Segmenting surface boundaries using luminance cues.
Segmenting surface boundaries using luminance cues.
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DOI：
10.1038/s41598-021-89277-2
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发表时间：
2021-05-12
影响因子：
4.6
通讯作者：
Baker CL Jr
中科院分区：
文献类型：
--
作者：
DiMattina C;Baker CL Jr
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重要的分割线索。然而，两个表面之间的平均亮度差异可能存在，而其边界处的反照率没有任何急剧变化，而是来自每个表面内的小亮区和暗区比例的差异，例如，纹理元素，我们将其称为亮度纹理边界。在这里，我们研究人类观察者分割亮度纹理边界的性能。我们证明，涉及单级过滤的简单模型无法解释观察者的性能，除非它包含对比度归一化。通过进行额外的实验，观察者分割亮度纹理边界，同时忽略叠加的亮度步长边界，我们证明单阶段模型即使具有对比度归一化，也无法解释性能。然后，我们提出了一个过滤器-校正-过滤器模型，该模型设置了两个级联的过滤阶段，这很好地适合我们的数据，并解释了观察者在存在干扰亮度步进边界的情况下分割亮度纹理边界刺激的能力。我们建议这种计算对于自然场景中的边界分割可能很有用，其中阴影通常会产生与表面边界不对应的亮度阶跃边缘。
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.
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