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Hough Transform Implementation For Event-Based Systems: Concepts and Challenges
Hough transform (HT) is one of the most well-known techniques in computer vision that has been the basis of many practical image processing algorithms. HT however is designed to work for frame-based systems such as conventional digital cameras. Recently, event-based systems such as Dynamic Vision Se...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6308381/ https://www.ncbi.nlm.nih.gov/pubmed/30622466 http://dx.doi.org/10.3389/fncom.2018.00103 |
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author | Seifozzakerini, Sajjad Yau, Wei-Yun Mao, Kezhi Nejati, Hossein |
author_facet | Seifozzakerini, Sajjad Yau, Wei-Yun Mao, Kezhi Nejati, Hossein |
author_sort | Seifozzakerini, Sajjad |
collection | PubMed |
description | Hough transform (HT) is one of the most well-known techniques in computer vision that has been the basis of many practical image processing algorithms. HT however is designed to work for frame-based systems such as conventional digital cameras. Recently, event-based systems such as Dynamic Vision Sensor (DVS) cameras, has become popular among researchers. Event-based cameras have a significantly high temporal resolution (1 μs), but each pixel can only detect change and not color. As such, the conventional image processing algorithms cannot be readily applied to event-based output streams. Therefore, it is necessary to adapt the conventional image processing algorithms for event-based cameras. This paper provides a systematic explanation, starting from extending conventional HT to 3D HT, adaptation to event-based systems, and the implementation of the 3D HT using Spiking Neural Networks (SNNs). Using SNN enables the proposed solution to be easily realized on hardware using FPGA, without requiring CPU or additional memory. In addition, we also discuss techniques for optimal SNN-based implementation using efficient number of neurons for the required accuracy and resolution along each dimension, without increasing the overall computational complexity. We hope that this will help to reduce the gap between event-based and frame-based systems. |
format | Online Article Text |
id | pubmed-6308381 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-63083812019-01-08 Hough Transform Implementation For Event-Based Systems: Concepts and Challenges Seifozzakerini, Sajjad Yau, Wei-Yun Mao, Kezhi Nejati, Hossein Front Comput Neurosci Neuroscience Hough transform (HT) is one of the most well-known techniques in computer vision that has been the basis of many practical image processing algorithms. HT however is designed to work for frame-based systems such as conventional digital cameras. Recently, event-based systems such as Dynamic Vision Sensor (DVS) cameras, has become popular among researchers. Event-based cameras have a significantly high temporal resolution (1 μs), but each pixel can only detect change and not color. As such, the conventional image processing algorithms cannot be readily applied to event-based output streams. Therefore, it is necessary to adapt the conventional image processing algorithms for event-based cameras. This paper provides a systematic explanation, starting from extending conventional HT to 3D HT, adaptation to event-based systems, and the implementation of the 3D HT using Spiking Neural Networks (SNNs). Using SNN enables the proposed solution to be easily realized on hardware using FPGA, without requiring CPU or additional memory. In addition, we also discuss techniques for optimal SNN-based implementation using efficient number of neurons for the required accuracy and resolution along each dimension, without increasing the overall computational complexity. We hope that this will help to reduce the gap between event-based and frame-based systems. Frontiers Media S.A. 2018-12-21 /pmc/articles/PMC6308381/ /pubmed/30622466 http://dx.doi.org/10.3389/fncom.2018.00103 Text en Copyright © 2018 Seifozzakerini, Yau, Mao and Nejati. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Seifozzakerini, Sajjad Yau, Wei-Yun Mao, Kezhi Nejati, Hossein Hough Transform Implementation For Event-Based Systems: Concepts and Challenges |
title | Hough Transform Implementation For Event-Based Systems: Concepts and Challenges |
title_full | Hough Transform Implementation For Event-Based Systems: Concepts and Challenges |
title_fullStr | Hough Transform Implementation For Event-Based Systems: Concepts and Challenges |
title_full_unstemmed | Hough Transform Implementation For Event-Based Systems: Concepts and Challenges |
title_short | Hough Transform Implementation For Event-Based Systems: Concepts and Challenges |
title_sort | hough transform implementation for event-based systems: concepts and challenges |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6308381/ https://www.ncbi.nlm.nih.gov/pubmed/30622466 http://dx.doi.org/10.3389/fncom.2018.00103 |
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