Event probability mask (EPM) and event denoising convolutional neural network (EDNCNN) for neuromorphic cameras
Document Type
Conference Paper
Publication Date
1-1-2020
Publication Source
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Abstract
This paper presents a novel method for labeling real-world neuromorphic camera sensor data by calculating the likelihood of generating an event at each pixel within a short time window, which we refer to as “event probability mask” or EPM. Its applications include (i) objective benchmarking of event denoising performance, (ii) training convolutional neural networks for noise removal called “event denoising convolutional neural network” (EDnCNN), and (iii) estimating internal neuromorphic camera parameters. We provide the first dataset (DVSNOISE20) of real-world labeled neuromorphic camera events for noise removal.
Inclusive pages
1698-1707
ISBN/ISSN
1063-6919
Copyright
Copyright © 2020 IEEE - All rights reserved
Publisher
IEEE
eCommons Citation
Baldwin, R. Wes; Almatrafi, Mohammed; Asari, Vijayan K.; and Hirakawa, Keigo, "Event probability mask (EPM) and event denoising convolutional neural network (EDNCNN) for neuromorphic cameras" (2020). Electrical and Computer Engineering Faculty Publications. 442.
https://ecommons.udayton.edu/ece_fac_pub/442
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