This paper describes novel event-based spatio-temporal features called time-surfaces and
how they can be used to create a hierarchical event-based pattern recognition architecture …

Neuromorphic vision research requires high-quality and appropriately challenging event-
stream datasets to support continuous improvement of algorithms and methods. However …

The nervous systems converts the physical quantities sensed by its primary receptors into
trains of events that are then processed in the brain. The unmatched efficiency in information …

Traditional frame-based cameras, despite their effectiveness and usage in computer vision,
exhibit limitations such as high latency, low dynamic range, high power consumption, and …

This paper introduces a spiking hierarchical model for object recognition which utilizes the
precise timing information inherently present in the output of biologically inspired …

Dynamic Vision Sensors (DVS) have recently appeared as a new paradigm for vision
sensing and processing. They feature unique characteristics such as contrast coding under …

This paper introduces an event-driven feedforward categorization system, which takes data
from a temporal contrast address event representation (AER) sensor. The proposed system …

The recently developed event cameras can directly sense the motion in the scene by
generating an asynchronous sequence of events, ie, event streams, where each individual …

This article proposes an unsupervised address event representation (AER) object
recognition approach. The proposed approach consists of a novel multiscale spatio …

In this article, we present a systematic computational model to explore brain-based
computation for object recognition. The model extracts temporal features embedded in …