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纹理稀疏性而非局部相位结构会损害二阶分割。

Texture sparseness, but not local phase structure, impairs second-order segmentation.

作者信息

Zavitz Elizabeth, Baker Curtis L

机构信息

McGill Vision Research, Department of Ophthalmology, McGill University, Montreal, Quebec, Canada; Department of Physiology, Monash University, Clayton, Victoria, Australia.

出版信息

Vision Res. 2013 Oct 18;91:45-55. doi: 10.1016/j.visres.2013.07.018. Epub 2013 Aug 11.

DOI:10.1016/j.visres.2013.07.018
PMID:23942289
Abstract

Texture boundary segmentation is typically thought to reflect a comparison of differences in Fourier energy (i.e. low-order texture statistics) on either side of a boundary. However in a previous study (Arsenault, Yoonessi, & Baker, 2011) we showed that the distribution of energy within a natural texture (i.e. its higher-order statistical structure) also influences segmentation of contrast boundaries. Here we examine the influence of specific higher-order texture statistics on segmentation of contrast- and orientation-defined boundaries. Using naturalistic synthetic textures to manipulate the sparseness, global phase structure, and local phase alignments of carrier textures, we measure segmentation thresholds based on forced-choice judgments of boundary orientation. We find a similar pattern of results for both contrast and orientation boundaries: (1) randomizing all structure by globally phase scrambling the texture reduces segmentation thresholds substantially, (2) decreasing sparseness also reduces thresholds, and (3) removing local phase alignments has little or no effect on segmentation thresholds. We show that a two-stage filter model with an intermediate compressive nonlinearity and expansive output nonlinearity can account for these data using synthetic textures. Furthermore, the model parameter fits obtained using synthetic textures also predict the segmentation thresholds presented in Arsenault, Yoonessi, and Baker (2011) for natural and phase-scrambled natural texture carriers.

摘要

纹理边界分割通常被认为反映了边界两侧傅里叶能量差异(即低阶纹理统计量)的比较。然而,在之前的一项研究中(Arsenault、Yoonessi和Baker,2011年),我们表明自然纹理中的能量分布(即其高阶统计结构)也会影响对比度边界的分割。在这里,我们研究特定高阶纹理统计量对对比度和方向定义边界分割的影响。使用自然主义的合成纹理来操纵载波纹理的稀疏性、全局相位结构和局部相位对齐,我们基于边界方向的强制选择判断来测量分割阈值。我们发现对比度和方向边界的结果模式相似:(1)通过全局相位扰乱纹理来随机化所有结构会大幅降低分割阈值,(2)降低稀疏性也会降低阈值,(3)去除局部相位对齐对分割阈值几乎没有影响。我们表明,一个具有中间压缩非线性和扩展输出非线性的两阶段滤波器模型可以使用合成纹理来解释这些数据。此外,使用合成纹理获得的模型参数拟合也预测了Arsenault、Yoonessi和Baker(2011年)中针对自然和相位扰乱自然纹理载波呈现的分割阈值。

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