Event cameras are novel bio-inspired sensors, which asynchronously capture pixel-level
intensity changes in the form of" events". Due to their sensing mechanism, event cameras …

With the establishment of Industry 4.0, machines are now required to interact with workers.
By observing biometrics they can assess if humans are authorized, or mentally and …

Motion deblurring from a blurred image is a challenging computer vision problem because
frame-based cameras lose information during the blurring process. Several attempts have …

With event-driven algorithms, especially spiking neural networks (SNNs), achieving
continuous improvement in neuromorphic vision processing, a more challenging event …

The main streams of human activity recognition (HAR) algorithms are developed based on
RGB cameras which usually suffer from illumination, fast motion, privacy preservation, and …

Recent advances in bio-inspired vision with event cameras and associated spiking neural
networks (SNNs) have provided promising solutions for low-power consumption …

Multi-modal learning has gained significant attention due to its ability to enhance machine
learning algorithms. However, it brings challenges related to modality heterogeneity and …

Human Action Recognition (HAR) stands as a pivotal research domain in both computer
vision and artificial intelligence, with RGB cameras dominating as the preferred tool for …

Event cameras are novel bio-inspired sensors, which asynchronously capture pixel-level
intensity changes in the form of “events”. The innovative way they acquire data presents …

With the growing popularity of wearable cameras, egocentric vision has become an
increasingly researched area. This perspective offers a direct view from the wearer's …