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首页> 外文期刊>Journal of vision >Dimensions of Masking Measured by Constrained Natural Scene Sampling
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Dimensions of Masking Measured by Constrained Natural Scene Sampling

机译:通过约束自然场景采样测量的蒙版尺寸

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An ultimate goal of vision science is to understand performance under natural conditions. We describe a direct experimental approach for identifying and quantifying the factors that affect detection performance in natural scenes. A large collection of calibrated natural images is divided into millions of background patches that are then sorted into narrow bins along the dimensions of interest. In the present study, each bin represents a particular (narrow range of) mean luminance, contrast, and similarity (phase-independent correlation of the background to the target). Next, detection thresholds are measured parametrically for a sparse subset of bins spanning the entire space. The psychometric function for each bin is measured by randomly sampling background patches from that bin, without replacement. Finally, we analyze the residual variation of the background patches within each bin for other factors that strongly correlate with the measured performance. We find that in the typical natural image amplitude thresholds vary by approximately two orders of magnitude. Further, threshold amplitude is a linear function of mean luminance (Webera??s law for luminance), threshold power is a linear function of background contrast power (Webera??s law for contrast), and threshold amplitude increases linearly with similarity once above a base level of similarity. We also find that the three dimensions combine systematically, in a fashion consistent with a mixture of separable and additive interactions. Finally, we identified another dimension, a??contrast-contrasta??, that explains some of the residual variance in the thresholds: all else being equal, thresholds tend to be lower with higher variation of contrast within a patch. We argue that the results may form the foundation for a general model of detection in natural scenes. We also argue that this direct experimental approach should be applicable to other natural tasks, if a sufficiently large set of natural stimuli can be obtained.
机译:视觉科学的最终目标是了解自然条件下的性能。我们描述了一种直接的实验方法,用于识别和量化影响自然场景中检测性能的因素。已校准的自然图像的大量集合被分成数百万个背景色块,然后沿着感兴趣的维度被分类到狭窄的条带中。在本研究中,每个bin代表特定的(狭窄范围)平均亮度,对比度和相似性(背景与目标的相位无关的相关性)。接下来,针对跨整个空间的稀疏bin子集,以参数方式测量检测阈值。每个垃圾箱的心理功能是通过从该垃圾箱中随机采样背景补丁(而不进行替换)来测量的。最后,我们针对与测量的性能密切相关的其他因素,分析了每个仓中背景色块的残留变化。我们发现在典型的自然图像中,振幅阈值相差大约两个数量级。此外,阈值幅度是平均亮度的线性函数(亮度的韦伯拉定律),阈值功效是背景对比度功率的线性函数(对比度的韦伯拉定律),并且阈值幅度一旦超过则线性地增加。基本相似度。我们还发现,这三个维度以可分离和累加相互作用的混合物一致的方式系统地组合在一起。最后,我们确定了另一个维度“对比度对比”,它可以解释阈值中的一些残余方差:在所有其他条件相同的情况下,阈值往往会较低,而贴片内的对比度变化也更大。我们认为结果可能构成自然场景中检测的通用模型的基础。我们还认为,如果可以获得足够多的自然刺激,则这种直接实验方法应适用于其他自然任务。

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