Edge co-occurrence in natural images predicts contour grouping performance
Edge co-occurrence in natural images predicts contour grouping performance
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DOI：
10.1016/s0042-6989(00)00277-7
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发表时间：
2001-03-01
期刊：
影响因子：
1.8
通讯作者：
Gallogly, DP
中科院分区：
文献类型：
--
作者：
Geisler, WS;Perry, JS;Gallogly, DP
人类大脑能够正确地解释它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，适当地链接到全局轮廓的局部边缘元素。虽然已经出现了关于大脑如何实现有效轮廓分组的一般观点，但是已经出现了许多不同的具体建议，并且在定量预测性能方面很少成功。这些先前的建议主要是通过直觉和计算试验和错误发展起来的。一种更有原则的方法是开始检查自然图像中存在的轮廓的统计特性，因为正是这些统计数据推动了分组机制的演变。在这里，我们报告测量的绝对和贝叶斯边缘共现统计在自然图像中，以及人类的性能检测自然形状的轮廓在复杂的背景。我们发现，轮廓检测性能是定量预测的本地分组规则直接来自同现统计，结合一个非常简单的集成规则（传递性规则），链接到更长的轮廓本地分组的轮廓元素。(C)2001爱思唯尔科技有限公司版权所有。
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, then 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. (C) 2001 Elsevier Science Ltd. All rights reserved.