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Paired Regions for Shadow Detection and Removal

机译:配对区域用于阴影检测和去除

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In this paper, we address the problem of shadow detection and removal from single images of natural scenes. Differently from traditional methods that explore pixel or edge information, we employ a region-based approach. In addition to considering individual regions separately, we predict relative illumination conditions between segmented regions from their appearances and perform pairwise classification based on such information. Classification results are used to build a graph of segments, and graph-cut is used to solve the labeling of shadow and nonshadow regions. Detection results are later refined by image matting, and the shadow-free image is recovered by relighting each pixel based on our lighting model. We evaluate our method on the shadow detection dataset in Zhu et al. . In addition, we created a new dataset with shadow-free ground truth images, which provides a quantitative basis for evaluating shadow removal. We study the effectiveness of features for both unary and pairwise classification.
机译:在本文中,我们解决了从自然场景的单个图像中进行阴影检测和去除的问题。与探索像素或边缘信息的传统方法不同,我们采用基于区域的方法。除了单独考虑各个区域外,我们还根据其外观预测分段区域之间的相对照明条件,并根据此类信息执行成对分类。分类结果用于构建线段图,图切割用于解决阴影区域和非阴影区域的标签。随后通过图像消光完善检测结果,并根据我们的光照模型对每个像素重新光照,以恢复无阴影图像。我们在Zhu等人的阴影检测数据集上评估了我们的方法。 。此外,我们使用无阴影的地面真实图像创建了一个新的数据集,为评估阴影去除提供了定量基础。我们研究了一元和成对分类特征的有效性。

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