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Adaptive Slicing Method of the Spatiotemporal Event Stream Obtained from a Dynamic Vision Sensor
The dynamic vision sensor (DVS) measures asynchronously change of brightness per pixel, then outputs an asynchronous and discrete stream of spatiotemporal event information that encodes the time, location, and sign of brightness changes. The dynamic vision sensor has outstanding properties compared...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9003192/ https://www.ncbi.nlm.nih.gov/pubmed/35408227 http://dx.doi.org/10.3390/s22072614 |
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author | Zhang, Yisa Zhao, Yuchen Lv, Hengyi Feng, Yang Liu, Hailong Han, Chengshan |
author_facet | Zhang, Yisa Zhao, Yuchen Lv, Hengyi Feng, Yang Liu, Hailong Han, Chengshan |
author_sort | Zhang, Yisa |
collection | PubMed |
description | The dynamic vision sensor (DVS) measures asynchronously change of brightness per pixel, then outputs an asynchronous and discrete stream of spatiotemporal event information that encodes the time, location, and sign of brightness changes. The dynamic vision sensor has outstanding properties compared to sensors of traditional cameras, with very high dynamic range, high temporal resolution, low power consumption, and does not suffer from motion blur. Hence, dynamic vision sensors have considerable potential for computer vision in scenarios that are challenging for traditional cameras. However, the spatiotemporal event stream has low visualization and is incompatible with existing image processing algorithms. In order to solve this problem, this paper proposes a new adaptive slicing method for the spatiotemporal event stream. The resulting slices of the spatiotemporal event stream contain complete object information, with no motion blur. The slices can be processed either with event-based algorithms or by constructing slices into virtual frames and processing them with traditional image processing algorithms. We tested our slicing method using public as well as our own data sets. The difference between the object information entropy of the slice and the ideal object information entropy is less than 1%. |
format | Online Article Text |
id | pubmed-9003192 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-90031922022-04-13 Adaptive Slicing Method of the Spatiotemporal Event Stream Obtained from a Dynamic Vision Sensor Zhang, Yisa Zhao, Yuchen Lv, Hengyi Feng, Yang Liu, Hailong Han, Chengshan Sensors (Basel) Article The dynamic vision sensor (DVS) measures asynchronously change of brightness per pixel, then outputs an asynchronous and discrete stream of spatiotemporal event information that encodes the time, location, and sign of brightness changes. The dynamic vision sensor has outstanding properties compared to sensors of traditional cameras, with very high dynamic range, high temporal resolution, low power consumption, and does not suffer from motion blur. Hence, dynamic vision sensors have considerable potential for computer vision in scenarios that are challenging for traditional cameras. However, the spatiotemporal event stream has low visualization and is incompatible with existing image processing algorithms. In order to solve this problem, this paper proposes a new adaptive slicing method for the spatiotemporal event stream. The resulting slices of the spatiotemporal event stream contain complete object information, with no motion blur. The slices can be processed either with event-based algorithms or by constructing slices into virtual frames and processing them with traditional image processing algorithms. We tested our slicing method using public as well as our own data sets. The difference between the object information entropy of the slice and the ideal object information entropy is less than 1%. MDPI 2022-03-29 /pmc/articles/PMC9003192/ /pubmed/35408227 http://dx.doi.org/10.3390/s22072614 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zhang, Yisa Zhao, Yuchen Lv, Hengyi Feng, Yang Liu, Hailong Han, Chengshan Adaptive Slicing Method of the Spatiotemporal Event Stream Obtained from a Dynamic Vision Sensor |
title | Adaptive Slicing Method of the Spatiotemporal Event Stream Obtained from a Dynamic Vision Sensor |
title_full | Adaptive Slicing Method of the Spatiotemporal Event Stream Obtained from a Dynamic Vision Sensor |
title_fullStr | Adaptive Slicing Method of the Spatiotemporal Event Stream Obtained from a Dynamic Vision Sensor |
title_full_unstemmed | Adaptive Slicing Method of the Spatiotemporal Event Stream Obtained from a Dynamic Vision Sensor |
title_short | Adaptive Slicing Method of the Spatiotemporal Event Stream Obtained from a Dynamic Vision Sensor |
title_sort | adaptive slicing method of the spatiotemporal event stream obtained from a dynamic vision sensor |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9003192/ https://www.ncbi.nlm.nih.gov/pubmed/35408227 http://dx.doi.org/10.3390/s22072614 |
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