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Edge integration and image segmentation in lightness and color

机译:亮度和色彩的边缘整合和图像分割

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Previous work has demonstrated that lightness in disk-annulus displays can be explained by a computational model based on the principle of edge integration (Rudd, Front. Hum. Neurosci., 2014). The disk lightness depends not just on the contrast, or luminance ratio, of the disk with respect to its immediate surround, but instead on a weighted sum of luminance steps (in log units) computed along a path from the background, through annulus, to the disk. The idea of summing steps in log luminance to compute lightness makes ecological sense as a means of relating the disk luminance to the background luminance for the purpose of constructing a global reflectance map, starting from information about local changes in luminance that might be encoded by early visual neurons (Rudd, 2014; J. Electron. Imaging, submitted). Edge integration theory explains disk-annulus lightness matching results with great precision, but can the theory generalize to explain surface lightness and color appearance in other contexts? Here I shown how it can account for assimilation in the classic Helson display, and in a new display--the Nebraska election map--in which the yellow color of hatched diagonal lines draw within an otherwise red region bleed into the red. I also present displays that I have constructed by modifying White's display. In these displays, edge integration operates within regions that are first visually segmented according to principles of perceptual organization. The phenomenology of my modified White's displays supports the conclusion that steps in log luminance sum to compute target lightness only within the segmented region to which the target perceptually belongs, suggesting a role of perceptual grouping or scission in depth in White's effect. My new results cannot be explained either by low-level normalized filter models, such as ODOG, or by an edge integration theory that ignores the role of image segmentation.
机译:先前的工作表明,圆盘环形显示器的亮度可以通过基于边缘积分原理的计算模型来解释(Rudd,Front。Hum。Neurosci。,2014)。圆盘的亮度不仅取决于圆盘相对于其直接周围的对比度或亮度比,还取决于沿从背景到环形空间到圆环的路径计算的亮度步长的加权总和(以对数为单位)。磁盘。汇总对数亮度的步骤以计算亮度的想法具有生态意义,因为它是将光盘亮度与背景亮度相关联的一种方法,目的是构建全局反射率图,该方法从有关亮度局部变化的信息开始,这些信息可能由早期编码而来。视觉神经元(Rudd,2014; J.Electron.Imaging,提交)。边缘积分理论可以非常精确地解释圆盘环的亮度匹配结果,但是该理论可以推广到其他背景下的表面亮度和颜色外观吗?在这里,我展示了它如何在经典的Helson显示器中以及在新的显示器(内布拉斯加州选举地图)中解释同化,其中阴影线的对角线的黄色绘制在原本为红色的区域中,然后变成红色。我还介绍了通过修改White的显示构造的显示。在这些显示器中,边缘整合在根据感知组织原理首先进行视觉分割的区域内运行。我修改过的White显示器的现象学支持以下结论:对数亮度和的阶跃仅在目标感知到的分段区域内计算目标亮度,这暗示了感知分组或深度分裂在White效应中的作用。我的新结果无法通过低级归一化滤波器模型(例如ODOG)或无法忽略图像分割作用的边缘积分理论来解释。

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