{"title":"A statistical analysis of shape reconstruction from areas of shadows","authors":"A. Poonawala, P. Milanfar, R. Gardner","doi":"10.1109/ACSSC.2002.1197310","DOIUrl":null,"url":null,"abstract":"We present a statistical analysis of the problem of shape reconstruction from measurements of the brightness function (areas of shadows) by deriving the Cramer-Rao lower bound (CRLB) on the estimated 2-D boundary. Confidence region techniques are used to analyze and visualize the performance of the 2-D parametric shape estimation problem. The brightness function data is very weak, so a constrained CRLB is used on the shape parameters to form the confidence regions. Algorithms for reconstructing the shape of a convex object from multiple measurements of its brightness function were developed in [R. J. Gardner and P. Milanfar, \"Shape reconstruction from brightness functions\", August 2001] and [R. J. Gardner and P. Milanfar, \"Reconstruction of convex bodies from brightness functions\"]. The Cramer-Rao bound analysis presented provides statistical estimates that can be used for performance evaluation of these algorithms.","PeriodicalId":284950,"journal":{"name":"Conference Record of the Thirty-Sixth Asilomar Conference on Signals, Systems and Computers, 2002.","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Conference Record of the Thirty-Sixth Asilomar Conference on Signals, Systems and Computers, 2002.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ACSSC.2002.1197310","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
Abstract
We present a statistical analysis of the problem of shape reconstruction from measurements of the brightness function (areas of shadows) by deriving the Cramer-Rao lower bound (CRLB) on the estimated 2-D boundary. Confidence region techniques are used to analyze and visualize the performance of the 2-D parametric shape estimation problem. The brightness function data is very weak, so a constrained CRLB is used on the shape parameters to form the confidence regions. Algorithms for reconstructing the shape of a convex object from multiple measurements of its brightness function were developed in [R. J. Gardner and P. Milanfar, "Shape reconstruction from brightness functions", August 2001] and [R. J. Gardner and P. Milanfar, "Reconstruction of convex bodies from brightness functions"]. The Cramer-Rao bound analysis presented provides statistical estimates that can be used for performance evaluation of these algorithms.