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Color contributes to object-contour perception in natural scenes

机译:颜色有助于自然场景中物体轮廓的感知

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The magnitudes of chromatic and achromatic edge contrast are statistically independent and thus provide independent information, which can be used for object-contour perception. However, it is unclear if and how much object-contour perception benefits from chromatic edge contrast. To address this question, we investigated how well human-marked object contours can be predicted from achromatic and chromatic edge contrast. We used four data sets of human-marked object contours with a total of 824 images. We converted the images to the Derringtona??Krauskopfa??Lennie color space to separate chromatic from achromatic information in a physiologically meaningful way. Edges were detected in the three dimensions of the color space (one achromatic and two chromatic) and compared to human-marked object contours using receiver operating-characteristic (ROC) analysis for a threshold-independent evaluation. Performance was quantified by the difference of the area under the ROC curves (??AUC). Results were consistent across different data sets and edge-detection methods. If chromatic edges were used in addition to achromatic edges, predictions were better for 83% of the images, with a prediction advantage of 3.5% ??AUC, averaged across all data sets and edge detectors. For some images the prediction advantage was considerably higher, up to 52% ??AUC. Interestingly, if achromatic edges were used in addition to chromatic edges, the average prediction advantage was smaller (2.4% ??AUC). We interpret our results such that chromatic information is important for object-contour perception.
机译:彩色和消色差边缘对比度的大小在统计上是独立的,因此提供独立的信息,可用于对象轮廓感知。然而,尚不清楚彩色边缘对比度是否以及有多少对象轮廓感知受益。为了解决这个问题,我们研究了如何从消色差和色差边缘对比中很好地预测出人类标记的物体轮廓。我们使用了四个带有人类标记的对象轮廓的数据集,总共有824张图像。我们将图像转换为Derringtona ?? Krauskopfa ?? Lennie色彩空间,以生理学上有意义的方式将色度与消色差信息分开。在色彩空间的三个维度(一个消色差和两个色差)中检测到边缘,并使用接收器工作特性(ROC)分析与人类标记的对象轮廓进行比较,以进行独立于阈值的评估。通过ROC曲线下面积的差(ΔAUC)来量化性能。结果在不同的数据集和边缘检测方法之间是一致的。如果除消色差边缘外还使用彩色边缘,则对83%的图像进行更好的预测,其预测优势为3.5%AUC,在所有数据集和边缘检测器上平均。对于某些图像,预测优势要高得多,高达52%的AUC。有趣的是,如果除彩色边缘外还使用无彩色边缘,则平均预测优势较小(2.4%ΔAUC)。我们解释我们的结果,使得色度信息对于对象轮廓感知很重要。

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