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自然图像中模式掩蔽的相位依赖性不对称由固有位置不确定性解释。

Phase-dependent asymmetry of pattern masking in natural images explained by intrinsic position uncertainty.

机构信息

Center for Perceptual Systems, University of Texas at Austin, Austin, TX, USA.

Department of Physics, University of Texas at Austin, Austin, TX, USA.

出版信息

J Vis. 2023 Sep 1;23(10):16. doi: 10.1167/jov.23.10.16.

Abstract

A number of recent studies have been directed at measuring and modeling detection of targets at specific locations in natural backgrounds, a key subtask of visual search in natural environments. A useful approach is to bin natural background patches into joint histograms with bins along specific background dimensions. By measuring psychometric functions in a sparse subset of these bins, it is possible to estimate how the included dimensions jointly affect detectability over the whole space of natural backgrounds. In previous studies, we found that threshold is proportional to the product of the background luminance, contrast, and similarity; a result predicted by a simple template-matching observer with divisive normalization along each of the dimensions. The measure of similarity was the cosine similarity of the amplitude spectra of the target and background (SA)-a phase-invariant measure. Here, we investigated the effect of the cosine similarity of the target and background images (SI|A)-a phase-dependent measure. We found that threshold decreases monotonically with SI|A in agreement with a recent study (Rideaux et al., 2022). In contrast, the template-matching observer predicts threshold to be a U-shaped function of SI|A reaching a minimum when the target and background are orthogonal (SI|A = 0). Surprisingly, when the template-matching observer includes a small amount of intrinsic position uncertainty (measured in a separate experiment) the pattern of thresholds is explained.

摘要

最近的一些研究旨在测量和建模在自然背景中特定位置的目标检测,这是自然环境中视觉搜索的关键子任务。一种有用的方法是将自然背景补丁按特定背景维度组合到联合直方图中。通过在这些 bin 的稀疏子集中测量心理测量函数,可以估计包含的维度如何共同影响整个自然背景空间的可检测性。在之前的研究中,我们发现阈值与背景亮度、对比度和相似性的乘积成正比;这是一个简单的模板匹配观察者的结果,该观察者沿每个维度进行除法归一化。相似性的度量是目标和背景的幅度谱的余弦相似度(SA)——一种相位不变的度量。在这里,我们研究了目标和背景图像的余弦相似度(SI|A)——一种相位相关的度量的影响。我们发现,阈值随着 SI|A 的单调下降与最近的一项研究(Rideaux 等人,2022 年)一致。相比之下,模板匹配观察者预测阈值是 SI|A 的 U 形函数,当目标和背景正交时(SI|A = 0)达到最小值。令人惊讶的是,当模板匹配观察者包含少量内在位置不确定性(在单独的实验中测量)时,解释了阈值的模式。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd17/10540874/2ae166af7a48/jovi-23-10-16-f001.jpg

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