Combination of Local and Global Line Extraction
Combination of Local and Global Line Extraction
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DOI：
10.1006/rtim.1999.0183
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发表时间：
2000-04
期刊：
影响因子：
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通讯作者：
V. Kyrki;H. Kälviäinen
中科院分区：
文献类型：
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作者：
V. Kyrki;H. Kälviäinen
本文研究了如何将局部直线提取和全局直线提取相结合。霍夫变换通常用于检测图像中的线段。然而，标准Hough变换(SHT)存在时间和存储复杂度高的问题，并且不能利用局部直线提取。最近，一种称为连接随机化Hough变换(CRHT)的方法被提出，该方法利用局部检测的优势，例如相邻边缘点的连通性和这些检测点的直线拟合。然而，这种方法在处理具有许多扭曲线条的图像时存在问题。我们提出了一种新的直线检测方法，称为扩展连接随机化Hough变换(ECRHT)，以缓解这些问题。文中还讨论了边缘点梯度信息的使用。使用模拟数据和真实世界数据的实验表明，与SHT和CRHT相比，ECRHT具有更好的性能。
In this paper we study how to combine local and global line extraction. The Hough transform is usually used to detect line segments in an image. However, the standard Hough transform (SHT) suffers from time and storage complexity, and it is incapable to utilize local line extraction. Recently an approach, called the connective randomized Hough transform (CRHT), has been proposed to take advantage of local detection, such as the connectivity of neighboring edge points and the line fitting of these detected points. However, this approach contains problems with images with many distorted lines. We suggest a new line detection approach, called the extended connective randomized Hough transform (ECRHT), to alleviate these problems. The use of gradient information of edge points is also discussed. Experiments with simulated and real-world data demonstrate the benefits of the ECRHT, as compared to the SHT and the CRHT.