The work explores bio-inspired applications for tasks where frame-based methods are already successful but present robustness flaws because classical frame-based imagers cannot be intrinsically high speed and high dynamic range.
This paper presents a novel fusion of low-level approaches for dimensionality reduction into an effective approach for high-level objects in neuromorphic camera data called Inceptive Event Time-Surfaces (IETS).
We demonstrate, for the first time, a spiking neural network running on neuromorphic hardware for a fully event-based flow cytometry pipeline with 98.45% testing accuracy. We open up new possibilities for online and on-chip learning in flow cytometry applications.
In this paper, we propose the first multibracket HDR pipeline combining a standard camera with an event camera. Our results show better overall robustness when using events, with improvements in PSNR by up to 5dB on synthetic data and up to 0.7dB on real-world data.
We further propose datasets captured by a high-speed camera and an RS-Event hybrid camera system for training and testing our network. Experimental results on both public and proposed datasets show a systematic performance improvement compared to state-of-the-art methods.We further propose datasets captured by a high-speed camera and an RS-Event hybrid camera system for training and testing our network.