{"title":"一种高效的RGB彩色图像Delaunay网格模型生成方法","authors":"Jun Luo, M. Adams","doi":"10.1109/PACRIM47961.2019.8985067","DOIUrl":null,"url":null,"abstract":"A highly effective method for generating Delaunay mesh models of RGB (i.e., red-green-blue) color images, known as CMG, is proposed. This method builds on ideas from the previously-proposed GPRFSED method of Adams for grayscale images to produce a method that can handle RGB color images. The key ideas embodied in our CMG method are Floyd-Steinberg error diffusion with improved initial-condition selection and greedy-point removal. Through experimental results, our CMG method is shown to outperform several competing methods that are based on a straightforward extension of grayscale mesh generators to color, with our method yielding meshes of vastly better quality at lower or comparable computational/memory cost.","PeriodicalId":152556,"journal":{"name":"2019 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A Highly-Effective Approach for Generating Delaunay Mesh Models of RGB Color Images\",\"authors\":\"Jun Luo, M. Adams\",\"doi\":\"10.1109/PACRIM47961.2019.8985067\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A highly effective method for generating Delaunay mesh models of RGB (i.e., red-green-blue) color images, known as CMG, is proposed. This method builds on ideas from the previously-proposed GPRFSED method of Adams for grayscale images to produce a method that can handle RGB color images. The key ideas embodied in our CMG method are Floyd-Steinberg error diffusion with improved initial-condition selection and greedy-point removal. Through experimental results, our CMG method is shown to outperform several competing methods that are based on a straightforward extension of grayscale mesh generators to color, with our method yielding meshes of vastly better quality at lower or comparable computational/memory cost.\",\"PeriodicalId\":152556,\"journal\":{\"name\":\"2019 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM)\",\"volume\":\"54 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PACRIM47961.2019.8985067\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PACRIM47961.2019.8985067","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Highly-Effective Approach for Generating Delaunay Mesh Models of RGB Color Images
A highly effective method for generating Delaunay mesh models of RGB (i.e., red-green-blue) color images, known as CMG, is proposed. This method builds on ideas from the previously-proposed GPRFSED method of Adams for grayscale images to produce a method that can handle RGB color images. The key ideas embodied in our CMG method are Floyd-Steinberg error diffusion with improved initial-condition selection and greedy-point removal. Through experimental results, our CMG method is shown to outperform several competing methods that are based on a straightforward extension of grayscale mesh generators to color, with our method yielding meshes of vastly better quality at lower or comparable computational/memory cost.