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 …

A critical challenge in neuromorphic computing is to present computationally efficient
algorithms of learning. When implementing gradient-based learning, error information must …

Edge intelligence capability is expected to enable the development of a new paradigm
integrated with edge computing and artificial intelligence. However, due to the multisource …

DAVIS camera, streaming two complementary sensing modalities of asynchronous events
and frames, has gradually been used to address major object detection challenges (eg, fast …

In frame-based vision, object detection faces substantial performance degradation under
challenging conditions due to the limited sensing capability of conventional cameras. Event …

Large-scale public datasets are vital for algorithm development in the computer vision field.
Thanks to the availability of advanced sensors such as cameras, Lidar and Kinect, massive …

Grasp pose estimation is a crucial procedure in robotic manipulation. Most of the current
robot grasp manipulation systems are built on frame-based cameras like RGB-D cameras …

In the field of autonomous vehicles, various heterogeneous sensors, such as LiDAR, Radar,
camera, etc, are combined to improve the vehicle ability of sensing accuracy and …

Neuromorphic vision sensors such as the Dynamic and Active-pixel Vision Sensor (DAVIS)
using silicon retina are inspired by biological vision, they generate streams of asynchronous …

For vehicles to navigate autonomously, they need to perceive and understand their
immediate surroundings. Currently, cameras are the preferred sensors, due to their high …