从阴影区域重建形状的统计分析

A. Poonawala, P. Milanfar, R. Gardner
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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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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