Neuromorphic Computing, a concept pioneered in the late 1980s, is receiving a lot of
attention lately due to its promise of reducing the computational energy, latency, as well as …

Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead
of capturing images at a fixed rate, they asynchronously measure per-pixel brightness …

Event cameras are novel sensors that report brightness changes in the form of a stream of
asynchronous “events” instead of intensity frames. They offer significant advantages with …

Spiking neural networks (SNNs) are inspired by information processing in biology, where
sparse and asynchronous binary signals are communicated and processed in a massively …

In this work, we propose a novel framework for unsupervised learning for event cameras that
learns motion information from only the event stream. In particular, we propose an input …

Event cameras are novel sensors that report brightness changes in the form of
asynchronous" events" instead of intensity frames. They have significant advantages over …

We propose to incorporate feature correlation and sequential processing into dense optical
flow estimation from event cameras. Modern frame-based optical flow methods heavily rely …

As a bio-inspired and emerging sensor, an event-based neuromorphic vision sensor has a
different working principle compared to the standard frame-based cameras, which leads to …

Event cameras are vision sensors that record asynchronous streams of per-pixel brightness
changes, referred to as" events". They have appealing advantages over frame based …

The field of neuromorphic computing promises extremely low-power and low-latency
sensing and processing. Challenges in transferring learning algorithms from traditional …