Approximating the packedness of polygonal curves

Joachim Gudmundsson, Y. Sha, Sampson Wong
{"title":"Approximating the packedness of polygonal curves","authors":"Joachim Gudmundsson, Y. Sha, Sampson Wong","doi":"10.4230/LIPIcs.ISAAC.2020.9","DOIUrl":null,"url":null,"abstract":"In 2012 Driemel et al. \\cite{DBLP:journals/dcg/DriemelHW12} introduced the concept of $c$-packed curves as a realistic input model. In the case when $c$ is a constant they gave a near linear time $(1+\\varepsilon)$-approximation algorithm for computing the Frechet distance between two $c$-packed polygonal curves. Since then a number of papers have used the model. \nIn this paper we consider the problem of computing the smallest $c$ for which a given polygonal curve in $\\mathbb{R}^d$ is $c$-packed. We present two approximation algorithms. The first algorithm is a $2$-approximation algorithm and runs in $O(dn^2 \\log n)$ time. In the case $d=2$ we develop a faster algorithm that returns a $(6+\\varepsilon)$-approximation and runs in $O((n/\\varepsilon^3)^{4/3} polylog (n/\\varepsilon)))$ time. \nWe also implemented the first algorithm and computed the approximate packedness-value for 16 sets of real-world trajectories. The experiments indicate that the notion of $c$-packedness is a useful realistic input model for many curves and trajectories.","PeriodicalId":11245,"journal":{"name":"Discret. Comput. Geom.","volume":"19 1","pages":"101920"},"PeriodicalIF":0.0000,"publicationDate":"2020-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Discret. Comput. Geom.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4230/LIPIcs.ISAAC.2020.9","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6

Abstract

In 2012 Driemel et al. \cite{DBLP:journals/dcg/DriemelHW12} introduced the concept of $c$-packed curves as a realistic input model. In the case when $c$ is a constant they gave a near linear time $(1+\varepsilon)$-approximation algorithm for computing the Frechet distance between two $c$-packed polygonal curves. Since then a number of papers have used the model. In this paper we consider the problem of computing the smallest $c$ for which a given polygonal curve in $\mathbb{R}^d$ is $c$-packed. We present two approximation algorithms. The first algorithm is a $2$-approximation algorithm and runs in $O(dn^2 \log n)$ time. In the case $d=2$ we develop a faster algorithm that returns a $(6+\varepsilon)$-approximation and runs in $O((n/\varepsilon^3)^{4/3} polylog (n/\varepsilon)))$ time. We also implemented the first algorithm and computed the approximate packedness-value for 16 sets of real-world trajectories. The experiments indicate that the notion of $c$-packedness is a useful realistic input model for many curves and trajectories.
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
近似多边形曲线的填充性
2012年,Driemel等\cite{DBLP:journals/dcg/DriemelHW12}引入了$c$填充曲线的概念,作为一种现实的输入模型。在$c$为常数的情况下,他们给出了一个近似线性时间$(1+\varepsilon)$ -近似算法,用于计算两条$c$填充多边形曲线之间的Frechet距离。从那以后,许多论文都使用了这个模型。本文考虑了$\mathbb{R}^d$中给定多边形曲线为$c$填充的最小$c$的计算问题。我们提出了两种近似算法。第一种算法是$2$ -近似算法,运行时间为$O(dn^2 \log n)$。在$d=2$的情况下,我们开发了一个更快的算法,它返回一个$(6+\varepsilon)$ -近似值,运行时间为$O((n/\varepsilon^3)^{4/3} polylog (n/\varepsilon)))$。我们还实现了第一种算法,并计算了16组真实轨迹的近似打包值。实验表明,$c$ -填充的概念对于许多曲线和轨迹是一个有用的现实输入模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
On Reverse Shortest Paths in Geometric Proximity Graphs Algorithms for Radius-Optimally Augmenting Trees in a Metric Space Augmenting Graphs to Minimize the Radius Linear-Time Approximation Scheme for k-Means Clustering of Axis-Parallel Affine Subspaces Intersecting Disks Using Two Congruent Disks
×
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