This paper proposes to use event cameras with bio-inspired silicon sensors, which are sensitive to radiance changes, to recover precise radiance values. It reveals that, under active lighting conditions, the transient frequency of event signals triggering linearly reflects the radiance value.
This paper aims to increase the performance of spiking neural networks for event data processing, in order to design intelligent automotive algorithms that are efficient, fast, and energy-efficient.
This research introduces a novel approach to Stereo Hybrid Event-Frame Disparity Estimation, leveraging the unique strengths of both event and frame-based cameras. By combining these modalities, significant improvements in depth estimation accuracy, enabling more robust and reliable 3D perception systems was achieved.
In this paper the local histograms are normalised to produce probability distributions. Once these distributions are obtained, the optical flow is estimated using powerful methods taken from probability theory, in particular, methods based on the Fisher–Rao metric.
This paper presents a real-time method to detect and track multiple mobile ground robots using event cameras. The method uses density-based spatial clustering of applications with noise (DBSCAN) to detect the robots and a single k-dimensional (k − d) tree to accurately keep track of them as they move in an indoor arena.