Pattern recognition in correlated and uncorrelated noise.
Pattern recognition in correlated and uncorrelated noise.
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DOI：
10.1364/josaa.26.000b94
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发表时间：
2009-11
期刊：
影响因子：
--
通讯作者：
Gold JM
中科院分区：
文献类型：
--
作者：
Conrey B;Gold JM
本研究探讨了相关的，或过滤，噪声如何影响识别两种类型的信号模式，伽柏补丁和三维物体的效率。一般来说，与理想观察者相比，人类观察者在低通噪声中执行任务的效率最高，其次是白色噪声;他们在高通噪声中效率最低。仿真表明，对比度相关的内部噪声可能会限制人类的表现在高通条件下的两种信号类型。分类图像显示，观察者可能会采取不同的策略，在低通与白色噪声的存在。然而，效率被低估的线性分类图像和不对称存在于分类子图像，表明非线性过程的影响。响应一致性分析表明，较低的对比度依赖的内部噪声有助于在一定程度上更高的效率，在低通噪声的Gabor补丁，但不是对象。两者合计，这些实验的结果表明，在确定相关和不相关噪声的效率中，信号、外部噪声谱和内部噪声之间存在复杂的相互作用。
This study examined how correlated, or filtered, noise affected efficiency for recognizing two types of signal patterns, Gabor patches and three-dimensional objects. In general, compared with the ideal observer, human observers were most efficient at performing tasks in low-pass noise, followed by white noise; they were least efficient in high-pass noise. Simulations demonstrated that contrast-dependent internal noise was likely to have limited human performance in the high-pass conditions for both signal types. Classification images showed that observers were likely adopting different strategies in the presence of low-pass versus white noise. However, efficiencies were underpredicted by the linear classification images and asymmetries were present in the classification subimages, indicating the influence of nonlinear processes. Response consistency analyses indicated that lower contrast-dependent internal noise contributed somewhat to higher efficiencies in low-pass noise for Gabor patches but not objects. Taken together, the results of these experiments suggest a complex interaction among signals, external noise spectra, and internal noise in determining efficiency in correlated and uncorrelated noise.