Computational Perception Laboratory & Department of Psychology, Florida Gulf Coast University, Whitaker Hall Room 215, 10501 FGCU Blvd S., Fort Myers, FL, 33965-6565, USA.
McGill Vision Research Unit, Department of Ophthalmology and Visual Sciences, McGill University, Montreal, QC, H3G1A4, Canada.
Sci Rep. 2021 May 12;11(1):10074. doi: 10.1038/s41598-021-89277-2.
Segmenting scenes into distinct surfaces is a basic visual perception task, and luminance differences between adjacent surfaces often provide an important segmentation cue. However, mean luminance differences between two surfaces may exist without any sharp change in albedo at their boundary, but rather from differences in the proportion of small light and dark areas within each surface, e.g. texture elements, which we refer to as a luminance texture boundary. Here we investigate the performance of human observers segmenting luminance texture boundaries. We demonstrate that a simple model involving a single stage of filtering cannot explain observer performance, unless it incorporates contrast normalization. Performing additional experiments in which observers segment luminance texture boundaries while ignoring super-imposed luminance step boundaries, we demonstrate that the one-stage model, even with contrast normalization, cannot explain performance. We then present a Filter-Rectify-Filter model positing two cascaded stages of filtering, which fits our data well, and explains observers' ability to segment luminance texture boundary stimuli in the presence of interfering luminance step boundaries. We propose that such computations may be useful for boundary segmentation in natural scenes, where shadows often give rise to luminance step edges which do not correspond to surface boundaries.
将场景分割成不同的表面是一项基本的视觉感知任务，相邻表面之间的亮度差异通常提供了一个重要的分割线索。然而，即使在两个表面的交界处没有明显的反照率变化，也可能存在两个表面之间的平均亮度差异，这是由于每个表面内小的亮区和暗区的比例不同，例如纹理元素，我们称之为亮度纹理边界。在这里，我们研究了人类观察者分割亮度纹理边界的性能。我们证明，除非包含对比度归一化，否则仅涉及单个滤波阶段的简单模型无法解释观察者的性能。在执行额外的实验中，观察者在忽略叠加的亮度阶跃边界的情况下分割亮度纹理边界，我们证明，即使具有对比度归一化，单阶段模型也无法解释性能。然后，我们提出了一个滤波-修正-滤波模型，假设存在两个级联的滤波阶段，该模型很好地拟合了我们的数据，并解释了观察者在存在干扰亮度阶跃边界的情况下分割亮度纹理边界刺激的能力。我们提出，这样的计算对于自然场景中的边界分割可能是有用的，因为阴影通常会产生与表面边界不对应的亮度阶跃边缘。
