Victor Jonathan D, Conte Mary M
Department of Neurology and Neuroscience, Weill Cornell Medical College, New York, New York 10065, USA.
J Opt Soc Am A Opt Image Sci Vis. 2012 Jul 1;29(7):1313-45. doi: 10.1364/JOSAA.29.001313.
The space of visual signals is high-dimensional and natural visual images have a highly complex statistical structure. While many studies suggest that only a limited number of image statistics are used for perceptual judgments, a full understanding of visual function requires analysis not only of the impact of individual image statistics, but also, how they interact. In natural images, these statistical elements (luminance distributions, correlations of low and high order, edges, occlusions, etc.) are intermixed, and their effects are difficult to disentangle. Thus, there is a need for construction of stimuli in which one or more statistical elements are introduced in a controlled fashion, so that their individual and joint contributions can be analyzed. With this as motivation, we present algorithms to construct synthetic images in which local image statistics--including luminance distributions, pair-wise correlations, and higher-order correlations--are explicitly specified and all other statistics are determined implicitly by maximum-entropy. We then apply this approach to measure the sensitivity of the human visual system to local image statistics and to sample their interactions.
视觉信号空间是高维的，自然视觉图像具有高度复杂的统计结构。虽然许多研究表明，只有有限数量的图像统计信息用于感知判断，但要全面理解视觉功能，不仅需要分析单个图像统计信息的影响，还需要分析它们之间的相互作用方式。在自然图像中，这些统计元素（亮度分布、低阶和高阶相关性、边缘、遮挡等）相互交织，其效果难以区分。因此，需要构建一种刺激，其中以可控方式引入一个或多个统计元素，以便能够分析它们的个体和联合贡献。基于此动机，我们提出了构建合成图像的算法，其中局部图像统计信息——包括亮度分布、成对相关性和高阶相关性——被明确指定，而所有其他统计信息则通过最大熵隐式确定。然后，我们应用这种方法来测量人类视觉系统对局部图像统计信息的敏感度，并对它们的相互作用进行采样。
