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.