This work aims at re-exposing the captured photo in the post-processing, providing a more flexible way to address issues within a unified framework. Specifically, it propose a neural network based image re-exposure framework.
The term “neuromorphic” refers to systems that closely resemble the architecture and dynamics of biological neural networks. From brain-inspired computer chips to sensory devices mimicking human vision and olfaction, neuromorphic computing aims to achieve efficiency levels comparable to biological organisms.
This paper proposes a simple but effective event-based pose estimation system using active LED markers (ALM) for fast and accurate pose estimation. The proposed algorithm is able to operate in real time with a latency below 0.5 ms while maintaining output rates of 3 kHz.
This paper proposes to develop a Hardware-in-the-Loop imaging setup that enables experimenting with an event-based and frame-based camera under simulated space conditions. The generated data sets were used to compare visual navigation algorithms in terms of an event-based and frame-based feature detection and tracking algorithm.
This paper explores the use of event cameras for collision detection in unmanned aerial vehicles (UAVs). Traditional cameras have been widely used in UAVs for obstacle avoidance and navigation, but they suffer from high latency and low dynamic range. Event cameras, on the other hand, capture only the changes in the scene and can operate at high speeds with low latency.