多边形闭合曲线分类的Legendre描述符

Q4 Computer Science
V. Neagoe
{"title":"多边形闭合曲线分类的Legendre描述符","authors":"V. Neagoe","doi":"10.1109/ICPR.1992.201877","DOIUrl":null,"url":null,"abstract":"Proposes the use of Legendre descriptors (LDs) as features for classification of polygonal closed curves. The normalized cumulative angular function of such a curve is expanded in a Legendre polynomial truncated series whose coefficients are used as shape features called the Legendre descriptors (LDs). By considering several examples of polygonal object classification, the computer simulation shows that the LDs lead to significantly better results (increase of interclass distances), by comparison with the classical Fourier descriptors. It seems that the world of Legendre polynomials is more suitable to approximate a polygonal curve than the world of sinusoidal function.<<ETX>>","PeriodicalId":34917,"journal":{"name":"模式识别与人工智能","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"1992-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Legendre descriptors for classification of polygonal closed curves\",\"authors\":\"V. Neagoe\",\"doi\":\"10.1109/ICPR.1992.201877\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Proposes the use of Legendre descriptors (LDs) as features for classification of polygonal closed curves. The normalized cumulative angular function of such a curve is expanded in a Legendre polynomial truncated series whose coefficients are used as shape features called the Legendre descriptors (LDs). By considering several examples of polygonal object classification, the computer simulation shows that the LDs lead to significantly better results (increase of interclass distances), by comparison with the classical Fourier descriptors. It seems that the world of Legendre polynomials is more suitable to approximate a polygonal curve than the world of sinusoidal function.<<ETX>>\",\"PeriodicalId\":34917,\"journal\":{\"name\":\"模式识别与人工智能\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1992-08-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"模式识别与人工智能\",\"FirstCategoryId\":\"1093\",\"ListUrlMain\":\"https://doi.org/10.1109/ICPR.1992.201877\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Computer Science\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"模式识别与人工智能","FirstCategoryId":"1093","ListUrlMain":"https://doi.org/10.1109/ICPR.1992.201877","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Computer Science","Score":null,"Total":0}
引用次数: 2

摘要

提出了将Legendre描述子(ld)作为多边形闭合曲线分类的特征。将这种曲线的归一化累积角函数展开为Legendre多项式截断级数,其系数用作形状特征,称为Legendre描述符(ld)。通过对若干多边形目标分类实例的计算机仿真,表明与经典傅里叶描述子相比,ld具有明显更好的分类效果(类间距离增加)。似乎让让德多项式的世界比正弦函数的世界更适合于近似多边形曲线
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Legendre descriptors for classification of polygonal closed curves
Proposes the use of Legendre descriptors (LDs) as features for classification of polygonal closed curves. The normalized cumulative angular function of such a curve is expanded in a Legendre polynomial truncated series whose coefficients are used as shape features called the Legendre descriptors (LDs). By considering several examples of polygonal object classification, the computer simulation shows that the LDs lead to significantly better results (increase of interclass distances), by comparison with the classical Fourier descriptors. It seems that the world of Legendre polynomials is more suitable to approximate a polygonal curve than the world of sinusoidal function.<>
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
模式识别与人工智能
模式识别与人工智能 Computer Science-Artificial Intelligence
CiteScore
1.60
自引率
0.00%
发文量
3316
期刊介绍:
×
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