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Design and Image Research of Tennis Line Examination Based on Machine Vision Analysis

In view of the intelligent demand of tennis line examination, this paper performs a systematic analysis on the intelligent recognition of tennis line examination. Then, a tennis line recognition method based on machine vision is proposed. In this paper, the color region of the image recognition regi...

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Detalles Bibliográficos
Autores principales: Yan, Liu, Xin, Sun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8478562/
https://www.ncbi.nlm.nih.gov/pubmed/34594370
http://dx.doi.org/10.1155/2021/2436120
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author Yan, Liu
Xin, Sun
author_facet Yan, Liu
Xin, Sun
author_sort Yan, Liu
collection PubMed
description In view of the intelligent demand of tennis line examination, this paper performs a systematic analysis on the intelligent recognition of tennis line examination. Then, a tennis line recognition method based on machine vision is proposed. In this paper, the color region of the image recognition region is divided based on the region growth, and the rough estimation of the court boundary is realized. In order to achieve the effect of camera calibration, a fast camera calibration method which can be used for a variety of court types is proposed. On the basis of camera calibration, a tennis line examination and segmentation system based on machine vision analysis is constructed, and the experimental results are verified by design experiments. The results show that the machine vision analysis-based intelligent segmentation system of tennis line examination has high recognition accuracy and can meet the actual needs of tennis line examination.
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spelling pubmed-84785622021-09-29 Design and Image Research of Tennis Line Examination Based on Machine Vision Analysis Yan, Liu Xin, Sun Comput Intell Neurosci Research Article In view of the intelligent demand of tennis line examination, this paper performs a systematic analysis on the intelligent recognition of tennis line examination. Then, a tennis line recognition method based on machine vision is proposed. In this paper, the color region of the image recognition region is divided based on the region growth, and the rough estimation of the court boundary is realized. In order to achieve the effect of camera calibration, a fast camera calibration method which can be used for a variety of court types is proposed. On the basis of camera calibration, a tennis line examination and segmentation system based on machine vision analysis is constructed, and the experimental results are verified by design experiments. The results show that the machine vision analysis-based intelligent segmentation system of tennis line examination has high recognition accuracy and can meet the actual needs of tennis line examination. Hindawi 2021-09-20 /pmc/articles/PMC8478562/ /pubmed/34594370 http://dx.doi.org/10.1155/2021/2436120 Text en Copyright © 2021 Liu Yan and Sun Xin. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Yan, Liu
Xin, Sun
Design and Image Research of Tennis Line Examination Based on Machine Vision Analysis
title Design and Image Research of Tennis Line Examination Based on Machine Vision Analysis
title_full Design and Image Research of Tennis Line Examination Based on Machine Vision Analysis
title_fullStr Design and Image Research of Tennis Line Examination Based on Machine Vision Analysis
title_full_unstemmed Design and Image Research of Tennis Line Examination Based on Machine Vision Analysis
title_short Design and Image Research of Tennis Line Examination Based on Machine Vision Analysis
title_sort design and image research of tennis line examination based on machine vision analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8478562/
https://www.ncbi.nlm.nih.gov/pubmed/34594370
http://dx.doi.org/10.1155/2021/2436120
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