George Dimas
Papers
7
Total Citations
138
H-Index
6
About
George Dimas is a leading researcher at the intersection of medical robotics, computer vision, and intelligent navigation systems. His primary contributions lie in advancing endoscopic technologies, particularly through the development of novel visual localization and 3D reconstruction methods for wireless capsule endoscopy (WCE). Dimas pioneered the use of color information and artificial neural networks for non-parametric visual odometry in WCE, addressing the critical challenge of reliable lesion localization within the gastrointestinal tract. His work on "Deep Endoscopic Visual Measurements" (44 citations) and "Intelligent visual localization of wireless capsule endoscopes enhanced by color information" (35 citations) has laid the groundwork for more accurate, minimally invasive diagnostic procedures. Beyond endoscopy, Dimas has explored obstacle detection using generative adversarial networks and fuzzy sets for computer-assisted navigation, as well as monocular salient object detection enhanced by predicted depth. His research, which bridges clinical needs with cutting-edge AI and robotic validation, has accumulated over 130 citations, establishing him as a key figure in the evolution of autonomous medical imaging and robotic-assisted diagnostics.
Research Focus
Key Achievements
Top Papers
- 1Deep Endoscopic Visual Measurements44 citations · 2018
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- 5Robotic validation of visual odometry for wireless capsule endoscopy11 citations · 2016
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- 7MonoSOD: Monocular Salient Object Detection based on Predicted Depth3 citations · 2021