Clarkson Eric
College of Optical Sciences, The University of Arizona, 1630 East University Boulevard, Tucson, Arizona 85721, USA.
J Opt Soc Am A Opt Image Sci Vis. 2007 Dec;24(12):B91-8. doi: 10.1364/josaa.24.000b91.
The localization receiver operating characteristic (LROC) curve is a standard method to quantify performance for the task of detecting and locating a signal. This curve is generalized to arbitrary detection/estimation tasks to give the estimation ROC (EROC) curve. For a two-alternative forced-choice study, where the observer must decide which of a pair of images has the signal and then estimate parameters pertaining to the signal, it is shown that the average value of the utility on those image pairs where the observer chooses the correct image is an estimate of the area under the EROC curve (AEROC). The ideal LROC observer is generalized to the ideal EROC observer, whose EROC curve lies above those of all other observers for the given detection/estimation task. When the utility function is nonnegative, the ideal EROC observer is shown to share many mathematical properties with the ideal observer for the pure detection task. When the utility function is concave, the ideal EROC observer makes use of the posterior mean estimator. Other estimators that arise as special cases include maximum a posteriori estimators and maximum-likelihood estimators.
定位接收器操作特性(LROC)曲线是一种用于量化信号检测和定位任务性能的标准方法。该曲线被推广到任意检测/估计任务,以给出估计ROC(EROC)曲线。对于二选一强制选择研究,观察者必须决定一对图像中哪一个有信号,然后估计与信号相关的参数,结果表明,观察者选择正确图像的那些图像对的效用平均值是EROC曲线下面积(AEROC)的估计值。理想LROC观察者被推广到理想EROC观察者,对于给定的检测/估计任务,其EROC曲线位于所有其他观察者的曲线之上。当效用函数为非负时,理想EROC观察者被证明与纯检测任务的理想观察者具有许多数学性质。当效用函数为凹函数时,理想EROC观察者使用后验均值估计器。作为特殊情况出现的其他估计器包括最大后验估计器和最大似然估计器。