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Depth perception in autostereograms: 1/f noise is best

机译:自动立体图中的深度感知:1 / f噪声最佳

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摘要

An autostereogram is a single image that encodes depth information that pops out when looking at it. The trick is achieved by setting a basic 2D pattern and continuously replicating the local pattern at each point in the image with a shift defined by the desired disparity. In this work, we explore the dependency between the ease of perceiving depth in autostereograms and the choice of the basic pattern used for generating them. We report the results of three sets of psychophysical experiments using autostereograms generated from 2D random noise patterns having power spectra of the form 1/f(beta). The experiments were designed to test the ability of human subjects to identify smooth low-resolution surfaces, as well as detail, in the form of higher-resolution objects in the depth profile, and to determine limits in identifying small objects as a function of their size. In accordance with the theory, we discover a significant advantage of the 1/f noise pattern (pink noise) for fast depth lock-in and fine detail detection, showing that such patterns are optimal choices for autostereogram design. (C) 2016 Optical Society of America
机译:自动立体图是对深度信息进行编码的单个图像,该深度信息在观看时会弹出。通过设置基本的2D图案并在图像的每个点上连续复制局部图案(具有由所需视差定义的移动)来实现技巧。在这项工作中,我们探索了自动立体图中感知深度的难易程度与用于生成它们的基本模式的选择之间的依赖性。我们报告了使用自动立体图的三组心理物理实验的结果,这些立体图由具有1 /fβ形式的功率谱的2D随机噪声模式生成。设计这些实验的目的是测试人类对象识别深度轮廓中高分辨率对象形式的平滑低分辨率表面和细节的能力,并确定根据它们的功能识别小对象的限制尺寸。根据该理论,我们发现了1 / f噪声模式(粉红色噪声)对于快速深度锁定和精细细节检测的显着优势,表明此类模式是自动立体图设计的最佳选择。 (C)2016美国眼镜学会

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