{"title":"从阴影区域重建形状的统计分析","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":"{\"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}","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
摘要
我们通过在估计的二维边界上推导Cramer-Rao下界(CRLB),对亮度函数(阴影区域)测量的形状重建问题进行了统计分析。利用置信区域技术分析和可视化二维参数形状估计问题的性能。由于亮度函数数据非常弱,因此对形状参数使用约束CRLB来形成置信区域。从亮度函数的多次测量中重建凸物体形状的算法在[R]中得到了发展。J. Gardner, P. Milanfar,“基于亮度函数的形状重建”,2001 [R]。J. Gardner和P. Milanfar,“从亮度函数重建凸体”。提出的Cramer-Rao界分析提供了统计估计,可用于这些算法的性能评估。
A statistical analysis of shape reconstruction from areas of shadows
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.