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 …

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

Event-based cameras bring a unique capability to tracking, being able to function in
challenging real-world conditions as a direct result of their high temporal resolution and high …

The best performing learning algorithms devised for event cameras work by first converting
events into dense representations that are then processed using standard CNNs. However …

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 …

Event cameras encode visual information with high temporal precision, low data-rate, and
high-dynamic range. Thanks to these characteristics, event cameras are particularly suited …

Spiking neural networks (SNNs) that enables energy efficient implementation on emerging
neuromorphic hardware are gaining more attention. Yet now, SNNs have not shown …

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 …

In recent years, deep learning has revolutionized the field of machine learning, for computer
vision in particular. In this approach, a deep (multilayer) artificial neural network (ANN) is …

Event cameras are bio-inspired vision sensors that naturally capture the dynamics of a
scene, filtering out redundant information. This paper presents a deep neural network …