EFFECT OF RANDOM BACKGROUND INHOMOGENEITY ON OBSERVER DETECTION PERFORMANCE
EFFECT OF RANDOM BACKGROUND INHOMOGENEITY ON OBSERVER DETECTION PERFORMANCE
复制标题
DOI：
10.1364/josaa.9.000649
复制
发表时间：
1992-05-01
影响因子：
1.9
通讯作者：
BARRETT, HH
中科院分区：
文献类型：
--
作者：
ROLLAND, JP;BARRETT, HH
许多心理物理学研究的能力，人类观察员检测叠加在一个统一的背景，其中的信号和背景都是已知的，已经在文献中报道。在这种情况下，理想或贝叶斯观测器通常被用作人类表现的数学模型，因为它可以很容易地计算出来，并且是手头任务的人类表现的良好预测器。然而，如果背景是空间不均匀的（亮度），理想的观察者成为非线性的，其性能变得难以评估。由于不均匀的背景是经常遇到的许多实际应用中，我们已经研究了背景的不均匀性对人类的表现的影响。任务是检测叠加在不均匀背景上的二维高斯信号，并通过针孔成像系统成像。对应于一定的曝光时间和光圈大小的泊松噪声被添加到检测到的图像。一个六点评级量表技术被用来衡量人类的表现作为一个功能的强度的不均匀性（块状）在背景中，成像系统的模糊量，和泊松噪声的图像。本研究的结果与Myers等人的早期理论预测进行了比较[J. Opt. Soc. Am. A 7，1279（1990）]的两个观测器模型：最佳线性判别，也称为霍特林观测器，和一个非预白化匹配滤波器。虽然人类观察者相对于霍特林观察者的效率仅为约10%，但霍特林模型很好地预测了人类表现相对于不同光圈大小和曝光时间的变化。另一方面，nonprewhitening模型在本研究中未能预测人类在明亮背景下的表现。特别是，该模型预测性能将随着暴露时间的增加而饱和，并随着块状度的增加而急剧下降;人类观察者没有观察到这两种效果。
Many psychophysical studies of the ability of the human observer to detect a signal superimposed upon a uniform background, where both the signal and the background are known exactly, have been reported in the literature. In such cases, the ideal or the Bayesian observer is often used as a mathematical model of human performance since it can be readily calculated and is a good predictor of human performance for the task at hand. If, however, the background is spatially inhomogeneous (lumpy), the ideal observer becomes nonlinear, and its performance becomes difficult to evaluate. Since inhomogeneous backgrounds are commonly encountered in many practical applications, we have investigated the effects of background inhomogeneities on human performance. The task was detection of a two-dimensional Gaussian signal superimposed upon an inhomogeneous background and imaged through a pinhole imaging system. Poisson noise corresponding to a certain exposure time and aperture size was added to the detected image. A six-point rating scale technique was used to measure human performance as a function of the strength of the nonuniformities (lumpiness) in the background, the amount of blur of the imaging system, and the amount of Poisson noise in the image. The results of this study were compared with earlier theoretical predictions by Myers et al. [J. Opt. Soc. Am. A 7, 1279 (1990)] for two observer models: the optimum linear discriminant, also known as the Hotelling observer, and a nonprewhitening matched filter. Although the efficiency of the human observer relative to the Hotelling observer was only approximately 10%, the variation in human performance with respect to varying aperture size and exposure time was well predicted by the Hotelling model. The nonprewhitening model, on the other hand, fails to predict human performance in lumpy backgrounds in this study. In particular, this model predicts that performance will saturate with increasing exposure time and drop precipitously with increasing lumpiness; neither effect is observed with human observers.