We describe a method for symbol spotting in born-digital architectural floor plans, which are a particularly challenging application domain of graphics recognition. We propose a hybrid method that capitalizes on strengths of both vector-based and pixel-based symbol spotting techniques. In the description phase, the salient geometric constituents of a symbol are extracted by a variety of vectorization techniques, including a proposed voting-based algorithm for finding partial ellipses. This enables us to better handle local shape irregularities and boundary discontinuities, as well as partial occlusion and overlap. In the matching phase, the spatial relationship between the geometric primitives is encoded via a primitive-aware proximity graph. A statistical approach is then used to rapidly yield a coarse localization of symbols within the plan. Localization is further refined with a pixel-based step implementing a modified cross-correlation function. Experimental results on the public SESYD synthetic dataset demonstrate that our approach clearly outperforms other popular symbol spotting approaches. Moreover, our approach yields promising results on real-world images, which are significantly more challenging in terms of overlap, occlusion, and presence of non-symbolic graphical data.

中文翻译：

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原生数字建筑平面图中基于几何的符号定位

我们描述了一种在原生数字建筑平面图中进行符号识别的方法，这是图形识别的一个特别具有挑战性的应用领域。我们提出了一种混合方法，它利用了基于矢量和基于像素的符号识别技术的优势。在描述阶段，符号的显着几何成分通过各种矢量化技术提取，包括提出的用于寻找部分椭圆的基于投票的算法。这使我们能够更好地处理局部形状不规则和边界不连续，以及部分遮挡和重叠。在匹配阶段，几何图元之间的空间关系通过图元感知邻近图进行编码。然后使用统计方法快速生成计划内符号的粗略定位。定位通过基于像素的步骤进一步细化，实现了修改后的互相关函数。在公共 SESYD 合成数据集上的实验结果表明，我们的方法明显优于其他流行的符号识别方法。此外，我们的方法在真实世界的图像上产生了有希望的结果，这在重叠、遮挡和非符号图形数据的存在方面更具挑战性。