Papers
2
Total Citations
33
H-Index
2
About
Xavier Lagorce is a leading researcher in neuromorphic vision, specializing in event-based cameras and their applications in robotics. His work focuses on developing efficient, asynchronous visual processing algorithms that leverage the unique properties of event-based sensors—high temporal resolution, low power consumption, and high dynamic range. His most impactful contribution is the HATS (Histograms of Averaged Time Surfaces) method, introduced in a 2018 paper with 24 citations, which provides a robust framework for object classification using event data. This technique has become a cornerstone for event-based computer vision. Earlier, in 2013, Lagorce pioneered event-based feature extraction for robotic vision, demonstrating how asynchronous cameras can reduce redundancy and energy consumption while enabling real-time perception. His research bridges the gap between biological inspiration and practical engineering, making him a key figure in the field. Lagorce’s work is essential reading for students and researchers exploring neuromorphic hardware, low-power vision systems, and autonomous robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2Event-based features for robotic vision9 citations · 2013