基于遗传算法的数字曲线多边形逼近

P. B. Alvarado-Velazco, V. Ayala-Ramírez
{"title":"基于遗传算法的数字曲线多边形逼近","authors":"P. B. Alvarado-Velazco, V. Ayala-Ramírez","doi":"10.1109/ICIT.2012.6209947","DOIUrl":null,"url":null,"abstract":"We present a method to approximate a digital curve with a straight line sequence. The curve approximation curve has been posed as an optimization problem, and a genetic algorithm is used to solve such a problem. The proposed approach outputs a set of ending points for the line segments used to encode the resulting polygonal approximation. We have tested our method with a curve dataset composed of 11 open and closed curves. The results show that the proposed method performs qualitatively well on the test dataset. We show also quantitative measures of the similarity between the original input curve and the results of the genetic algorithm-based optimization process.","PeriodicalId":365141,"journal":{"name":"2012 IEEE International Conference on Industrial Technology","volume":"53 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Polygonal approximation of digital curves using genetic algorithms\",\"authors\":\"P. B. Alvarado-Velazco, V. Ayala-Ramírez\",\"doi\":\"10.1109/ICIT.2012.6209947\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present a method to approximate a digital curve with a straight line sequence. The curve approximation curve has been posed as an optimization problem, and a genetic algorithm is used to solve such a problem. The proposed approach outputs a set of ending points for the line segments used to encode the resulting polygonal approximation. We have tested our method with a curve dataset composed of 11 open and closed curves. The results show that the proposed method performs qualitatively well on the test dataset. We show also quantitative measures of the similarity between the original input curve and the results of the genetic algorithm-based optimization process.\",\"PeriodicalId\":365141,\"journal\":{\"name\":\"2012 IEEE International Conference on Industrial Technology\",\"volume\":\"53 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-03-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 IEEE International Conference on Industrial Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIT.2012.6209947\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE International Conference on Industrial Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIT.2012.6209947","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

摘要

提出了一种用直线序列近似数字曲线的方法。将曲线逼近问题作为一个优化问题,利用遗传算法求解该问题。所提出的方法输出一组用于编码所得到的多边形近似的线段端点。我们用一个由11条开曲线和闭曲线组成的曲线数据集测试了我们的方法。结果表明,该方法在测试数据集上具有良好的定性效果。我们还展示了原始输入曲线与基于遗传算法的优化过程的结果之间的相似性的定量度量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Polygonal approximation of digital curves using genetic algorithms
We present a method to approximate a digital curve with a straight line sequence. The curve approximation curve has been posed as an optimization problem, and a genetic algorithm is used to solve such a problem. The proposed approach outputs a set of ending points for the line segments used to encode the resulting polygonal approximation. We have tested our method with a curve dataset composed of 11 open and closed curves. The results show that the proposed method performs qualitatively well on the test dataset. We show also quantitative measures of the similarity between the original input curve and the results of the genetic algorithm-based optimization process.
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