Compact Image Representation by Edge Primitives

Altergartenberg R., Huck F.O., Narayanswamy R.
{"title":"Compact Image Representation by Edge Primitives","authors":"Altergartenberg R.,&nbsp;Huck F.O.,&nbsp;Narayanswamy R.","doi":"10.1006/cgip.1994.1001","DOIUrl":null,"url":null,"abstract":"<div><p>Bandpassed images, commonly used for edge detection, also retain information about intensities between the edge boundaries. Using the familiar Laplacian-of-Gaussian as a bandpass filter, we present a method to extract and code the edge-associated information (edge primitives) and recover an image representation with high structural fidelity. We demonstrate that the edge-primitives representation is compact and therefore can be coded with high compression ratios.</p></div>","PeriodicalId":100349,"journal":{"name":"CVGIP: Graphical Models and Image Processing","volume":"56 1","pages":"Pages 1-7"},"PeriodicalIF":0.0000,"publicationDate":"1994-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1006/cgip.1994.1001","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"CVGIP: Graphical Models and Image Processing","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1049965284710017","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8

Abstract

Bandpassed images, commonly used for edge detection, also retain information about intensities between the edge boundaries. Using the familiar Laplacian-of-Gaussian as a bandpass filter, we present a method to extract and code the edge-associated information (edge primitives) and recover an image representation with high structural fidelity. We demonstrate that the edge-primitives representation is compact and therefore can be coded with high compression ratios.

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
基于边缘基元的压缩图像表示
通常用于边缘检测的带通图像也保留了边缘边界之间的强度信息。使用我们熟悉的拉普拉斯高斯滤波器作为带通滤波器,我们提出了一种提取和编码边缘相关信息(边缘原语)的方法,并恢复具有高结构保真度的图像表示。我们证明了边缘基元表示是紧凑的,因此可以用高压缩比编码。
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
A New Dynamic Approach for Finding the Contour of Bi-Level Images Building Skeleton Models via 3-D Medial Surface Axis Thinning Algorithms Estimation of Edge Parameters and Image Blur Using Polynomial Transforms Binarization and Multithresholding of Document Images Using Connectivity Novel Deconvolution of Noisy Gaussian Filters with a Modified Hermite Expansion
×
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