灰色关联度与改进的八方向Sobel算子边缘检测

Yang Yang, Lian Wei
{"title":"灰色关联度与改进的八方向Sobel算子边缘检测","authors":"Yang Yang, Lian Wei","doi":"10.4236/JSIP.2021.122002","DOIUrl":null,"url":null,"abstract":"Edge \ndetection is an important aspect to improve image edge quality in image \nprocessing. The purpose of edge detection is to identify the points in digital \nimages with great brightness variation. However, the accuracy of traditional \nedge detection methods in edge extraction is low. For the actual image, the \ngrey edge is sometimes not very clear, the image also contains noise. The \ndetection result of the traditional Sobel operator is relatively accurate, but the detection \nresult is rough and sensitive to noise. To solve the above problems, this paper \nproposes an improved eight-direction Sobel operator based on grey relevancy \ndegree, which combines 5 × 5 Sobel operator with a grey relational degree and \na new eight-direction grey relevancy method. The results show that this method \ncan detect the useful information of edge more accurately and improve the \nanti-noise performance. However, the drawback is that the algorithm is not \nautomatic.","PeriodicalId":38474,"journal":{"name":"Journal of Information Hiding and Multimedia Signal Processing","volume":"6 1","pages":"43-55"},"PeriodicalIF":0.0000,"publicationDate":"2021-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Grey Relevancy Degree and Improved Eight-Direction Sobel Operator Edge Detection\",\"authors\":\"Yang Yang, Lian Wei\",\"doi\":\"10.4236/JSIP.2021.122002\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Edge \\ndetection is an important aspect to improve image edge quality in image \\nprocessing. The purpose of edge detection is to identify the points in digital \\nimages with great brightness variation. However, the accuracy of traditional \\nedge detection methods in edge extraction is low. For the actual image, the \\ngrey edge is sometimes not very clear, the image also contains noise. The \\ndetection result of the traditional Sobel operator is relatively accurate, but the detection \\nresult is rough and sensitive to noise. To solve the above problems, this paper \\nproposes an improved eight-direction Sobel operator based on grey relevancy \\ndegree, which combines 5 × 5 Sobel operator with a grey relational degree and \\na new eight-direction grey relevancy method. The results show that this method \\ncan detect the useful information of edge more accurately and improve the \\nanti-noise performance. However, the drawback is that the algorithm is not \\nautomatic.\",\"PeriodicalId\":38474,\"journal\":{\"name\":\"Journal of Information Hiding and Multimedia Signal Processing\",\"volume\":\"6 1\",\"pages\":\"43-55\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-04-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Information Hiding and Multimedia Signal Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4236/JSIP.2021.122002\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Computer Science\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Information Hiding and Multimedia Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4236/JSIP.2021.122002","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Computer Science","Score":null,"Total":0}
引用次数: 2

摘要

在图像处理中,边缘检测是提高图像边缘质量的一个重要方面。边缘检测的目的是识别数字图像中亮度变化较大的点。然而,传统的边缘检测方法在边缘提取中准确率较低。对于实际图像来说,灰度边缘有时不是很清晰,图像中还含有噪声。传统Sobel算子的检测结果相对准确,但检测结果粗糙且对噪声敏感。针对上述问题,本文提出了一种改进的基于灰色关联度的八方向Sobel算子,该算子将5 × 5 Sobel算子与灰色关联度相结合,提出了一种新的八方向灰色关联方法。结果表明,该方法能更准确地检测出边缘的有用信息,提高了图像的抗噪性能。然而,缺点是该算法不是自动的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Grey Relevancy Degree and Improved Eight-Direction Sobel Operator Edge Detection
Edge detection is an important aspect to improve image edge quality in image processing. The purpose of edge detection is to identify the points in digital images with great brightness variation. However, the accuracy of traditional edge detection methods in edge extraction is low. For the actual image, the grey edge is sometimes not very clear, the image also contains noise. The detection result of the traditional Sobel operator is relatively accurate, but the detection result is rough and sensitive to noise. To solve the above problems, this paper proposes an improved eight-direction Sobel operator based on grey relevancy degree, which combines 5 × 5 Sobel operator with a grey relational degree and a new eight-direction grey relevancy method. The results show that this method can detect the useful information of edge more accurately and improve the anti-noise performance. However, the drawback is that the algorithm is not automatic.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
3.20
自引率
0.00%
发文量
0
期刊最新文献
Adaptive Variational Mode Decomposition for Bearing Fault Detection White Blood Cells Detection Using Spectral Tresholding 3D Ergonomic Board: Kids Teaching and Learning Proposition Design and Evaluation of a Distributed Security Framework for the Internet of Things Improved Bearing Fault Diagnosis by Feature Extraction Based on GLCM, Fusion of Selection Methods, and Multiclass-Naïve Bayes Classification
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1