Geisler W S, Perry J S, Super B J, Gallogly D P
Department of Psychology, University of Texas at Austin, Austin, TX 78712, USA.
Vision Res. 2001 Mar;41(6):711-24. doi: 10.1016/s0042-6989(00)00277-7.
The human brain manages to correctly interpret almost every visual image it receives from the environment. Underlying this ability are contour grouping mechanisms that appropriately link local edge elements into global contours. Although a general view of how the brain achieves effective contour grouping has emerged, there have been a number of different specific proposals and few successes at quantitatively predicting performance. These previous proposals have been developed largely by intuition and computational trial and error. A more principled approach is to begin with an examination of the statistical properties of contours that exist in natural images, because it is these statistics that drove the evolution of the grouping mechanisms. Here we report measurements of both absolute and Bayesian edge co-occurrence statistics in natural images, as well as human performance for detecting natural-shaped contours in complex backgrounds. We find that contour detection performance is quantitatively predicted by a local grouping rule derived directly from the co-occurrence statistics, in combination with a very simple integration rule (a transitivity rule) that links the locally grouped contour elements into longer contours.
人类大脑能够正确解读其从环境中接收到的几乎每一个视觉图像。这种能力的基础是轮廓分组机制，该机制能将局部边缘元素适当地链接成全局轮廓。尽管关于大脑如何实现有效轮廓分组已形成了一个总体观点，但仍有许多不同的具体提议，且在定量预测性能方面鲜有成功案例。这些先前的提议很大程度上是通过直觉和计算试错发展而来的。一种更具原则性的方法是首先研究自然图像中存在的轮廓的统计特性，因为正是这些统计特性推动了分组机制的进化。在此，我们报告了自然图像中绝对和贝叶斯边缘共现统计的测量结果，以及人类在复杂背景中检测自然形状轮廓的性能。我们发现，轮廓检测性能可通过直接从共现统计中导出的局部分组规则，结合一个非常简单的积分规则（传递性规则）进行定量预测，该积分规则将局部分组的轮廓元素链接成更长的轮廓。
