Diederik Paul Moeys
Papers
7
Total Citations
163
H-Index
4
About
Diederik Paul Moeys is a neuromorphic engineering researcher whose work sits at the intersection of bio-inspired vision, event-based sensing, and real-time robotics. His research centers on Dynamic Vision Sensors (DVS) and the DAVIS camera platform, which combines conventional frame-based imaging with asynchronous event-driven output to unlock ultra-low-latency visual processing. Moeys has made significant contributions to the field through his development of novel object detection and tracking algorithms that harness the unique strengths of event-based cameras, most notably demonstrated in his widely cited 2016 paper on combined frame- and event-based tracking, which has accumulated 89 citations. His subsequent work on FPGA implementations of event-based filtering and feature extraction, cited 48 times, further advanced the practical deployment of these sensors in power-constrained real-time systems. Moeys has also explored the biological foundations of his engineering work, modeling retinal ganglion cells and translating their computational principles into hardware for robotic navigation. His predator-prey robot chasing experiments, driven by convolutional neural networks trained on DAVIS data, showcase his ability to bridge neuroscience-inspired theory with applied robotics, establishing him as a distinctive voice in the neuromorphic computing community.
Research Focus
Key Achievements
Top Papers
- 1Combined frame- and event-based detection and tracking89 citations · 2016
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