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人类观察者在随机背景中检测随机信号的效率。

Efficiency of the human observer detecting random signals in random backgrounds.

作者信息

Park Subok, Clarkson Eric, Kupinski Matthew A, Barrett Harrison H

机构信息

Program in Applied Mathematics and Department of Radiology, University of Arizona, Tucson, Arizona 85724, USA.

出版信息

J Opt Soc Am A Opt Image Sci Vis. 2005 Jan;22(1):3-16. doi: 10.1364/josaa.22.000003.

Abstract

The efficiencies of the human observer and the channelized-Hotelling observer relative to the ideal observer for signal-detection tasks are discussed. Both signal-known-exactly (SKE) tasks and signal-known-statistically (SKS) tasks are considered. Signal location is uncertain for the SKS tasks, and lumpy backgrounds are used for background uncertainty in both cases. Markov chain Monte Carlo methods are employed to determine ideal-observer performance on the detection tasks. Psychophysical studies are conducted to compute human-observer performance on the same tasks. Efficiency is computed as the squared ratio of the detectabilities of the observer of interest to the ideal observer. Human efficiencies are approximately 2.1% and 24%, respectively, for the SKE and SKS tasks. The results imply that human observers are not affected as much as the ideal observer by signal-location uncertainty even though the ideal observer outperforms the human observer for both tasks. Three different simplified pinhole imaging systems are simulated, and the humans and the model observers rank the systems in the same order for both the SKE and the SKS tasks.

摘要

讨论了人类观察者和通道化霍特林观察者在信号检测任务中相对于理想观察者的效率。同时考虑了精确已知信号(SKE)任务和统计已知信号(SKS)任务。对于SKS任务,信号位置是不确定的,并且在两种情况下都使用块状背景来表示背景不确定性。采用马尔可夫链蒙特卡罗方法来确定理想观察者在检测任务中的性能。进行了心理物理学研究以计算人类观察者在相同任务上的性能。效率被计算为感兴趣观察者与理想观察者的可检测性的平方比。对于SKE和SKS任务,人类效率分别约为2.1%和24%。结果表明,尽管理想观察者在两项任务中都优于人类观察者,但信号位置不确定性对人类观察者的影响不如对理想观察者的影响大。模拟了三种不同的简化针孔成像系统,在SKE和SKS任务中,人类观察者和模型观察者对这些系统的排序相同。

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1
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4
Validating the use of channels to estimate the ideal linear observer.
J Opt Soc Am A Opt Image Sci Vis. 2003 Sep;20(9):1725-38. doi: 10.1364/josaa.20.001725.
5
Ideal-observer computation in medical imaging with use of Markov-chain Monte Carlo techniques.
J Opt Soc Am A Opt Image Sci Vis. 2003 Mar;20(3):430-8. doi: 10.1364/josaa.20.000430.
6
The psychometric function: II. Bootstrap-based confidence intervals and sampling.
Percept Psychophys. 2001 Nov;63(8):1314-29. doi: 10.3758/bf03194545.
7
The psychometric function: I. Fitting, sampling, and goodness of fit.
Percept Psychophys. 2001 Nov;63(8):1293-313. doi: 10.3758/bf03194544.
8
Human observer detection experiments with mammograms and power-law noise.
Med Phys. 2001 Apr;28(4):419-37. doi: 10.1118/1.1355308.
9
Effect of inherent location uncertainty on detection of stationary targets in noisy image sequences.
J Opt Soc Am A Opt Image Sci Vis. 2001 Jan;18(1):78-85. doi: 10.1364/josaa.18.000078.
10
"Proper" Binormal ROC Curves: Theory and Maximum-Likelihood Estimation.
J Math Psychol. 1999 Mar;43(1):1-33. doi: 10.1006/jmps.1998.1218.

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