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-based cameras have shown great promise in a variety of situations where frame
based cameras suffer, such as high speed motions and high dynamic range scenes …

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

Abstract Spiking Neural Networks (SNNs) have recently emerged as a new generation of
low-power deep neural networks due to sparse, asynchronous, and binary event-driven …

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 …

Event cameras respond to scene dynamics and offer advantages to estimate motion.
Following recent image-based deep-learning achievements, optical flow estimation methods …

Event-based cameras display great potential for a variety of tasks such as high-speed
motion detection and navigation in low-light environments where conventional frame-based …

Neuromorphic vision sensing (NVS) devices represent visual information as sequences of
asynchronous discrete events (aka," spikes'") in response to changes in scene reflectance …

Event cameras are paradigm-shifting novel sensors that report asynchronous, per-pixel
brightness changes called 'events' with unparalleled low latency. This makes them ideal for …

The combination of spiking neural networks and event-based vision sensors holds the
potential of highly efficient and high-bandwidth optical flow estimation. This paper presents …