|本期目录/Table of Contents|

[1]闫晖,鲁统伟.图论在路面裂缝分割中的应用研究[J].武汉工程大学学报,2012,(1):53-57.
　YAN Hui,LU Tong wei.Method for segmentation of pavement crack based on graph theory[J].Journal of Wuhan Institute of Technology,2012,(1):53-57.
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图论在路面裂缝分割中的应用研究(/HTML)
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《武汉工程大学学报》[ISSN:1674-2869/CN:42-1779/TQ]

卷:
期数:
2012年1期
页码:
53-57
栏目:
机电与信息工程
出版日期:
2012-02-28

文章信息/Info

Title:
Method for segmentation of pavement crack based on graph theory
文章编号:
16742869(2012)1005304
作者:
闫晖1; 2; 鲁统伟1; 2
1．武汉工程大学计算机科学与工程学院，湖北 武汉 430074；2．智能机器人湖北省重点实验室，湖北 武汉 430074
Author(s):
YAN Hui1; 2;  LU Tongwei1; 2
1.Computer Science of Wuhan Institute of Technology, Wuhan 430074, China;
   2.Hubei Province key Laburatory of Intelligent Robot,Wuhan 430074,China
关键词:
裂缝检测; 图割; 最大类间方差法; 领域差异直方图法
Keywords:
crack detection;  graph cut;  OTSU;  NDHM
分类号:
TP391.41
DOI:
-
文献标志码:
AAdoi:10.3969/j.issn.16742869.2012.1.011
摘要:
针对图谱划分理论与路面裂缝的特点，提出了一种基于图论的路面裂缝分割方法，该方法将图像作为一个带权图进行分析.建立节点之间的位置距离和灰度差异的能量函数，函数的最小值作为路面裂缝检测的最优分组.通过实验与传统方法最大类间方差法、领域差异直方图法相比，此方法能较好的检测出大于1mm的路面裂缝.
Abstract:
With the theory of graph cut and the characteristic of pavement crack, a method for segmentation of pavement crack based on graph theory was proposed. The graph regarded as weighted graph was analyzed in the method. An energy function was established according to the distance and grayscale differences between nodes. The minimum of function corresponds to an optimal group of detection. By experiment and comparing with the existing method such as OTSU（Maximum Variance Between Clusters)、NDHM(Neigboring Difference Histogram Method), the proposed method is better to detect the pavement crack which is larger than 1 mm.

参考文献/References:

\[1\]刘渝，张彦锋，鲁统伟.OCR中一种基于最小乘法的连通域去噪方法［J］.武汉工程大学学报，2011，33（1）：84-87.
\[2\]Li  L, Chan  P, Rao  A,et al. Flexible pavement distress evaluation using image analysis［C］//Proceeding of the 2nd International Conference on Applications of Advanced Technologies in Transportation Engineering.1991: 473-477.
\[3\]Seung-Nam Yu, Jae-Ho Jang. Auto inspection system using a mobile robot for detecting concrete cracks in a tunnel［J］. Automation in Construction, 2006,16(3): 255-261.
\[4\]Cheng H D ，Miyojim M.Automatic Pavement Distress Detection System［J］. Journal of Irfo~afion Sciences，1998（108）:204-219．
\[5\]李晋惠．公路路面裂缝类病害图像处理算法研究\[J\]．计算机工程与应用，2003（35）：212-232．
\[6\]初秀民，严新平，陈先桥，路面破损图像二值化方法研究\[J\]计算机工程与应用,2008，44(28):161-165.
\[7\]Cheng H D, Rong jim. Novel approach to pavement cracking detection based on fuzzy set theory\[J\].ASCE，1999(13):270-280.
\[8\]Li Q Q, Liu X L. Novel Approach to Pavement Image Segmentation Based on Neighboring Difference Histogram Method\[C\]//2008 Congress on Image and Signal Processing, Sanya：2008：792-795.
\[9\]刘向龙, 李清泉. 基于金字塔结构的海量路面影像破损统计模型\[J\] . 武汉大学学报:信息科学版,2008, 33(4): 430-432.
\[10\]Otsu N.A Threshold Selection Method from Gray-level Histogram［J］.IEEE Trans，1979（SMC-9）:62-66.
\[11\]容观澳. 计算机图像处理\[M\].北京：清华大学出版社，2000.
\[12\]付忠良.图像阈值选取方法的构造\[J\].中国图像图形学报,2000,5(6)：466-469.
\[13\]Lee S U.A Comparative Performance Study of Several Global Thresholding Techniques for Segmention［J］. CVGIP，1900（52）:171-190.
\[14\]Shi J, Malik J . Normalized cut s and image segmentation［J］. IEEE Transactions on Pattern Analysis Machine Intelligence,2000,22(8):888-905.
\[15\]Wiedemann C, Ebner H. Automatic completion and evaluation of road networks \[J\] . Int Arch Photogramm Remote Sens, 2000（33）: 976-986.

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