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An Improved Approach for Visualizing Dynamic Vision Sensor and its Video Denoising

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Wang LiAuthors Info & Claims

ICVIP '17: Proceedings of the International Conference on Video and Image Processing

Pages 176 - 180

https://doi.org/10.1145/3177404.3177411

Published: 27 December 2017 Publication History

Abstract

Dynamic vision sensor (DVS) is an event-based camera capturing the changes of vision with high speed and low storage consumption. To better understand what DVS captures, we need to visualize the events. Existing methods have realized visualization. To optimize the vision experience, this paper proposes a framework to visualize events with rich information, high speed and less noise. Firstly, we propose an improved visualization approach using overlapped events based on human vision system. Secondly, we propose a video denoising method using shared dictionaries. In our experiments, the proposed method realizes the expected purpose on the whole video.

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Cited By

Jiang HWang XTang WSong QSong QHao W(2024)Event Stream Denoising Method Based on Spatio-Temporal Density and Time Sequence AnalysisSensors10.3390/s2420652724:20(6527)Online publication date: 10-Oct-2024
https://doi.org/10.3390/s24206527
Zhou QZhang G(2024)Event camera object recognition using spatiotemporal event time surface and reward-modulated spike-timing-dependent plasticity learning ruleJournal of Electronic Imaging10.1117/1.JEI.33.1.01301733:01Online publication date: 1-Jan-2024
https://doi.org/10.1117/1.JEI.33.1.013017
Harada SHara JHigashi HTanaka Y(2024)Denoising for Neuromorphic Cameras Based on Graph Spectral Features2024 IEEE 26th International Workshop on Multimedia Signal Processing (MMSP)10.1109/MMSP61759.2024.10743445(1-6)Online publication date: 2-Oct-2024
https://doi.org/10.1109/MMSP61759.2024.10743445
Show More Cited By

Index Terms

An Improved Approach for Visualizing Dynamic Vision Sensor and its Video Denoising
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Reconstruction

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cover image ACM Other conferences

ICVIP '17: Proceedings of the International Conference on Video and Image Processing

December 2017

272 pages

ISBN:9781450353830

DOI:10.1145/3177404

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Nanyang Technological University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 December 2017

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Ministry of Education project
National Science Foundation (NSF) of China

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ICVIP 2017

ICVIP 2017: International Conference on Video and Image Processing

December 27 - 29, 2017

Singapore, Singapore

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Citations

Cited By

Jiang HWang XTang WSong QSong QHao W(2024)Event Stream Denoising Method Based on Spatio-Temporal Density and Time Sequence AnalysisSensors10.3390/s2420652724:20(6527)Online publication date: 10-Oct-2024
https://doi.org/10.3390/s24206527
Zhou QZhang G(2024)Event camera object recognition using spatiotemporal event time surface and reward-modulated spike-timing-dependent plasticity learning ruleJournal of Electronic Imaging10.1117/1.JEI.33.1.01301733:01Online publication date: 1-Jan-2024
https://doi.org/10.1117/1.JEI.33.1.013017
Harada SHara JHigashi HTanaka Y(2024)Denoising for Neuromorphic Cameras Based on Graph Spectral Features2024 IEEE 26th International Workshop on Multimedia Signal Processing (MMSP)10.1109/MMSP61759.2024.10743445(1-6)Online publication date: 2-Oct-2024
https://doi.org/10.1109/MMSP61759.2024.10743445
Lin WLi YXu CLiu L(2024)A motion denoising algorithm with Gaussian self-adjusting threshold for event cameraThe Visual Computer10.1007/s00371-023-03183-440:9(6567-6580)Online publication date: 18-Jan-2024
https://doi.org/10.1007/s00371-023-03183-4
Zhou QLi X(2022)A Bio-Inspired Hierarchical Spiking Neural Network With Reward-Modulated STDP Learning Rule for AER Object RecognitionIEEE Sensors Journal10.1109/JSEN.2022.318967922:16(16323-16338)Online publication date: 15-Aug-2022
https://doi.org/10.1109/JSEN.2022.3189679
Wu JMa CLi LDong WShi G(2021)Probabilistic Undirected Graph Based Denoising Method for Dynamic Vision SensorIEEE Transactions on Multimedia10.1109/TMM.2020.299395723(1148-1159)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1109/TMM.2020.2993957
Jing LLuo JShi DLi RZhu YXu HCui Y(2021)Two-stage Local Spatio-temporal Event Filter based on Adaptive Thresholds2021 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN52387.2021.9534114(1-8)Online publication date: 18-Jul-2021
https://doi.org/10.1109/IJCNN52387.2021.9534114
Salvatore NGeorge A(2020)Event-Based Noise Filtration with Point-of-Interest Detection and Tracking for Space Situational AwarenessProceedings of the 2020 2nd International Conference on Intelligent Medicine and Image Processing10.1145/3399637.3399656(29-39)Online publication date: 23-Apr-2020
https://dl.acm.org/doi/10.1145/3399637.3399656
Du JXie XWang CShi G(2018)Perceptual Compressive SensingPattern Recognition and Computer Vision10.1007/978-3-030-03338-5_23(268-279)Online publication date: 3-Nov-2018
https://doi.org/10.1007/978-3-030-03338-5_23

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