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An efficient and robust line segment matching approach based on LBD descriptor and pairwise geometric consistency

机译:基于LBD描述符和成对几何一致性的高效鲁棒线段匹配方法

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摘要

We present a line matching algorithm which utilizes both the local appearance of lines and their geometric attributes. To overcome the problem of segment fragmentation and geometric variation, we extract lines in the scale space. To depict the local appearance of lines, we design a novel line descriptor called Line Band Descriptor (LBD). To evaluate the pairwise geometric consistency, we define the pairwise geometric attributes between line pairs. Then we built a relational graph for candidate line matches and employ a spectral technique to solve this matching problem efficiently. The advantages of the proposed algorithm are as follows: (1) it is robust to image transformations because of the multi-scale line detection strategy; (2) it is efficient because the designed LBD descriptor is fast to compute and the appearance similarities reduce the dimension of the graph matching problem; (3) it is accurate even for low-texture images because of the pairwise geometric consistency evaluation.
机译:我们提出一种线匹配算法,该算法利用线的局部外观及其几何属性。为了克服分段和几何变化的问题,我们在比例空间中提取线。为了描述线条的局部外观,我们设计了一种新颖的线条描述符,称为“线条带描述符”(LBD)。为了评估成对的几何一致性,我们定义线对之间的成对几何属性。然后我们建立了候选线匹配的关系图,并采用频谱技术有效地解决了该匹配问题。该算法的优点如下:(1)由于采用多尺度线检测策略,对图像变换具有鲁棒性; (2)之所以有效,是因为设计的LBD描述符计算速度快,并且外观相似性降低了图匹配问题的维数; (3)由于成对的几何一致性评估,它甚至对于低纹理图像也是准确的。

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