Bio-inspired Motion Estimation with Event-Driven Sensors


This paper presents a method for image motion estimation for event-based sensors. Accurate and fast image flow estimation still challenges Computer Vision. A new paradigm based on asynchronous event-based data provides an interesting alternative and has shown to provide good estimation at high contrast contours by estimating motion based on very accurate timing. However, these techniques still fail in regions of high-frequency texture. This work presents a simple method for locating those regions, and a novel phase-based method for event sensors that estimates more accurately these regions. Finally, we evaluate and compare our results with other state-of-the-art techniques.

International Work-Conference on Artificial Neural Networks
Francisco Barranco
Francisco Barranco
Associate Professor

Associate Professor at the Department of Computer Engineering, Automation and Robotics, Principal Investigator at the Applied Computational Neuroscience Group and the Computer Vision and Robotics Lab of the University of Granada.