Theory of texture discrimination of based on higher-order perturbations in individual texture samples.
Theory of texture discrimination of based on higher-order perturbations in individual texture samples.
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基于个体纹理样本的高阶扰动的纹理辨别理论。
DOI：
10.1016/j.visres.2004.03.029
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发表时间：
2004
期刊：
影响因子：
--
通讯作者：
Tyler,ChristopherW
中科院分区：
文献类型：
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作者：
Tyler,ChristopherW
这种分析解决了这个问题，纹理属性的定义上可能的纹理合奏，而心理物理的纹理属性的判断必须对个别纹理样本，或在一个更大的纹理领域内的均匀纹理区域。由于基本的判别任务需要比较由不同的集合规则指定的两个样本图像（或区域），因此需要观察者比较它们的单个纹理的集合统计的估计。本文开发了一种纹理歧视理论，结合一个流动的本地采样窗口，允许视觉系统从任何特定的纹理图像的窗口，而不需要提出多个样本进行评估，以获得估计的合奏统计。这种纹理方法解释了我们如何能够清楚地感觉到两种模式来自不同的统计生成规则，即使我们只看到每种类型的一个例子。在提供理论基础的纹理歧视的个别样品，这种分析超越了以前的工作，占我们的直觉，我们可以估计特定纹理的生成规则。它还分析了在扩展图像中区分纹理边界的决策过程，定义了一种新的“格里高利吸引子”，取代和扩展了标准的贝叶斯决策规则。
This analysis addresses the issue that texture properties are defined on ensembles of possible textures, while psychophysical judgments of texture properties must be made on individual texture samples, or regions of uniform texture within a larger texture field. Since the basic discrimination task requires comparison of two sample images (or regions) specified by different ensemble rules, the viewer is thus required to compare the estimates of their ensemble statistics of single textures. This paper develops a theory of texture discrimination incorporating a roving local sampling window that allows the visual system to derive an estimate of the ensemble statistics over the window from any particular texture image, without the need to present multiple samples for evaluation. This approach to texture explains how we can have a clear sense that two patterns derive from different statistical generation rules even though we see only one example of each type. In providing the theoretical basis for texture discrimination of individual samples, this analysis goes beyond previous work to account for our intuitive sense that we can estimate the generation rule underlying particular textures. It also analyzes the decision process for discriminating texture boundaries in extended images, defining a novel “Gregorian attractor” that replaces and extends standard Bayesian decision rules.