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

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.

arXiv commentsubmitted to CVPR 2020

References in corpus (5)

• EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras
• Event-based Moving Object Detection and Tracking
• HFirst: A Temporal Approach to Object Recognition
• Event-Based Motion Segmentation by Motion Compensation
• Event-based Star Tracking via Multiresolution Progressive Hough Transforms

Cited by in corpus (1)

• An O(m+n)-Space Spatiotemporal Denoising Filter with Cache-Like Memories for Dynamic Vision Sensors