{"title":"点画艺术的分层重建","authors":"A. D. Parakkat, Pooran Memari, Marie-Paule Cani","doi":"10.1145/3306214.3338598","DOIUrl":null,"url":null,"abstract":"Given a point set S ⊆ R2, reconstruction refers to the process of identifying a vector shape that best approximates the input. Although this field was pioneered since 1983 by Edelsbrunner [Edelsbrunner et al. 2006] and has been heavily studied since then, the general problem still remains open, ill-posed and challenging. Solving it is essential for a wide range of applications, from image processing, pattern recognition and sketching to wireless networks.","PeriodicalId":216038,"journal":{"name":"ACM SIGGRAPH 2019 Posters","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Layered reconstruction of stippling art\",\"authors\":\"A. D. Parakkat, Pooran Memari, Marie-Paule Cani\",\"doi\":\"10.1145/3306214.3338598\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Given a point set S ⊆ R2, reconstruction refers to the process of identifying a vector shape that best approximates the input. Although this field was pioneered since 1983 by Edelsbrunner [Edelsbrunner et al. 2006] and has been heavily studied since then, the general problem still remains open, ill-posed and challenging. Solving it is essential for a wide range of applications, from image processing, pattern recognition and sketching to wireless networks.\",\"PeriodicalId\":216038,\"journal\":{\"name\":\"ACM SIGGRAPH 2019 Posters\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-07-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM SIGGRAPH 2019 Posters\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3306214.3338598\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM SIGGRAPH 2019 Posters","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3306214.3338598","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
摘要
给定一个点集S≥R2，重构是指确定一个最接近输入的矢量形状的过程。尽管这一领域自1983年由Edelsbrunner (Edelsbrunner et al. 2006)开创，并从那时起进行了大量研究，但总体问题仍然是开放的，病态的和具有挑战性的。从图像处理、模式识别和素描到无线网络，解决这个问题对于广泛的应用至关重要。
Given a point set S ⊆ R2, reconstruction refers to the process of identifying a vector shape that best approximates the input. Although this field was pioneered since 1983 by Edelsbrunner [Edelsbrunner et al. 2006] and has been heavily studied since then, the general problem still remains open, ill-posed and challenging. Solving it is essential for a wide range of applications, from image processing, pattern recognition and sketching to wireless networks.
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