Abstract In this paper we consider the multiple line detection problem by using the center-based clustering approach, and propose a new incremental method based on iterative reweighted approach. We prove the convergence theorem and construct an appropriate algorithm which we test on numerous artificial data sets. A stopping criterion in the algorithm is defined by using the parameters from the DBSCAN algorithm. We give necessary conditions for the most appropriate partition, which have been used during elimination of unacceptable center-lines that appear in the output of the algorithm. The algorithm is also illustrated on a real-world image coming from Precision Agriculture.

中文翻译：

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多线检测问题的增量方法——迭代重加权方法

摘要 本文采用基于中心的聚类方法考虑多线检测问题，提出了一种基于迭代重加权方法的增量方法。我们证明了收敛定理并构建了一个适当的算法，我们在大量人工数据集上进行测试。算法中的停止标准是通过使用来自 DBSCAN 算法的参数来定义的。我们给出了最合适的分区的必要条件，这些条件已用于消除出现在算法输出中的不可接受的中心线。该算法还在来自精准农业的真实世界图像上进行了说明。