Repeated patterns (such as windows, balconies, and doors) are prominent and significant features in urban scenes. Therefore, detection of these repeated patterns becomes very important for city scene analysis. This paper attacks the problem of repeated pattern detection in a precise, efficient and automatic way, by combining traditional feature extraction with a Kronecker product based low-rank model. We introduced novel algorithms that extract repeated patterns from rectified images with solid theoretical support. Our method is tailored for 2D images of building façades and tested on a large set of façade images.

中文翻译：

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用于在2D城市图像上进行重复模式检测的Kronecker产品模型

重复的图案（例如窗户，阳台和门）是城市场景中的突出和重要特征。因此，这些重复模式的检测对于城市场景分析变得非常重要。本文通过将传统特征提取与基于Kronecker产品的低秩模型相结合，以精确，高效和自动的方式解决了重复模式检测的问题。我们引入了新颖的算法，这些算法在可靠的理论支持下从校正后的图像中提取出重复的图案。我们的方法专为建筑立面的2D图像量身定制，并在大量立面图像上进行了测试。