This paper proposes a novel spatio-temporal Vision Transformer model that uses Shifted Patch Tokenization (SPT) and locality Self-Attention (LSA) to enhance the accuracy of Action Unit classification from event streams.
Eoptic, Inc., a leader in advanced imaging and optics systems integration, and Prophesee, the global pioneer in neuromorphic vision systems, today announced a strategic collaboration to integrate high-speed event detection into Eoptic’s innovative and flexible prismatic sensor module. By combining Eoptic’s Cambrian Edge imaging platform with Prophesee’s cutting-edge, event-based Metavision® sensors, the partnership aims to tackle real-time imaging challenges and open new frontiers in dynamic visual processing.
This paper explores the use of event cameras as an add-on to traditional MOKE microscopy to enhance time resolution for observing magnetic domains. Event cameras improve temporal resolution to 1 µs, enabling real-time monitoring and post-processing of fast magnetic dynamics. A proof-of-concept feedback control experiment demonstrated a latency of just 25 ms, highlighting the potential for dynamic material research. Limitations of current event cameras in this application are also discussed.
This paper presents a hybrid image- and event-based architecture for detecting dim space objects in geosynchronous orbit using dynamic vision sensing. Combining conventional and point-cloud feature extractors like PointNet, the approach enhances detection performance in high-background activity scenes. An event-based imaging simulator is also developed for model training and sensor parameter optimization, demonstrating improved recall for dim objects in challenging conditions.
This paper introduces a dataset from a subterranean (SubT) environment, captured with state-of-the-art sensors like RGB, RGB-D, event-based, and thermal cameras, along with 2D/3D lidars, IMUs, and UWB positioning systems. Synchronized raw data is provided in ROS message format, enabling evaluations of navigation, localization, and mapping algorithms.