Event Probability Mask (EPM) and Event Denoising Convolutional Neural Network (EDnCNN) for Neuromorphic Cameras

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 benchmarkin...

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Bibliographic Details
Published in:Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) pp. 1698 - 1707
Main Authors: Baldwin, R. Wes, Almatrafi, Mohammed, Asari, Vijayan, Hirakawa, Keigo
Format: Conference Proceeding
Language:English
Published: IEEE 01.06.2020
Subjects:
Benchmark testing
Cameras
Hardware
Neuromorphics
Noise measurement
Noise reduction
Voltage control
ISSN:1063-6919
Online Access:Get full text
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Summary: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.
ISSN:1063-6919
DOI:10.1109/CVPR42600.2020.00177