Region grouping in natural foliage scenes: image statistics and human performance.
Region grouping in natural foliage scenes: image statistics and human performance.
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DOI：
10.1167/10.4.10
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发表时间：
2010-04-27
影响因子：
1.8
通讯作者：
Geisler WS
中科院分区：
文献类型：
--
作者：
Ing AD;Wilson JA;Geisler WS
近距离树叶是人类和非人类灵长类动物的一类重要场景，本研究探讨了近距离树叶在自然场景中的聚集和分离机制。近距离的树叶图像是用数码相机采集的，该相机经过校准，以匹配人类L、M和S视锥在每个像素上的反应。这些图像被用来构建一个手工分割的树叶和树枝的数据库，该数据库正确地定位了每个对象所覆盖的图像区域。我们考虑了这样一个任务，其中向视觉系统呈现两个图像块，并被要求根据这些块看起来位于同一表面还是不同表面上来分配类别标签(相同或不同)。我们为该任务估计了几个近似理想的分类器，每个分类器使用一组唯一的图像属性。在所考虑的图像属性中，我们发现理想的分类器主要取决于斑块之间的平均强度和颜色的差异，其次是斑块之间的对比度的差异。在心理物理实验中，人类的表现反映了理想分类器预测的趋势。在没有纠正反馈的初始阶段，人类的准确率略低于理想水平。经过反馈练习后，人类的准确率几乎是理想的。
This study investigated the mechanisms of grouping and segregation in natural scenes of close-up foliage, an important class of scenes for human and non-human primates. Close-up foliage images were collected with a digital camera calibrated to match the responses of human L, M, and S cones at each pixel. The images were used to construct a database of hand-segmented leaves and branches that correctly localizes the image region subtended by each object. We considered a task where a visual system is presented with two image patches and is asked to assign a category label (either same or different) depending on whether the patches appear to lie on the same surface or different surfaces. We estimated several approximately ideal classifiers for the task, each of which used a unique set of image properties. Of the image properties considered, we found that ideal classifiers rely primarily on the difference in average intensity and color between patches, and secondarily on the differences in the contrasts between patches. In psychophysical experiments, human performance mirrored the trends predicted by the ideal classifiers. In an initial phase without corrective feedback, human accuracy was slightly below ideal. After practice with feedback, human accuracy was approximately ideal.
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影响因子：
1.9
作者：
Geisler, Wilson S.;Perry, Jeffrey S.
通讯作者：
Perry, Jeffrey S.
影响因子：
1.8
作者：
Balboa, RM;Grzywacz, NM
通讯作者：
Grzywacz, NM
影响因子：
2.5
作者：
Geisler, WS;Diehl, RL
通讯作者：
Diehl, RL
影响因子：
1.8
作者：
Fowlkes, Charless C.;Martin, David R.;Malik, Jitendra
通讯作者：
Malik, Jitendra
影响因子：
--
作者：
PARZEN, E
通讯作者：
PARZEN, E