Zeiner Katharina, Maertens Marianne
Modeling of Cognitive Processes Group, Department of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, Berlin, Germany.
J Vis. 2014 Jun 3;14(7):3. doi: 10.1167/14.7.3.
In the present experiment we addressed the question of how the visual system determines surface lightness from luminances in the retinal image. We measured the perceived lightness of target surfaces that were embedded in custom-made checkerboards. The checkerboards consisted of 10 by 10 checks of 10 different reflectance values that were arranged randomly across the board. They were rendered under six viewing conditions including plain view, with a shadow-casting cylinder, or with one of four different transparent media covering part of the board. For each reflectance we measured its corresponding luminance in the different viewing conditions. We then assessed the lightness matches of four observers for each of the reflectances in the different viewing conditions. We derived predictions of perceived lightness based on local luminance, Michelson contrast, edge integration, anchoring theory, and a normalized Michelson contrast measure. The normalized contrast measure was the best predictor of surface lightness and was almost as good as the actual reflectance values. The normalized contrast measure combines a local computation of Michelson contrast with a region-based normalization of contrast ranges with respect to the contrast range in plain view. How the segregation of image regions is accomplished remains to be elucidated.
在本实验中，我们探讨了视觉系统如何根据视网膜图像中的亮度来确定表面明度这一问题。我们测量了嵌入定制棋盘格中的目标表面的感知明度。棋盘格由10×10个方格组成，这些方格具有10种不同的反射率值，随机分布在整个棋盘上。它们在六种观察条件下呈现，包括平视、有一个投射阴影的圆柱体，或用四种不同的透明介质之一覆盖棋盘的一部分。对于每种反射率，我们测量了其在不同观察条件下对应的亮度。然后，我们评估了四位观察者在不同观察条件下对每种反射率的明度匹配情况。我们基于局部亮度、迈克尔逊对比度、边缘整合、锚定理论以及一种归一化的迈克尔逊对比度度量得出了感知明度的预测值。归一化对比度度量是表面明度的最佳预测指标，几乎与实际反射率值一样好。归一化对比度度量将迈克尔逊对比度的局部计算与基于区域的对比度范围归一化相结合，该归一化是相对于平视时的对比度范围而言的。图像区域的分离是如何实现的仍有待阐明。
