Segmentation of three-dimensional objects from background in digital holograms

C. McElhinney, J. McDonald, A. Castro, Y. Frauel, B. Javidi, T. Naughton
{"title":"Segmentation of three-dimensional objects from background in digital holograms","authors":"C. McElhinney, J. McDonald, A. Castro, Y. Frauel, B. Javidi, T. Naughton","doi":"10.1109/IMVIP.2007.35","DOIUrl":null,"url":null,"abstract":"We present a technique for performing segmentation of three-dimensional, objects encoded using in-line digital holography from the scenes background. We create a volume of reconstructions through numerically reconstructing a digital hologram at a range of depths. For each reconstruction a variance map is created through calculating variance about a neighbourhood for each of the reconstructions pixels. We can then classify a pixel as object or background by thresholding the maximum variance of every pixel over all depths. We present segmentation results for objects of low and high contrast.","PeriodicalId":249544,"journal":{"name":"International Machine Vision and Image Processing Conference (IMVIP 2007)","volume":"2017 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Machine Vision and Image Processing Conference (IMVIP 2007)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IMVIP.2007.35","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

We present a technique for performing segmentation of three-dimensional, objects encoded using in-line digital holography from the scenes background. We create a volume of reconstructions through numerically reconstructing a digital hologram at a range of depths. For each reconstruction a variance map is created through calculating variance about a neighbourhood for each of the reconstructions pixels. We can then classify a pixel as object or background by thresholding the maximum variance of every pixel over all depths. We present segmentation results for objects of low and high contrast.
数字全息图中三维物体与背景的分割
我们提出了一种技术,用于执行分割三维,对象编码使用在线数字全息从场景背景。我们通过在一定深度范围内对数字全息图进行数值重建,创建了大量的重建。对于每次重建,通过计算每个重建像素的邻域方差来创建方差图。然后,我们可以通过对所有深度上每个像素的最大方差设定阈值,将像素分类为对象或背景。我们给出了低对比度和高对比度目标的分割结果。
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 求助全文
来源期刊
自引率
0.00%
发文量
0
×
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