一种新的聚类扫描线算法

Xiaoguang Tian, Yuke Ma, X. Hou
{"title":"一种新的聚类扫描线算法","authors":"Xiaoguang Tian, Yuke Ma, X. Hou","doi":"10.1109/HIS.2009.129","DOIUrl":null,"url":null,"abstract":"Correct recognition of the lines is essential for technical drawing understanding. Automation solution is quite difficult due to the limitations of machine vision algorithm. In order to promote development of better technology, according to the fast and high-quality clustering algorithm Particle Swarm Optimization (PSO), a new fast and high-quality line clustering algorithm present in this paper, that consisting of one scan-line connected components processing are clustered and an appropriate measure to recognize the pattern of every line including the dash-line in the drawing paper. The underlying mechanisms are excluding isolated components, a sequential stepwise recovery of components that meet certain continuity conditions and the results presented the node-tree structure that can enhance efficiency of computer. The performance of the algorithm is better in our experiment","PeriodicalId":414085,"journal":{"name":"2009 Ninth International Conference on Hybrid Intelligent Systems","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"A New Scan-Line Algorithm Using Clustering Approach\",\"authors\":\"Xiaoguang Tian, Yuke Ma, X. Hou\",\"doi\":\"10.1109/HIS.2009.129\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Correct recognition of the lines is essential for technical drawing understanding. Automation solution is quite difficult due to the limitations of machine vision algorithm. In order to promote development of better technology, according to the fast and high-quality clustering algorithm Particle Swarm Optimization (PSO), a new fast and high-quality line clustering algorithm present in this paper, that consisting of one scan-line connected components processing are clustered and an appropriate measure to recognize the pattern of every line including the dash-line in the drawing paper. The underlying mechanisms are excluding isolated components, a sequential stepwise recovery of components that meet certain continuity conditions and the results presented the node-tree structure that can enhance efficiency of computer. The performance of the algorithm is better in our experiment\",\"PeriodicalId\":414085,\"journal\":{\"name\":\"2009 Ninth International Conference on Hybrid Intelligent Systems\",\"volume\":\"9 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-08-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 Ninth International Conference on Hybrid Intelligent Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/HIS.2009.129\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 Ninth International Conference on Hybrid Intelligent Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HIS.2009.129","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

摘要

对线条的正确认识对于理解技术图纸是至关重要的。由于机器视觉算法的限制,自动化解决相当困难。为了促进更好的技术发展,根据快速、高质量的聚类算法粒子群优化(Particle Swarm Optimization, PSO),本文提出了一种新的快速、高质量的直线聚类算法,该算法由一条扫描线相连的组件处理聚类,并采取适当的措施来识别图纸上包括虚线在内的每条直线的模式。其基本机制是排除孤立组件,对满足一定连续性条件的组件进行顺序逐步恢复,并给出了提高计算机效率的节点树结构。在我们的实验中,该算法的性能较好
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A New Scan-Line Algorithm Using Clustering Approach
Correct recognition of the lines is essential for technical drawing understanding. Automation solution is quite difficult due to the limitations of machine vision algorithm. In order to promote development of better technology, according to the fast and high-quality clustering algorithm Particle Swarm Optimization (PSO), a new fast and high-quality line clustering algorithm present in this paper, that consisting of one scan-line connected components processing are clustered and an appropriate measure to recognize the pattern of every line including the dash-line in the drawing paper. The underlying mechanisms are excluding isolated components, a sequential stepwise recovery of components that meet certain continuity conditions and the results presented the node-tree structure that can enhance efficiency of computer. The performance of the algorithm is better in our experiment
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
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信