What characterizes a shadow boundary under the sun and sky?

Xiang Huang, G. Hua, J. Tumblin, Lance Williams
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引用次数: 78

Abstract

Despite decades of study, robust shadow detection remains difficult, especially within a single color image. We describe a new approach to detect shadow boundaries in images of outdoor scenes lit only by the sun and sky. The method first extracts visual features of candidate edges that are motivated by physical models of illumination and occluders. We feed these features into a Support Vector Machine (SVM) that was trained to discriminate between most-likely shadow-edge candidates and less-likely ones. Finally, we connect edges to help reject non-shadow edge candidates, and to encourage closed, connected shadow boundaries. On benchmark shadow-edge data sets from Lalonde et al. and Zhu et al., our method showed substantial improvements when compared to other recent shadow-detection methods based on statistical learning.
太阳和天空下的阴影边界的特征是什么?
尽管几十年的研究,强大的阴影检测仍然困难,特别是在单色图像。我们描述了一种新的方法来检测仅由太阳和天空照亮的户外场景图像中的阴影边界。该方法首先提取候选边缘的视觉特征,这些特征是由照明和遮挡物的物理模型驱动的。我们将这些特征输入到支持向量机(SVM)中,该支持向量机被训练来区分最可能的阴影边缘候选和不太可能的候选。最后,我们连接边缘以帮助拒绝非阴影边缘候选,并鼓励封闭的,连接的阴影边界。在Lalonde等人和Zhu等人的基准阴影边缘数据集上,与最近基于统计学习的其他阴影检测方法相比,我们的方法有了实质性的改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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