Department of Information and Communications Engineering, Tokyo Institute of Technology, Nagatsuta-cho, Midori-ku, Yokohama, Japan.
J Vis. 2023 Mar 1;23(3):1. doi: 10.1167/jov.23.3.1.
The human visual system estimates the physical properties of objects, such as their lightness. Previous studies on the lightness perception of glossy three-dimensional objects have suggested that specular highlights are detected and excluded in lightness perception. However, only a few studies have attempted to elucidate the mechanisms underlying this exclusion. This study aimed to elucidate the image features that contribute to the highlight exclusion of lightness perception. We used Portilla-Simoncelli texture statistics (PS statistics), an image feature set similar to the representation in the early visual cortex, to explore their relationships with highlight exclusion for lightness perception. In experiment 1, computer graphics images of bumpy plastic plates with various physical parameters were used as stimuli, and the lightness perception on them was measured using a lightness matching task. We then calculated the highlight exclusion index, which represented the degree of highlight exclusion. Finally, we evaluated the correlation between the highlight exclusion index and the four PS statistic subsets. In experiment 2, an image synthesis algorithm was used to create images in which either the PS statistic subset was manipulated. The highlight exclusion indexes of the synthesized images were then measured. The results revealed that the PS statistic subset consisting of lowest-order image features, such as moment statistics of luminance, acts as a necessary condition for highlight exclusion, whereas the other three subsets consisting of higher order features are not crucial. These results suggest that the low-order image features are the most important among the features in PS statistics for highlight exclusion, even though image features higher order than those in PS statistics must be directly involved.
人类视觉系统估计物体的物理属性，例如它们的明度。先前关于有光泽的三维物体明度感知的研究表明，镜面反射高光会在明度感知中被检测和排除。然而，只有少数研究试图阐明这种排除的机制。本研究旨在阐明有助于明度感知高光排除的图像特征。我们使用 Portilla-Simoncelli 纹理统计（PS 统计），一种类似于早期视觉皮层中表示的图像特征集，来探索它们与明度感知高光排除的关系。在实验 1 中，我们使用具有各种物理参数的凹凸塑料板的计算机图形图像作为刺激，并使用明度匹配任务来测量它们的明度感知。然后，我们计算了高光排除指数，该指数代表高光排除的程度。最后，我们评估了高光排除指数与四个 PS 统计子集之间的相关性。在实验 2 中，我们使用图像合成算法来创建 PS 统计子集被操纵的图像。然后测量合成图像的高光排除指数。结果表明，由亮度的矩统计等最低阶图像特征组成的 PS 统计子集是高光排除的必要条件，而由更高阶特征组成的其他三个子集则不是至关重要的。这些结果表明，在 PS 统计中，低阶图像特征是高光排除最重要的特征，即使 PS 统计中更高阶的图像特征必须直接参与。
