A Bayesian model of lightness perception that incorporates spatial variation in the illumination
A Bayesian model of lightness perception that incorporates spatial variation in the illumination
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DOI：
10.1167/13.7.18
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发表时间：
2013-01-01
影响因子：
1.8
通讯作者：
Brainard, David H.
中科院分区：
文献类型：
--
作者：
Allred, Sarah R.;Brainard, David H.
测试刺激的亮度以复杂的方式取决于观看它的环境。为了预测亮度，有必要利用对可行数量的上下文配置的测量来预测更广泛的配置。在这里，我们利用亮度源于视觉系统试图提供有关物体表面反射率的稳定信息的想法来追求这一目标。我们开发了一种贝叶斯算法，可以根据图像亮度估计照度和反射率，并将感知亮度与算法对表面反射率的估计联系起来。该算法通过应用指定查看场景中可能出现的照明和表面反射率的先验来解决图像中的模糊性。选择先验分布是为了允许照明和表面反射率的空间变化。为了评估我们的模型，我们将其预测与嵌入消色差棋盘中的测试色块的感知亮度判断数据集进行了比较（Allred、Radonjic、Gilchrist 和 Brainard，2012）。棋盘刺激包含了亮度的巨大变化，这是自然场景的普遍特征。此外，系统地控制了靠近和远离中心测试斑块的检查的亮度分布。这些操作提供了照明空间变化的简化版本。该模型可以解释图像亮度整体变化的影响以及此类变化对空间位置的依赖性以及数据的一些但不是全部更详细的特征。
The lightness of a test stimulus depends in a complex manner on the context in which it is viewed. To predict lightness, it is necessary to leverage measurements of a feasible number of contextual configurations into predictions for a wider range of configurations. Here we pursue this goal, using the idea that lightness results from the visual system's attempt to provide stable information about object surface reflectance. We develop a Bayesian algorithm that estimates both illumination and reflectance from image luminance, and link perceived lightness to the algorithm's estimates of surface reflectance. The algorithm resolves ambiguity in the image through the application of priors that specify what illumination and surface reflectances are likely to occur in viewed scenes. The prior distributions were chosen to allow spatial variation in both illumination and surface reflectance. To evaluate our model, we compared its predictions to a data set of judgments of perceived lightness of test patches embedded in achromatic checkerboards (Allred, Radonjic, Gilchrist, & Brainard, 2012). The checkerboard stimuli incorporated the large variation in luminance that is a pervasive feature of natural scenes. In addition, the luminance profile of the checks both near to and remote from the central test patches was systematically manipulated. The manipulations provided a simplified version of spatial variation in illumination. The model can account for effects of overall changes in image luminance and the dependence of such changes on spatial location as well as some but not all of the more detailed features of the data.