization, and hierarchical robot control, Bebis’s research continues to drive progress toward more perceptive, autonomous, and collaborative machines.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3Advances in Visual Computing41 citations · 2016
- 4An Edge-Less Approach to Horizon Line Detection32 citations · 2015
- 5
- 6Advances in Visual Computing27 citations · 2016
- 7A Vision-Based Architecture for Intent Recognition20 citations · 2007
- 8Advances in Visual Computing17 citations · 2009
- 9Fusion of edge-less and edge-based approaches for horizon line detection13 citations · 2015
- 10A compact task representation for hierarchical robot control11 citations · 2016