Abstract:

Event cameras are novel, bio-inspired visual sensors, whose pixels output asynchronous and independent timestamped spikes at local intensity changes, called ‘events’. Event cameras offer advantages over conventional frame-based cameras in terms of latency, high dynamic range (HDR) and temporal resolution. Event cameras do not output conventional image frames, thus, image reconstruction from events enables visualisation and frame-based processing. Convolution is a fundamental tool for computer vision, however, its application to event cameras is unclear. We show how to compute convolution event-by-event. Until recently, event cameras have been limited to grayscale intensity. Now, a new color event camera is available and we unveil the color information contained in events by reconstructing color images, and release the Color Event Camera Dataset.

Speaker Bio:

Cedric completed his Master of Engineering (Mechanical) at the University of Melbourne in 2016. In 2015 he worked as a research assistant in the Fluid Dynamics lab at Melbourne before completing an exchange semester at ETH Zurich. In 2017 Cedric commenced his PhD in Robotic Vision under the supervision of Rob Mahony at the ANU. His PhD topic is the development of novel optical flow algorithms capable of running in real-time for high-speed robotics applications. Cedric is currently pursuing this research using event cameras, which are bio-inspired vision sensors with microsecond temporal resolution.