Contour statistics in natural images: grouping across occlusions.
Contour statistics in natural images: grouping across occlusions.
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DOI：
10.1017/s0952523808080875
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发表时间：
2009-01
影响因子：
1.9
通讯作者：
Perry, Jeffrey S.
中科院分区：
文献类型：
--
作者：
Geisler, Wilson S.;Perry, Jeffrey S.
正确解释自然图像需要正确处理遮挡的影响，因此跨遮挡的轮廓分组是许多自然视觉任务的主要组成部分。为了更好地理解跨遮挡的轮廓分组机制，我们（a）测量了自然图像中轮廓的边缘元素的成对统计数据，作为边缘元素几何和对比极性的函数，（b）导出了轮廓遮挡任务的理想贝叶斯观察者，其中直接从自然图像中提取刺激，然后（c）测量了同一轮廓遮挡任务中的人类表现。除了发现自然轮廓的新统计特性之外，我们还发现天真的人类观察者在轮廓遮挡任务中的理想表现非常相似。事实上，在四维刺激空间（3 个几何维度和 1 个对比维度）的任何区域中，人类的表现与理想观察者的表现不完全一致（即，效率在整个空间内近似恒定）。这些结果拒绝了许多其他轮廓分组假设，并强烈表明轮廓分组的神经机制与自然图像中轮廓的统计特性密切相关。
Correctly interpreting a natural image requires dealing properly with the effects of occlusion, and hence contour grouping across occlusions is a major component of many natural visual tasks. To better understand the mechanisms of contour grouping across occlusions we (a) measured the pair-wise statistics of edge elements from contours in natural images, as a function of edge-element geometry and contrast polarity, (b) derived the ideal Bayesian observer for a contour occlusion task where the stimuli were extracted directly from natural images, and then (c) measured human performance in the same contour occlusion task. In addition to discovering new statistical properties of natural contours, we found that naïve human observers closely parallel ideal performance in our contour occlusion task. In fact, there was no region of the four-dimensional stimulus space (3 geometry dimensions and 1 contrast dimension) where humans did not closely parallel the performance of the ideal observer (i.e., efficiency was approximately constant over the entire space). These results reject many other contour grouping hypotheses and strongly suggest that the neural mechanisms of contour grouping are tightly related to the statistical properties of contours in natural images.