Mean shift based clustering of Hough domain for fast line segment detection
Mean shift based clustering of Hough domain for fast line segment detection
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DOI：
10.1016/j.patrec.2005.09.023
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发表时间：
2006-04-15
影响因子：
5.1
通讯作者：
Sandoval, F
中科院分区：
文献类型：
--
作者：
Bandera, A;Pérez-Lorenzo, JM;Sandoval, F
提出了一种从边缘图像中提取直线段的新算法。基本上，该方法执行两个连续的阶段。在第一阶段，该算法遵循线段随机窗口随机Hough变换（RWRHT）为基础的方法。这种方法提供了一种从全局角度寻找更有利的线段的机制。在我们的情况下，RWRHT为基础的方法是用来实现一个准确的霍夫参数空间。在第二阶段中，该参数空间的项被无监督地聚类在一组类中，使用可变带宽均值漂移算法。该算法提供的聚类模式构成了一组基线。因此，聚类过程允许使用精确的霍夫参数，然而，当沿着它的像素沿着不完全共线时，仅检测一条线。位于被分组以生成每个基线的线上的边缘像素被投影到该基线上。采用一种快速的纯局部分组算法将沿沿着每条基线的点合并成线段。我们已经进行了几个实验，比较我们的方法与其他方法的性能。实验结果表明，该方法在线段检测能力和执行时间方面具有很高的性能。(c)2005 Elsevier B.V.保留所有权利。
This paper proposes a new algorithm for extracting line segments from edge images. Basically, the method performs two consecutive stages. In the first stage, the algorithm follows a line segment random window randomized Hough transform (RWRHT) based approach. This approach provides a mechanism for finding more favorable line segments from a global point of view. In our case, the RWRHT based approach is used to actualise an accurate Hough parameter space. In the second stage, items of this parameter space are unsupervisedly clustered in a set of classes using a variable bandwidth mean shift algorithm. Cluster modes provided by this algorithm constitute a set of base lines. Thus, clustering process allows using accurate Hough parameters and, however, detecting only one line when pixels along it are not exactly collinear. Edge pixels lying on the lines grouped to generate each base line are projected onto this base line. A fast and purely local grouping algorithm is employed to merge points along each base line into line segments. We have performed several experiments to compare the performance of our method with that of other methods. Experimental results show that the performance of the proposed method is very high in terms of line segment detection ability and execution time. (c) 2005 Elsevier B.V. All rights reserved.