基于灰色系统理论的路面图像裂缝检测算法研究
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摘要
公路路面的裂缝检测是公路运营后维护与保养的主要问题之一。随着全球经济的持续发展,世界上大多数国家都掀起了一股大力发展基础设施的热潮,其中,公路建设作为交通行业的重头戏而受到普遍重视。由于公路建成的里程越来越长,传统的主要靠人工进行路面检测的方式也越来越满足不了日益增长的检测工作量,也不能适应新时期的检测要求,因此研制和开发全自动、高科技的公路路面检测设备与算法成为了发展交通道路行业的一项重要任务。
本文在查阅大量相关文献的基础上,对目前的公路路面图像的裂缝检测算法进行了综述。针对目前的路面图像裂缝检测算法主要局限在基本的图像处理技术、数学形态学、神经网络、小波分析等经典理论上的特点,本文尝试把灰色系统理论应用到对路面图像的预处理与分割中来,为裂缝检测的后续处理打下一定的基础。
从路面裂缝产生的力学原因入手,本文在分析路面裂缝图像的灰色特点的基础上,阐明了应用灰色系统理论来解决路面图像裂缝检测问题的合理性和优越性,总结了路面检测设备的发展状况,介绍了灰色系统理论的基本概念,通过对路面图像的去噪、滤波、增强以及边缘检测的基本原理的分析,阐述了10种结合灰色关联分析、灰熵理论、灰色预测模型的路面图像的去噪、滤波、增强、边缘检测的新算法。
针对传统的邓氏关联度在应用图像数据计算时,公式中可能出现分母为零、滤波效果不理想等情况,本文结合路面图像数据的特征,提出了一种灰色图像关联度模型,选取图像邻域中的部分数据进行加权平均,并充分利用当前滤波窗口中在本次遍历中新得到的图像像素灰度值。当路面图像中的噪声密度增加时,图像邻域窗口中的噪声点在进行加权滤波计算时也增加到了一个不可忽略的地步。本文提出,在对路面图像进行去噪前,先根据邻域窗口中像素灰度值的灰关联序进行噪声判别,再对当前像素为噪声点的邻域窗口实施非噪声点选取的灰关联去噪或扩大窗口中值滤波。在含有噪声的路面图像中,本文利用邻域中各像素值与邻域中值的灰熵值作为邻域中各像素值的权系数,求邻域各像素的加权平均值作为中心点的新灰度值以此来实现对噪声的滤除。通过对图像邻域窗口中像素的灰度值进行排序,将图像的像素分为三类进行分别处理：正常点不变,大噪声块要扩大窗口进行中值滤波,仅对邻域中含有少量噪声的噪声点实施基于仿射变换的灰色预测滤波。
通过计算邻域窗口中中心像素的灰度值与邻域各像素值的灰关联熵,来度量邻域窗口中的局部边缘程度,然后搜索阈值来对路面图像的边缘进行分割。通过计算图像邻域窗口中的16种纹理方向的像素灰熵值,以及邻域中灰熵值的最大值与最小值之差,来找出图像中局部纹理起伏的边缘特征,从而设定阂值实现路面图像的边缘检测。对图像邻域窗口中的四个主要的纹理走向的像素组进行添加辅助点的GM(1,1,C)建模,并将四个模型的拟合值的残差和的最大值与最小值之差作为当前中心点的边缘程度的测度,设定阈值,提取路面图像的边缘。
在图像邻域窗口中应用灰色图像关联度选择出邻域中部分与中心像素不同属性的像素并增大它们的均值与中心像素的对比度,以此提高图像增强的效果。通过利用图像邻域窗口中的灰熵值构造图像模糊局部对比度增强的增强指数,来实现对模糊对比度函数的自适应增强。从当前中心点周围的八个方向指向邻域窗口中心选取中心点与邻域均值之差作为原始数据点,并按紧邻均值生成的方式添加辅助点,建立终点固定的离散灰色预测模型,将模型的拟合值作为对局部对比度函数的增强尺度,来自适应地调节图像的局部对比度,提高路面图像的增强效果。
本课题来源为：教育部高等学校博士点基金项目：基于广义累加灰生成的极限承载力建模与预测研究(项目编号：200804970005)、国家自然科学基金项目：基于矩阵分析的灰序列生成预测建模及应用研究(项目编号：70471019)、武汉市科技攻关计划项目：基于演化优化技术的Web图像语义模糊分类研究(项目编号：201010621218).
Road surface crack detection has been one of the main issues of highway care and maintenance after the highway being put into operation. With the sustainable development of global economy, most countries have set off a wave of enthusiasm about developing the infrastructure, in which road-building, as a highlight of the transport sector, has attracted universal attention. As the mileage of completed highway is becoming longer and longer, traditional manual way of road pavement testing is increasingly unable to meet the growing testing workload, neither can it adapt to the testing requirements of the new era. Therefore, researching and developing the automated, high-tech road surface testing equipment and algorithm has become an important task for the development of road traffic industry.
In this paper, via referring to a large number of corresponding literatures, we summarise the current crack detection algorithms of the haighway pavement image. Viewing the fact that the current road surface image crack detection algorithms are mainly limited to the traditional theories of the basic image processing techniques, mathematical morphology, neural networks, and wavelet analysis theory, this paper attempts to apply the grey system theory to the road surface image preprocessing and segmentation with an effort to lay a foundation for the follow-up treatment of crack detection.
Beginning with the analysis of the mechanical causes of road pavement crack, the paper has clarified the rationality and superiority of applying the grey system theory to solving the problem of pavement image crack detection based on the analysis of the grey characteristics of the pavement crack image, has sumed up the development of road testing equipment and has introduced the basic concept of grey system theory. By analyzing the basic principles of the pavement crack image denoising, filtering, enhancement, and edge detection, we propose ten new kinds of algorithms about the pavement image denoising, filtering, enhancement and edge detection combining with the grey relational analysis, grey entropy, grey prediction theory respectively.
Viewing the fact that when applying the Deng's grey relational degree to the image data computation, the denominator in the formula may appear zero, the filtering effect is not very satisfactory, and taking into account the characteristics of the road image data, this paper propose a new grey relational degree model, named grey image relational degree, which selects the part of the data in the neighborhood of image to carry out weighted average operation, and makes full use of the new pixel gray value in the neighborhood window resulting from the current traversal.When the noise density of the road surface image increases, the noise points in the image neighborhood window turn to an extent that can not be ignored when computing the central weighted value. This paper propose that, before pavement image denoising, the noise and non-noise points should be distinguished according to grey relational order of pixel gray value in the neighborhood window. And then for the pixel that is noise point as the center of the neighborhood window, the non-noise pixels around the central pixel in the neighborhood are chosen to carry out the grey relational noise reduction operator, or expand the window to carry out the median filter operator. In a noise-containing pavement image, this paper uses the entropy between the central pixel and each other pixel of the beighborhood as the weighted exponent to compute the weighted average value of all pixels in the neighborhood as the new value of central pixel in the neighborhood to achieve the noise filtered. By sorting the pixel gray value of the image neighborhood window, we divide the pixels of the image into three groups to deal with them respectively:those remain the normal unchanged, those expand the window for median filtering for large noise block, and those only make the noise with a neighborhood containing a small amount of noise operated by grey prediction filtering based on a affine transformation.
Through computing the grey relational entropy between the central pixel of the neighborhood window and each adjacent pixel of the neighboerhood, we measured the local edge degree of the neighborhood window, and then searched the threshold value to nake the edge of the pavement image that has been segmented from the background. Aftering calculating the pixel grey entropy values of sixteen kinds of texrure in the image neighborhood, we computed the difference between the maximum and minimum of the gray entropy in order to find out the edge feature of local texture variances, and then set a threshold to make the edge of pavement detected. We made GM(1,1,C) models for the pixel groups with four main texture toward in the image neighborhood window by adding auxiliary point and by supposing the difference of the maximum and minimum of rediduals summation of fitting values of four models be the extent of measure of the current central point,then we set thresholds and extracted the edge of the pavement image.
In the image neighborhood window, we selected the part of pixels which are with property different from the central pixel by using the gray image relational degree, and increased the contrast between their average value and the central pixel value in order to improve the effect of image enhancement. Using the grey entropy value of the neighborhood of the image, we constructed the enhancement factor of the image fuzzy local contrast enhancement in order to make the fuzzy local contrast function adaptively enhanced. From eight directions of the current central pixel, pointing to the centre point of the surrounding neighborhood window, we chose the absolute value of difference between the centre poit and the average value of all the pixels in the neighborhood as the original data points, generated auxiliary data points according to the way of using the mean value of pixels adjacent, established a discrete grey prediction model with a fixed end point, made the fitted values of the model as the scale of local contrast enhancement to adaptively adjust the image local contrast, and improved the effect of the pavement image enhancement.
This dissertation was from Specialized Research Fund for the Doctoral Program of Higher Education of China:Study on Modeling and Prediction of the Ultimate Bearing Capacity Based on the Generalized Accumulated Generating Operation (NO.200804970005), the National Natural Science Fund Project:Study on Modeling and Prediction of the Short-term Traffic Flow in the Road Network based on Grey Generation Space Modeling (NO.70971103), and the Wuhan Science and Technology Research Projects:Fuzzy Classification of Web Image Semantics based on Evolutionary Optimization(NO.201010621218).
本文在查阅大量相关文献的基础上,对目前的公路路面图像的裂缝检测算法进行了综述。针对目前的路面图像裂缝检测算法主要局限在基本的图像处理技术、数学形态学、神经网络、小波分析等经典理论上的特点,本文尝试把灰色系统理论应用到对路面图像的预处理与分割中来,为裂缝检测的后续处理打下一定的基础。
从路面裂缝产生的力学原因入手,本文在分析路面裂缝图像的灰色特点的基础上,阐明了应用灰色系统理论来解决路面图像裂缝检测问题的合理性和优越性,总结了路面检测设备的发展状况,介绍了灰色系统理论的基本概念,通过对路面图像的去噪、滤波、增强以及边缘检测的基本原理的分析,阐述了10种结合灰色关联分析、灰熵理论、灰色预测模型的路面图像的去噪、滤波、增强、边缘检测的新算法。
针对传统的邓氏关联度在应用图像数据计算时,公式中可能出现分母为零、滤波效果不理想等情况,本文结合路面图像数据的特征,提出了一种灰色图像关联度模型,选取图像邻域中的部分数据进行加权平均,并充分利用当前滤波窗口中在本次遍历中新得到的图像像素灰度值。当路面图像中的噪声密度增加时,图像邻域窗口中的噪声点在进行加权滤波计算时也增加到了一个不可忽略的地步。本文提出,在对路面图像进行去噪前,先根据邻域窗口中像素灰度值的灰关联序进行噪声判别,再对当前像素为噪声点的邻域窗口实施非噪声点选取的灰关联去噪或扩大窗口中值滤波。在含有噪声的路面图像中,本文利用邻域中各像素值与邻域中值的灰熵值作为邻域中各像素值的权系数,求邻域各像素的加权平均值作为中心点的新灰度值以此来实现对噪声的滤除。通过对图像邻域窗口中像素的灰度值进行排序,将图像的像素分为三类进行分别处理：正常点不变,大噪声块要扩大窗口进行中值滤波,仅对邻域中含有少量噪声的噪声点实施基于仿射变换的灰色预测滤波。
通过计算邻域窗口中中心像素的灰度值与邻域各像素值的灰关联熵,来度量邻域窗口中的局部边缘程度,然后搜索阈值来对路面图像的边缘进行分割。通过计算图像邻域窗口中的16种纹理方向的像素灰熵值,以及邻域中灰熵值的最大值与最小值之差,来找出图像中局部纹理起伏的边缘特征,从而设定阂值实现路面图像的边缘检测。对图像邻域窗口中的四个主要的纹理走向的像素组进行添加辅助点的GM(1,1,C)建模,并将四个模型的拟合值的残差和的最大值与最小值之差作为当前中心点的边缘程度的测度,设定阈值,提取路面图像的边缘。
在图像邻域窗口中应用灰色图像关联度选择出邻域中部分与中心像素不同属性的像素并增大它们的均值与中心像素的对比度,以此提高图像增强的效果。通过利用图像邻域窗口中的灰熵值构造图像模糊局部对比度增强的增强指数,来实现对模糊对比度函数的自适应增强。从当前中心点周围的八个方向指向邻域窗口中心选取中心点与邻域均值之差作为原始数据点,并按紧邻均值生成的方式添加辅助点,建立终点固定的离散灰色预测模型,将模型的拟合值作为对局部对比度函数的增强尺度,来自适应地调节图像的局部对比度,提高路面图像的增强效果。
本课题来源为：教育部高等学校博士点基金项目：基于广义累加灰生成的极限承载力建模与预测研究(项目编号：200804970005)、国家自然科学基金项目：基于矩阵分析的灰序列生成预测建模及应用研究(项目编号：70471019)、武汉市科技攻关计划项目：基于演化优化技术的Web图像语义模糊分类研究(项目编号：201010621218).
Road surface crack detection has been one of the main issues of highway care and maintenance after the highway being put into operation. With the sustainable development of global economy, most countries have set off a wave of enthusiasm about developing the infrastructure, in which road-building, as a highlight of the transport sector, has attracted universal attention. As the mileage of completed highway is becoming longer and longer, traditional manual way of road pavement testing is increasingly unable to meet the growing testing workload, neither can it adapt to the testing requirements of the new era. Therefore, researching and developing the automated, high-tech road surface testing equipment and algorithm has become an important task for the development of road traffic industry.
In this paper, via referring to a large number of corresponding literatures, we summarise the current crack detection algorithms of the haighway pavement image. Viewing the fact that the current road surface image crack detection algorithms are mainly limited to the traditional theories of the basic image processing techniques, mathematical morphology, neural networks, and wavelet analysis theory, this paper attempts to apply the grey system theory to the road surface image preprocessing and segmentation with an effort to lay a foundation for the follow-up treatment of crack detection.
Beginning with the analysis of the mechanical causes of road pavement crack, the paper has clarified the rationality and superiority of applying the grey system theory to solving the problem of pavement image crack detection based on the analysis of the grey characteristics of the pavement crack image, has sumed up the development of road testing equipment and has introduced the basic concept of grey system theory. By analyzing the basic principles of the pavement crack image denoising, filtering, enhancement, and edge detection, we propose ten new kinds of algorithms about the pavement image denoising, filtering, enhancement and edge detection combining with the grey relational analysis, grey entropy, grey prediction theory respectively.
Viewing the fact that when applying the Deng's grey relational degree to the image data computation, the denominator in the formula may appear zero, the filtering effect is not very satisfactory, and taking into account the characteristics of the road image data, this paper propose a new grey relational degree model, named grey image relational degree, which selects the part of the data in the neighborhood of image to carry out weighted average operation, and makes full use of the new pixel gray value in the neighborhood window resulting from the current traversal.When the noise density of the road surface image increases, the noise points in the image neighborhood window turn to an extent that can not be ignored when computing the central weighted value. This paper propose that, before pavement image denoising, the noise and non-noise points should be distinguished according to grey relational order of pixel gray value in the neighborhood window. And then for the pixel that is noise point as the center of the neighborhood window, the non-noise pixels around the central pixel in the neighborhood are chosen to carry out the grey relational noise reduction operator, or expand the window to carry out the median filter operator. In a noise-containing pavement image, this paper uses the entropy between the central pixel and each other pixel of the beighborhood as the weighted exponent to compute the weighted average value of all pixels in the neighborhood as the new value of central pixel in the neighborhood to achieve the noise filtered. By sorting the pixel gray value of the image neighborhood window, we divide the pixels of the image into three groups to deal with them respectively:those remain the normal unchanged, those expand the window for median filtering for large noise block, and those only make the noise with a neighborhood containing a small amount of noise operated by grey prediction filtering based on a affine transformation.
Through computing the grey relational entropy between the central pixel of the neighborhood window and each adjacent pixel of the neighboerhood, we measured the local edge degree of the neighborhood window, and then searched the threshold value to nake the edge of the pavement image that has been segmented from the background. Aftering calculating the pixel grey entropy values of sixteen kinds of texrure in the image neighborhood, we computed the difference between the maximum and minimum of the gray entropy in order to find out the edge feature of local texture variances, and then set a threshold to make the edge of pavement detected. We made GM(1,1,C) models for the pixel groups with four main texture toward in the image neighborhood window by adding auxiliary point and by supposing the difference of the maximum and minimum of rediduals summation of fitting values of four models be the extent of measure of the current central point,then we set thresholds and extracted the edge of the pavement image.
In the image neighborhood window, we selected the part of pixels which are with property different from the central pixel by using the gray image relational degree, and increased the contrast between their average value and the central pixel value in order to improve the effect of image enhancement. Using the grey entropy value of the neighborhood of the image, we constructed the enhancement factor of the image fuzzy local contrast enhancement in order to make the fuzzy local contrast function adaptively enhanced. From eight directions of the current central pixel, pointing to the centre point of the surrounding neighborhood window, we chose the absolute value of difference between the centre poit and the average value of all the pixels in the neighborhood as the original data points, generated auxiliary data points according to the way of using the mean value of pixels adjacent, established a discrete grey prediction model with a fixed end point, made the fitted values of the model as the scale of local contrast enhancement to adaptively adjust the image local contrast, and improved the effect of the pavement image enhancement.
This dissertation was from Specialized Research Fund for the Doctoral Program of Higher Education of China:Study on Modeling and Prediction of the Ultimate Bearing Capacity Based on the Generalized Accumulated Generating Operation (NO.200804970005), the National Natural Science Fund Project:Study on Modeling and Prediction of the Short-term Traffic Flow in the Road Network based on Grey Generation Space Modeling (NO.70971103), and the Wuhan Science and Technology Research Projects:Fuzzy Classification of Web Image Semantics based on Evolutionary Optimization(NO.201010621218).
引文
[1]邓聚龙,灰理论基础[M],武汉：华中科技大学出版社,2002
[2]王保平,基于模糊技术的图像处理方法研究[D],西安：西安电子科技大学,2004.
[3]何靓俊,基于图像处理的沥青路面裂缝检测系统研究[D],西安：长安大学,2008：17-35
[4]田恩杰,高等级公路路面病害自动检测方法研究[D],长春市：吉林大学,2007：3-9,23-24
[5]楼竞,基于图像分析的路面病害检测方法与系统开发[D],南京：南京理工大学,2008：2-3,14-15
[6]张娟,沙爱民,孙朝云等,路面裂缝自动识别的图像增强技术[J],中外公路,2009,29(4)：304-305
[7]张岐山,郭喜江,邓聚龙,灰关联熵分析方法[J],系统工程理论与实践,1996(8)：7-11
[8]王季方,卢正鼎,模糊控制中隶属度函数的确定方法[J],河南科学,2000,18(4)：348-351
[9]陈利利.基于多尺度图像分析的路面病害检测方法研究与分析[D].南京：南京理工大学,2009
[10]Li L,Chan P, Rao A. Flexible pavement distress evaluation using image analysis[J],1993(119):402-418
[11]Liu, Fanfan; Xu, Guoai; Yang, Yixian, et al. Novel approach to pavement cracking automatic detection based on segment extending[C].//Proceedings-2008 International Symposium on Knowledge Acquisition and Modeling, Inst. of Elec. and Elec. Eng. Computer Society, Wuhan 2008:610-614
[12]Grivas D A, Bhagvati C, Skolnick M M. Feasibility of automating pavement distress assessment using mathematical morphology [J]. Transportation Research Record. 1994(1435):52-58.
[13]Li, Li, Chan, P, Lytton, R L. DETECTION OF THIN CRACKS ON NOISY PAVEMENT IMAGES. Transportation Research Recora [J],1991(1311):131-135
[14]Yaxiong Huang, Bugao Xu, Automatic inspection of pavement cracking distress. Journal of electronic imaging [J],15(1),013017, Jan-Mar 2006
[15]Ying L, Salari E, Beamlet transform based technique for pavement image processing and classification[C].//Proceedings of 2009 IEEE International conference on Electro/information technology. Windsor ON.7-9 June,2009:141-145
[16]Sun, Y. Salari, E. Chou, E. Automated pavement distress detection using advanced image processing techniques [C]//Proceedings of 2009 IEEE International conference on Electro/information technology. Windsor ON.7-9 June,2009:373-377
[17]王刚,王娟,王德华等,基于Contourlet变换域统计模型的路面图像去噪算法[J],光电子激光,2009,20(10)：1394-1398
[18]李刚,贺昱曜,赵妍,基于大津法和互信息量的路面破损图像自动识别算法[J],微电子学与计算机,2009,26(7)：241-247
[19]朱其刚,刘明,杨峰.基于区域特征的路面裂缝图像滤噪算法[J],机器视觉,2005(11)：118-120
[20]张宏,英红,沥青路面裂缝图像识别技术研究进展[J],华东公路,2009(4)：81-84
[21]Siriphan Jitprasithsir.i Development of a new digital pavement image processing algorithm for unified crack index computation[D]. A Aissertation Submitted to the Faculty of theUniversity ofUtah,1997
[22]谢昌荣,张郭晶,路面裂缝检测图像处理算法的研究,中外公路[J],2009,29(6)：112-115
[23]孙波成,邱延峻,路面裂缝图像处理算法研究[J],公路交通科技,2008,25(2)：64-68
[24]MEIGNEN D, BERNADET M, BRIAND H·One Application of Neural Networks for Detection of Defects Using Video Data Bases:Identification of Road Distresses [J]·//Proceedings Database and Expert Systems Applications,1997,9 (8):459-464-
[25]CHENGHD, SHI X J, GLAZIER C·Real-time ImageThresholding Based on Sample Space Reduction and Interpolation Approach [J]-American Society of Civil Engineers (ASCE),2003,17 (4):264-272-
[26]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
[27]Egemen Teomete, Viren R Amin,Halil Ceylan.Digital image processing for pavement distress analyses[C].//:Proceesdings of the 2005 Mid-Continent Transportation Research Symposium.2005:1-13.
[28]Kitter J, Illingworth J. Minimum error thresholding [J]. Pattern Recognition, 1986(1):41-47
[29]Koutsopoulos H N, Downey A B. Primitive-based classification of pavement cracking images [J]. Journal of Transportation Engineering,1993(3):402-418
[30]赵明宇,孙立车,一种路面损坏状况的数字图像识别方法[J],交通与计算机,2007(6)：13-15
[31]Georgopoulos A,Loizos A,Flouda A. Digital image processing as a tool for pavement distress evaluation[J]. ISPRS Journal of Photogrammetry and Remote ,Sensing,1995,50(1):23-33
[32]李晋惠,用图像处理的方法检测公路路面裂缝类病害[J],长安大学学报(自然科学版),2004(3)：24-29
[33]陈先桥,严新平,初秀民,用于带阴影路面图像增强的处理方法[J],计算机工程与应用,2008,44(33)：188-190
[34]Juan Wang, Qun Wan, Anmin Huang, A Pavement Distress Survey Algorithm With Novel Models And Line Points Detection[C].//2009 2nd International Conference on Power Electronics and Intelligent Transportation System,2009:140-143
[35]Wei Wei, Boying Liu, Peng Bai, Automatic Road Crack Image Preprocessing for Detection and Identification[C],//2009 Second International Conference on Intelligent Networks and Intelligent Systems,2009:319-322
[36]L. Ying and E. Salari, Beamlet Transform Based Technique for Pavement Image Processing and Classification[C],//IEEE International Conference on Electro/Information Technology,2009:141-145
[37]Siwaporn Sorncharean and Suebskul Phiphobmongkol, Crack Detection on Asphalt Surface Image Using Enhanced Grid Cell Analysis[C],//4th IEEE International Symposium on Electronic Design, Test.& Applications,2008.10:49-54
[38]Chang-Xia Ma, Chun-Xia Zhao, Ying-kun Hou, Pavement Distress Detection Based on Nonsubsampled Contourlet Transform[C],//2008 International Conference on Computer Science and Software Engineering,2008.10:28-31
[39]Fanfan Liu, Guoai Xu, Yixian Yang, Xinxin Niu, Yuli Pan, Novel Approach'to Pavement Cracking Automatic Detection Based on Segment Extending[C],//2008 International Symposium on Knowledge Acquisition and Modeling,2008:610-614
[40]LI Qingquan, LIU Xianglong, Novel Approach to Pavement Image Segmentation Based on Neighboring Difference Histogram Method[C],//2008 Congress on Image and Signal Processing,2008:792-796
[41]Bing Ma, Sridhar Lakshmanan, Alfred 0. Hero, Pavement Boundary Detection Via Circular Shape Models[C],//Proceedings of the IEEE intelligent Vehicles Symposium 2000 Dearborn (MI), USA October 3-5,2000:644-649
[42]Bing Ma, Sridhar Lakshmanan, and Alfred O. Hero, Simultaneous Detection of Lane and Pavement Boundaries Using Model-Based Multisensor Fusion[J], IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL.1, NO. 3,SEPTEMBER 2000:135-147
[43]HOU Xiang-dan, DONG Yong-feng,GUO Hai-jiao,YANG Xin, The Method of Pavement Image Splicing Based on SIFT Algorithm[C],//Global Congress on Intelligent Systems,2009:538-542
[44]S K Pal, R A King. Image Enhancement Using Fuzzy Sets[J]. Electron. Lett.,1980, 16(9):376-378.
[45]Pal S K, King R A. Image Enhancement Using Smoothing with Fuzzy Sets[J]. IEEE Trans. Syst. Man. Cybern.,1981,11(7):494-501.
[46]李弼程,郭志刚,文超,图像的多层次模糊增强与边缘提取[J],模糊系统与数学,2000,14(4)：77-83
[47]闫茂德,伯绍波,李雪等,一种自适应模糊的局部区域图像增强算法[C],//Proceedings of the 26th Chinese Control Conference July 26-31,2007, Zhangjiajie, Hunan, China:
[48]付忠良,图像阈值选取方法-OTSU方法的推广[J],计算机应用,2000,20(5)：37-39
[49]王保平,刘升虎,范九伦等,基于模糊熵的自适应图像多层次模糊增强算法[J],电子学报,2005,33(4)：730-734
[50]宋洁,孙荣艳,张永杰,李娜娜,基于金字塔和模糊聚类的路面图像拼接方法[J],河北工业大学学报,2008,37(5)：13-19
[51]杜文靖,朱少辉,刘玉臣,高等级沥青公路路面裂缝图像处理技术[J],建设机械技术与管理,2006(3)：65-68
[52]刘玉臣,王国强,林建荣,基于模糊理论的路面裂缝图像增强方法[J],筑路机械与施工机械化,2006(2):35-37
[53]Cheng H D, Rong Jim. Novel approach to pavement cracking detection based on fuzzy set theory [J]. ASCE.1999(13):270-280.
[54]Peggy Subirats, Jean Dumoulin, Vincent Legeay and Dominique Barba, AUTOMATION OF PAVEMENT SURFACE CRACK DETECTION USING THE CONTINUOUS WAVELET TRANSFORM[C],//ICIP2006:3037-3040
[55]左永霞,高速公路路面破损图像识别技术研究[D],沈阳：吉林大学,2008
[56]Yongxia Zuo, Guoqiang Wang, Chuncheng Zuo, Wavelet Packet Denoising for Pavement Surface Cracks Detection[C],//2008 International Conference on Computational Intelligence and Security,2008:481-484
[57]初秀民,王荣本,储江伟,王超,基于不变矩特征的沥青路面破损图像识别[J],吉林大学学报(工学版),2003,33(1)：1-7
[58]王珣,图像分割技术及其在路面开裂损坏识别中的应用[J],计算机工程,2003,29(17)：117-119
[59]Kaseko Mohamed S,Ritchie,Stephen G.Pavement image processing using neural networks[C].//Proceedings of the 2nd International Conference on Applications of Advanced Technologies in Transportation Engineering,1991:18-21.
[60]Chou J, Cheng H D.Pavement distress classification using neural networks[C].//Proceedings,IEEE International Conference on Systems,Man and Cybernetics,1994:397-401.
[61]Guoai Xu, Jianli Ma, Fanfan Liu, Xinxin Niu, Automatic Recognition of Pavement Surface Crack Based on BP Neural Network[C],//2008 International Conference on Computer and Electrical Engineering,2008:19-22
[62]张雷,肖梅,马建,宋宏勋,基于视觉模型的路面裂缝检测算法[J],长安大学学报(自然科学版),2009(5)：21-24
[63]闫茂德,伯绍波,贺昱曜,一种基于形态学的路面裂缝图像检测与分析方法[J],工程图学学报,2008(2)：142-147
[64]伯绍波,闫茂德,孙国军,贺昱曜,沥青路面裂缝检测图像处理算法研究[J],微计算机信息,2007,23(4-3)：280-282
[65]Perona P, Malik J. Scale-space and edge detection using anisotropic Diffusion[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1990,12(7):629-639.
[66]Weickert J. Coherence enhancing diffusion filtering[J]. International Journal ofComputerVision,1999,31(2/3):111-127.
[67]Yongxia Zuo, Guoqiang Wang, Chuncheng Zuo, A Novel Image Segmentation Method of Pavement Surface Cracks Based on Fractal Theory[C]//2008 International Conference on Computational Intelligence and Security,2008:485-488
[68]Philippe Delanes and Dominique Barba. A Markov random field for rectilinear structure extraction in pavement distress image analysis[C],//IEEE.Internat.;Conf. on Image Processing, vol.1,1995:446-449
[69]Nana Li, Xiangdan Hou, Xinyu Yang, Yongfeng Dong, Automation Recognition of Pavement Surface Distress Based on Support Vector Machine[C],//2009 Second International Conference on Intelligent Networks and Intelligent Systems.2009:346-349
[70]唐磊,基于图像分析的路面病害自动检测[D],南京：南京理工大学,2007
[71]马常霞,赵春霞,胡勇,结合NSCT和图像形态学的路面裂缝检测[J],计算机辅助设计与图形学学报,2009,21(12)：1761-1767
[72]王士清,韩力群,用于公路路面状况自动检测的图像处理技术[J],科技摘报开发与经济,2003(8)
[73]张宏,英红,沥青路面裂缝图像识别技术研究进展[J],华东公路,2009(4)：81-84
[74]刘思峰,党耀国,方志耕,灰色系统理论及其应用(第三版)[M],北京：科学出版社,2005.6
[75]肖新平,宋中民,李峰,灰技术基础及其应用[M],北京：科学出版社,2005.8
[76]谢乃明,灰色系统建模技术研究[D],南京：南京航空航天大学,2008.
[77]章毓晋,图象处理和分析[M],北京：清华大学出版社,1999.3
[78]王耀南,李树涛,毛建旭,计算机图像处理与识别技术[M],2001.6
[79]胡隽,何辅云,贺静,灰色系统理论在图像处理中的应用[J],电视技术,2003(7)：19-22
[80]马苗,郝重阳,黎新伍,图像灰处理技术研究综述[C],//Proceedings of the 5th World Congress on Intelligent Control and Automation, June 15-19.2004, Hangzhou. P.R. China,Vol.5:4141-4145
[81]马苗,田红鹏,张艳宁,灰色理论在图像工程中的应用研究进展[J],中国图象图形学报,Vol.12(11),2007.11：1943-1951
[82]路瑶,王晶,胡蕾,灰色系统理论在图像处理中的应用综述[J],自动化技术与应用,2007(6),49-52
[83]温丽华,灰色系统理论及其应用[D],哈尔滨：哈尔滨工程大学,2003
[84]鲁胜强,基于灰色系统理论的图像去噪算法研究[D],武汉：武汉理工大学,2008
[85]李雪莲,灰色系统理论及其在医学图像处理中的应用[D],哈尔滨：哈尔滨工程大学,2005
[86]魏丽,陈丽宇,关于一类新的广义灰色关联度[J],宁夏大学学报(自然科学版),2007,28(1),：22-24
[87]曹明霞,党耀国,张蓉等,对灰色关联度计算方法的改进[J],统计与决策,2007(4)：29-30
[88]东亚斌,段志善,灰色关联度分辨系数的一种新的确定方法[J],西安建筑科技大学学报(自然科学版),2008,40(4)：589-592
[89]田民,刘思峰,卜志坤,灰色关联度算法模型的研究综述[J],统计与决策,2008(1)：24-27
[90]田红鹏,马苗,灰色关联分析在工程图纸除噪预处理中的应用研究[J],计算机应用研究,2007,24(4)：198-200
[9l]马苗,樊养余,谢松云等,基于灰色系统理论的图象边缘检测新算法[J],中国图象图形学报,2003,8(A)(10)：1136-1139
[92]郑子华,陈家祯,陈利永,基于灰色绝对关联度的边缘检测算法[J],福建师范大学学报(自然科学版),2004,20(4)：20-23
[93]胡鹏,傅仲良,陈楠,利用灰色理论进行图像边缘检测[J],武汉大学学报信息科学版,2006,31(5)：411-414
[94]李俊峰,杨瑷萍,戴文战等,基于灰色绝对关联度和LOG算子的图像边缘检测算法研究[C],//Proceedings of the 26th Chinese control conference, July 26-31, 2007, Zhangjiajie,Hunan, China:480-484
[95]韩学平,芮筱亭,赵革,多决策目标排序的层次分析与灰色关联法[J],火力与指挥控制,2009,34(9)：80-83
[96]Miao Ma, Hongpeng Tian, Chongyang Hao, Image Denoising Using Grey Relational Analysis in Spatial Domain[C],//Visual Communications and Image Processing 2005:335-342
[97]陶剑锋,基于灰色系统理论的数字图像处理算法研究[D],武汉：武汉理工大学,2004
[98]褶乃强,基于灰色理论的图像边缘检测算法研究[D],武汉：武汉理工大学,2009
[99]冯冬竹,阎杰.一种基于灰关联分析的红外图像滤波算法[J],.西北工业大学学报,2006,24(6)：710-712
[100]冯冬竹,阎杰,基于灰关联分析的红外图像边缘检测算法[J],红外技术,2006,28(3)：161-164
[101]MA Miao, ZHANG Yanning, SUN Li, et al. SAR Image Despecking Using Grey System Theory[C],//Proceedings of 2007 IEEE International Conference on Grey Systems and Intelligent Service,November 18-20,2007, Nanjing,China:458-462
[102]Dongzhu Feng, Jie Yan and Panfeng Huang, A Novel De-noise method Based on the Grey Relational Analysis[C],//Proceedings of the 2006 IEEE International Conference on Information Acquisition, August 20-23,2006, Weihai,Shandong,China:1385-1389
[103]Dongzhu Feng, Xin Wang, Yuhe Liu, An edge detection method for infrared image based on grey relational analysis[C],//2nd International Symposium on Systems and Control in Aerospace and Astronautics,2008.:1-5
[104]Ziyang Zhen, Zhou Gu, and Yuanyuan Liu, A Novel Fuzzy Entropy.Image Segmentation Approach Based on Grey Relational Analysis[C],//Proceedings of 2007 IEEE International Conference on Grey Systems and Intelligent Services, November 18-20,2007, Nanjing, China:1019-1022
[105]Li Guoliang, Fu Qiang, Grey Relational Analysis Model Based on Weighted Entropy and Its Application[C],//International Conference on Wireless Communications, Networking and Mobile Computing,2007:5500-5503
[106]王博,熵理论在证券投资中的模型及应用研究[D],合肥：合肥工业大学,2008.
[107]程亮,最大熵原理与最小熵方法在测量数据处理中的应用[D],成都：电子科技大学,2008
[108]周桂萍,基于熵AHP法的经济发展评价[D],大连：大连理工大学,2007.
[109]高跃文,基于熵-灰色关联-理想解法的城市道路建设时序规划研究[D],北京：北京交通大学,2009.
[110]刘勇,基于热力学熵和混沌理论的城市道路交通系统的研究[D],西安：长安大学,2009.
[111]孙淑绒,基于熵的深海资源图像处理算法研究与应用[D],长沙：中南大学,20008.
[112]赵凤,基于模糊熵理论的若干图象分割方法研究[D],西安：西安邮电学院,2007.
[113]吴成茂,模糊熵的修改及其在图像分割中的应用[J],微电子学与计算机,2008,25(1)：25-28
[114]Zhang Qishan, HanWeiyou, DengJulong, Information Entropy of Discrete Grey number[J], Journal of Grey Systems,1994,6(4):303-314
[115]张岐山,李锡纯,邓聚龙,不确定型决策的灰熵方法[J],决策借鉴,1995(6)：37-39
[116]杨玉中,吴立云,张强,基于灰熵的不确定型决策方法及其应用[J],工业工程与管理,2006(2)：92-94
[117]骆文辉,杨建军,基于灰熵方法的综合评估[J],指挥控制与仿真,2008,30(2)：74-77
[118]李朝霞,牛文娟,系统多层次灰色熵优选理论及其应用[J],系统工程理论与实践,2007(8)：49-55
[119]Miao Ma, Yanning Zhang, Hongpeng Tian, et al. A Fast SAR Image Segmentation Algorithm based on Particle Swarm Optimization and Grey Entropy[C],//Fourth International Conference on Natural Computation,2008,4:8-12
[120]Ma Lihua, Yan Huizhe, Study on Gray Entropy Comprehensive Evaluation Model of the Economic Benefit in Mining Enterprises[C],//ICCSIT2009:317-320.
[121]YANG Yuzhong, WU Liyun, Grey Entropy Method for Green Supplier Selection[C],//WICOM2007:4682-4685
[122]Guan Yeqing; Liu Si-Feng, An Approach of the Grey Modeling Based on Cotangent Function Transformation [C],//4th International Conference on Wireless Communications, Networking and Mobile Computing,2008:1-4
[123]Cuifeng Li, Wenzhan Dai, GM(1,1) Model and Its Application Based on Transformation Function sinx[C].//Proceedings of the 6th world congress on intelligent control and automation, June 21-23,2006,Dalian,China:4682-4685
[124]Xin-ping XIAO, SHI-KUAN TONG, SHU-HUA MAO, research on ill-condition problem in grey prediction control model[C],//Proceedings of the third international conference on machine learning and cybernetics, Shanghai,26-29 August 2004:2010-2013
[125]毛树华.灰色预测控制模型的病态性研究[D],武汉：武汉理工大学,2004. [126] Dou YU, Yong WEI, New algorithms of initial development coefficients and optimization of grey model [C],//Proceedings of 2007 IEEE international conference on grey systems and intelligent services, Novermber 18-20,2007,Nanjing,China: 602-606 [127] JI Peirong,Luo Xianju, Zou Hongbo, A study on properties of GM(1,1) Model and direct GM(1,1) model[C],//Proceedings of 2007 IEEE International conference on grey systems and intelligent services, November 18-20,2007, Nanjing China:399-403 [128] LI Mao-lin, WEI Yong, DONG Yan-qiu, A new Method to estimate parameter of grey model [C],//Proceedings of 2007 IEEE International conference on grey systems and intelligent services, November 18-20,2007, Nanjing China:619-623 [129] Ping Xueliang,Zhou Rurong, Liu Shenglan, Two-dimensional data sequence and its grey generation[C],//2004 IEEE international conference on systems,man and cybernetics:2425-2430 [130] Junfeng Li,Aiping Yang, Wenzhan Dai, New approach of building GM(1,1) background value and its application[C],//Proceedings of the IEEE international conference on automation and logistics, August 18-21,2007, Jinan,China:16-19 [131] Changyi Zhang,Wenzhan Dai, Improverment of background value and its application in Non-equidistance GM(1,1) modeling[C],//Prceedings of the 7th·world congress on intelligent control and automation, June 25-27,2008,Chongqing,china:209-212 [132] Songyun Xie, Pengwei Wang,Yubin Xie, New image denoising algorithm based on improved grey prediction model[C],'//2008 congress on image and. signal Processing.:367-371 [133] Junfeng Li, Wenzhan DAI, research of image edge detection and application based on grey predication model[C],//Proceedings of the 2007 international conference on Wavelet analysis and pattern recognition, Beijing, China,2-4 Nov.2007:286-291 [134] Dongzhu Feng,Xin Wang, Bin zhang, A Novel Filter Based on the Grey Modeling[C], //ASID 2008:249-253 [135] Zhiyu zhou,Jianxin Zhang, Object Detection and tracking based on adaptive canny operator and GM(1,1) Model[C],//Proceedings of 2007 IEEE International conference on grey systems and intelligent services, November 18-20,2007, Nanjing China: 434-439[136]胡鹏,付仲良,李炳生,基于灰预测模型的边缘检测新方法[J],计算机工程,2006,32(22)：175-177[137]谢松云,王鹏伟,谢玉斌,基于灰预测模型的图像边缘检测新方法研究[J],西北工业大学学报,2008,26(5)：603-606[138]何仁贵,灰色预测模型在数字图像处理中的应用[D],西安：西北工业大学,2005.
[139]谢松云,谢玉斌,杨威,等.基于GM(1,1)图像边缘检测改进算法研究[J].西北工业大学学报,2009,27(6)：817-821
[140]谢松云,张坤,张海军,基于灰预测的图象去噪新方法[J],微电子学与计算机,2006(12),151-153
[141]沈秀娥,高等级公路沥青混凝土路面裂缝原因分析与防治措施[J],四川建材,2009(4)：47-50
[142]胡雅琴,断裂力学理论在路面反射裂缝分析中的应用[J],西安公路学院学报,1990,10(2)：15-20
[143]谢晨之,汤伟,基于力学分析法的沥青路面疲劳开裂研究[J],公路与汽运,2008(4)：82-84
[144]啜二勇,国外路面自动检测系统发展综述[J],交通标准化,2009(17)：96-99
[145]啜二勇,国内路面自动检测系统研究历程及展望[J],中国高新技术企业,2009(19)：195-196
[146]高建贞,基于图像分析的道路病害自动检测研究[D],南京：南京理工大学,2003：4
[147]郭玉堂,吕皖丽,罗斌,基于模糊熵和结构特征的边缘检测方法[J],华南理工大学学报(自然科学版),2008,36(5)：89-94
[148]唐松云,陈绵云,插入值方法的灰色建模[J],武汉理工大学学报,2005,27(2)：87-89
[149]黄晨华,谢存禧,张铁,基于灰模型白化响应的边缘检测算法[J],华南理工大学学报(自然科学版),2008,36(8)：33-36
[150]Abdel-Ouahab BOUDRAA and El-Hadji Samba DIOP, Image contrast enhancement based on 2D teager-kaiser operator[C],//ICIP2008:3180-3183
[151]A. Beghdadi and A. L. Negrate, Contrast enhancement technique based on local detection of edges [J], Comput.Vis. Graph. Imag. Proc., vol.46,1989:162-174,
[152]高岚,基于模糊边缘判决的自适应图像增弹算法[D],武汉：武汉科技大学,2004：18-25
[153]Y Nakagawa, A Rosenfeld. A note on the use of local min and max operations in digital picture processing [J]. IEEE Trans, Syst, Man, Cybern,1978,8(8):632-635
[154]李久贤,孙伟,夏良正,一种新的模糊对比度增强算法[J].南京：东南大学学报(自然科学版),2004,34(5)：675-677
[2]王保平,基于模糊技术的图像处理方法研究[D],西安：西安电子科技大学,2004.
[3]何靓俊,基于图像处理的沥青路面裂缝检测系统研究[D],西安：长安大学,2008：17-35
[4]田恩杰,高等级公路路面病害自动检测方法研究[D],长春市：吉林大学,2007：3-9,23-24
[5]楼竞,基于图像分析的路面病害检测方法与系统开发[D],南京：南京理工大学,2008：2-3,14-15
[6]张娟,沙爱民,孙朝云等,路面裂缝自动识别的图像增强技术[J],中外公路,2009,29(4)：304-305
[7]张岐山,郭喜江,邓聚龙,灰关联熵分析方法[J],系统工程理论与实践,1996(8)：7-11
[8]王季方,卢正鼎,模糊控制中隶属度函数的确定方法[J],河南科学,2000,18(4)：348-351
[9]陈利利.基于多尺度图像分析的路面病害检测方法研究与分析[D].南京：南京理工大学,2009
[10]Li L,Chan P, Rao A. Flexible pavement distress evaluation using image analysis[J],1993(119):402-418
[11]Liu, Fanfan; Xu, Guoai; Yang, Yixian, et al. Novel approach to pavement cracking automatic detection based on segment extending[C].//Proceedings-2008 International Symposium on Knowledge Acquisition and Modeling, Inst. of Elec. and Elec. Eng. Computer Society, Wuhan 2008:610-614
[12]Grivas D A, Bhagvati C, Skolnick M M. Feasibility of automating pavement distress assessment using mathematical morphology [J]. Transportation Research Record. 1994(1435):52-58.
[13]Li, Li, Chan, P, Lytton, R L. DETECTION OF THIN CRACKS ON NOISY PAVEMENT IMAGES. Transportation Research Recora [J],1991(1311):131-135
[14]Yaxiong Huang, Bugao Xu, Automatic inspection of pavement cracking distress. Journal of electronic imaging [J],15(1),013017, Jan-Mar 2006
[15]Ying L, Salari E, Beamlet transform based technique for pavement image processing and classification[C].//Proceedings of 2009 IEEE International conference on Electro/information technology. Windsor ON.7-9 June,2009:141-145
[16]Sun, Y. Salari, E. Chou, E. Automated pavement distress detection using advanced image processing techniques [C]//Proceedings of 2009 IEEE International conference on Electro/information technology. Windsor ON.7-9 June,2009:373-377
[17]王刚,王娟,王德华等,基于Contourlet变换域统计模型的路面图像去噪算法[J],光电子激光,2009,20(10)：1394-1398
[18]李刚,贺昱曜,赵妍,基于大津法和互信息量的路面破损图像自动识别算法[J],微电子学与计算机,2009,26(7)：241-247
[19]朱其刚,刘明,杨峰.基于区域特征的路面裂缝图像滤噪算法[J],机器视觉,2005(11)：118-120
[20]张宏,英红,沥青路面裂缝图像识别技术研究进展[J],华东公路,2009(4)：81-84
[21]Siriphan Jitprasithsir.i Development of a new digital pavement image processing algorithm for unified crack index computation[D]. A Aissertation Submitted to the Faculty of theUniversity ofUtah,1997
[22]谢昌荣,张郭晶,路面裂缝检测图像处理算法的研究,中外公路[J],2009,29(6)：112-115
[23]孙波成,邱延峻,路面裂缝图像处理算法研究[J],公路交通科技,2008,25(2)：64-68
[24]MEIGNEN D, BERNADET M, BRIAND H·One Application of Neural Networks for Detection of Defects Using Video Data Bases:Identification of Road Distresses [J]·//Proceedings Database and Expert Systems Applications,1997,9 (8):459-464-
[25]CHENGHD, SHI X J, GLAZIER C·Real-time ImageThresholding Based on Sample Space Reduction and Interpolation Approach [J]-American Society of Civil Engineers (ASCE),2003,17 (4):264-272-
[26]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
[27]Egemen Teomete, Viren R Amin,Halil Ceylan.Digital image processing for pavement distress analyses[C].//:Proceesdings of the 2005 Mid-Continent Transportation Research Symposium.2005:1-13.
[28]Kitter J, Illingworth J. Minimum error thresholding [J]. Pattern Recognition, 1986(1):41-47
[29]Koutsopoulos H N, Downey A B. Primitive-based classification of pavement cracking images [J]. Journal of Transportation Engineering,1993(3):402-418
[30]赵明宇,孙立车,一种路面损坏状况的数字图像识别方法[J],交通与计算机,2007(6)：13-15
[31]Georgopoulos A,Loizos A,Flouda A. Digital image processing as a tool for pavement distress evaluation[J]. ISPRS Journal of Photogrammetry and Remote ,Sensing,1995,50(1):23-33
[32]李晋惠,用图像处理的方法检测公路路面裂缝类病害[J],长安大学学报(自然科学版),2004(3)：24-29
[33]陈先桥,严新平,初秀民,用于带阴影路面图像增强的处理方法[J],计算机工程与应用,2008,44(33)：188-190
[34]Juan Wang, Qun Wan, Anmin Huang, A Pavement Distress Survey Algorithm With Novel Models And Line Points Detection[C].//2009 2nd International Conference on Power Electronics and Intelligent Transportation System,2009:140-143
[35]Wei Wei, Boying Liu, Peng Bai, Automatic Road Crack Image Preprocessing for Detection and Identification[C],//2009 Second International Conference on Intelligent Networks and Intelligent Systems,2009:319-322
[36]L. Ying and E. Salari, Beamlet Transform Based Technique for Pavement Image Processing and Classification[C],//IEEE International Conference on Electro/Information Technology,2009:141-145
[37]Siwaporn Sorncharean and Suebskul Phiphobmongkol, Crack Detection on Asphalt Surface Image Using Enhanced Grid Cell Analysis[C],//4th IEEE International Symposium on Electronic Design, Test.& Applications,2008.10:49-54
[38]Chang-Xia Ma, Chun-Xia Zhao, Ying-kun Hou, Pavement Distress Detection Based on Nonsubsampled Contourlet Transform[C],//2008 International Conference on Computer Science and Software Engineering,2008.10:28-31
[39]Fanfan Liu, Guoai Xu, Yixian Yang, Xinxin Niu, Yuli Pan, Novel Approach'to Pavement Cracking Automatic Detection Based on Segment Extending[C],//2008 International Symposium on Knowledge Acquisition and Modeling,2008:610-614
[40]LI Qingquan, LIU Xianglong, Novel Approach to Pavement Image Segmentation Based on Neighboring Difference Histogram Method[C],//2008 Congress on Image and Signal Processing,2008:792-796
[41]Bing Ma, Sridhar Lakshmanan, Alfred 0. Hero, Pavement Boundary Detection Via Circular Shape Models[C],//Proceedings of the IEEE intelligent Vehicles Symposium 2000 Dearborn (MI), USA October 3-5,2000:644-649
[42]Bing Ma, Sridhar Lakshmanan, and Alfred O. Hero, Simultaneous Detection of Lane and Pavement Boundaries Using Model-Based Multisensor Fusion[J], IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL.1, NO. 3,SEPTEMBER 2000:135-147
[43]HOU Xiang-dan, DONG Yong-feng,GUO Hai-jiao,YANG Xin, The Method of Pavement Image Splicing Based on SIFT Algorithm[C],//Global Congress on Intelligent Systems,2009:538-542
[44]S K Pal, R A King. Image Enhancement Using Fuzzy Sets[J]. Electron. Lett.,1980, 16(9):376-378.
[45]Pal S K, King R A. Image Enhancement Using Smoothing with Fuzzy Sets[J]. IEEE Trans. Syst. Man. Cybern.,1981,11(7):494-501.
[46]李弼程,郭志刚,文超,图像的多层次模糊增强与边缘提取[J],模糊系统与数学,2000,14(4)：77-83
[47]闫茂德,伯绍波,李雪等,一种自适应模糊的局部区域图像增强算法[C],//Proceedings of the 26th Chinese Control Conference July 26-31,2007, Zhangjiajie, Hunan, China:
[48]付忠良,图像阈值选取方法-OTSU方法的推广[J],计算机应用,2000,20(5)：37-39
[49]王保平,刘升虎,范九伦等,基于模糊熵的自适应图像多层次模糊增强算法[J],电子学报,2005,33(4)：730-734
[50]宋洁,孙荣艳,张永杰,李娜娜,基于金字塔和模糊聚类的路面图像拼接方法[J],河北工业大学学报,2008,37(5)：13-19
[51]杜文靖,朱少辉,刘玉臣,高等级沥青公路路面裂缝图像处理技术[J],建设机械技术与管理,2006(3)：65-68
[52]刘玉臣,王国强,林建荣,基于模糊理论的路面裂缝图像增强方法[J],筑路机械与施工机械化,2006(2):35-37
[53]Cheng H D, Rong Jim. Novel approach to pavement cracking detection based on fuzzy set theory [J]. ASCE.1999(13):270-280.
[54]Peggy Subirats, Jean Dumoulin, Vincent Legeay and Dominique Barba, AUTOMATION OF PAVEMENT SURFACE CRACK DETECTION USING THE CONTINUOUS WAVELET TRANSFORM[C],//ICIP2006:3037-3040
[55]左永霞,高速公路路面破损图像识别技术研究[D],沈阳：吉林大学,2008
[56]Yongxia Zuo, Guoqiang Wang, Chuncheng Zuo, Wavelet Packet Denoising for Pavement Surface Cracks Detection[C],//2008 International Conference on Computational Intelligence and Security,2008:481-484
[57]初秀民,王荣本,储江伟,王超,基于不变矩特征的沥青路面破损图像识别[J],吉林大学学报(工学版),2003,33(1)：1-7
[58]王珣,图像分割技术及其在路面开裂损坏识别中的应用[J],计算机工程,2003,29(17)：117-119
[59]Kaseko Mohamed S,Ritchie,Stephen G.Pavement image processing using neural networks[C].//Proceedings of the 2nd International Conference on Applications of Advanced Technologies in Transportation Engineering,1991:18-21.
[60]Chou J, Cheng H D.Pavement distress classification using neural networks[C].//Proceedings,IEEE International Conference on Systems,Man and Cybernetics,1994:397-401.
[61]Guoai Xu, Jianli Ma, Fanfan Liu, Xinxin Niu, Automatic Recognition of Pavement Surface Crack Based on BP Neural Network[C],//2008 International Conference on Computer and Electrical Engineering,2008:19-22
[62]张雷,肖梅,马建,宋宏勋,基于视觉模型的路面裂缝检测算法[J],长安大学学报(自然科学版),2009(5)：21-24
[63]闫茂德,伯绍波,贺昱曜,一种基于形态学的路面裂缝图像检测与分析方法[J],工程图学学报,2008(2)：142-147
[64]伯绍波,闫茂德,孙国军,贺昱曜,沥青路面裂缝检测图像处理算法研究[J],微计算机信息,2007,23(4-3)：280-282
[65]Perona P, Malik J. Scale-space and edge detection using anisotropic Diffusion[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1990,12(7):629-639.
[66]Weickert J. Coherence enhancing diffusion filtering[J]. International Journal ofComputerVision,1999,31(2/3):111-127.
[67]Yongxia Zuo, Guoqiang Wang, Chuncheng Zuo, A Novel Image Segmentation Method of Pavement Surface Cracks Based on Fractal Theory[C]//2008 International Conference on Computational Intelligence and Security,2008:485-488
[68]Philippe Delanes and Dominique Barba. A Markov random field for rectilinear structure extraction in pavement distress image analysis[C],//IEEE.Internat.;Conf. on Image Processing, vol.1,1995:446-449
[69]Nana Li, Xiangdan Hou, Xinyu Yang, Yongfeng Dong, Automation Recognition of Pavement Surface Distress Based on Support Vector Machine[C],//2009 Second International Conference on Intelligent Networks and Intelligent Systems.2009:346-349
[70]唐磊,基于图像分析的路面病害自动检测[D],南京：南京理工大学,2007
[71]马常霞,赵春霞,胡勇,结合NSCT和图像形态学的路面裂缝检测[J],计算机辅助设计与图形学学报,2009,21(12)：1761-1767
[72]王士清,韩力群,用于公路路面状况自动检测的图像处理技术[J],科技摘报开发与经济,2003(8)
[73]张宏,英红,沥青路面裂缝图像识别技术研究进展[J],华东公路,2009(4)：81-84
[74]刘思峰,党耀国,方志耕,灰色系统理论及其应用(第三版)[M],北京：科学出版社,2005.6
[75]肖新平,宋中民,李峰,灰技术基础及其应用[M],北京：科学出版社,2005.8
[76]谢乃明,灰色系统建模技术研究[D],南京：南京航空航天大学,2008.
[77]章毓晋,图象处理和分析[M],北京：清华大学出版社,1999.3
[78]王耀南,李树涛,毛建旭,计算机图像处理与识别技术[M],2001.6
[79]胡隽,何辅云,贺静,灰色系统理论在图像处理中的应用[J],电视技术,2003(7)：19-22
[80]马苗,郝重阳,黎新伍,图像灰处理技术研究综述[C],//Proceedings of the 5th World Congress on Intelligent Control and Automation, June 15-19.2004, Hangzhou. P.R. China,Vol.5:4141-4145
[81]马苗,田红鹏,张艳宁,灰色理论在图像工程中的应用研究进展[J],中国图象图形学报,Vol.12(11),2007.11：1943-1951
[82]路瑶,王晶,胡蕾,灰色系统理论在图像处理中的应用综述[J],自动化技术与应用,2007(6),49-52
[83]温丽华,灰色系统理论及其应用[D],哈尔滨：哈尔滨工程大学,2003
[84]鲁胜强,基于灰色系统理论的图像去噪算法研究[D],武汉：武汉理工大学,2008
[85]李雪莲,灰色系统理论及其在医学图像处理中的应用[D],哈尔滨：哈尔滨工程大学,2005
[86]魏丽,陈丽宇,关于一类新的广义灰色关联度[J],宁夏大学学报(自然科学版),2007,28(1),：22-24
[87]曹明霞,党耀国,张蓉等,对灰色关联度计算方法的改进[J],统计与决策,2007(4)：29-30
[88]东亚斌,段志善,灰色关联度分辨系数的一种新的确定方法[J],西安建筑科技大学学报(自然科学版),2008,40(4)：589-592
[89]田民,刘思峰,卜志坤,灰色关联度算法模型的研究综述[J],统计与决策,2008(1)：24-27
[90]田红鹏,马苗,灰色关联分析在工程图纸除噪预处理中的应用研究[J],计算机应用研究,2007,24(4)：198-200
[9l]马苗,樊养余,谢松云等,基于灰色系统理论的图象边缘检测新算法[J],中国图象图形学报,2003,8(A)(10)：1136-1139
[92]郑子华,陈家祯,陈利永,基于灰色绝对关联度的边缘检测算法[J],福建师范大学学报(自然科学版),2004,20(4)：20-23
[93]胡鹏,傅仲良,陈楠,利用灰色理论进行图像边缘检测[J],武汉大学学报信息科学版,2006,31(5)：411-414
[94]李俊峰,杨瑷萍,戴文战等,基于灰色绝对关联度和LOG算子的图像边缘检测算法研究[C],//Proceedings of the 26th Chinese control conference, July 26-31, 2007, Zhangjiajie,Hunan, China:480-484
[95]韩学平,芮筱亭,赵革,多决策目标排序的层次分析与灰色关联法[J],火力与指挥控制,2009,34(9)：80-83
[96]Miao Ma, Hongpeng Tian, Chongyang Hao, Image Denoising Using Grey Relational Analysis in Spatial Domain[C],//Visual Communications and Image Processing 2005:335-342
[97]陶剑锋,基于灰色系统理论的数字图像处理算法研究[D],武汉：武汉理工大学,2004
[98]褶乃强,基于灰色理论的图像边缘检测算法研究[D],武汉：武汉理工大学,2009
[99]冯冬竹,阎杰.一种基于灰关联分析的红外图像滤波算法[J],.西北工业大学学报,2006,24(6)：710-712
[100]冯冬竹,阎杰,基于灰关联分析的红外图像边缘检测算法[J],红外技术,2006,28(3)：161-164
[101]MA Miao, ZHANG Yanning, SUN Li, et al. SAR Image Despecking Using Grey System Theory[C],//Proceedings of 2007 IEEE International Conference on Grey Systems and Intelligent Service,November 18-20,2007, Nanjing,China:458-462
[102]Dongzhu Feng, Jie Yan and Panfeng Huang, A Novel De-noise method Based on the Grey Relational Analysis[C],//Proceedings of the 2006 IEEE International Conference on Information Acquisition, August 20-23,2006, Weihai,Shandong,China:1385-1389
[103]Dongzhu Feng, Xin Wang, Yuhe Liu, An edge detection method for infrared image based on grey relational analysis[C],//2nd International Symposium on Systems and Control in Aerospace and Astronautics,2008.:1-5
[104]Ziyang Zhen, Zhou Gu, and Yuanyuan Liu, A Novel Fuzzy Entropy.Image Segmentation Approach Based on Grey Relational Analysis[C],//Proceedings of 2007 IEEE International Conference on Grey Systems and Intelligent Services, November 18-20,2007, Nanjing, China:1019-1022
[105]Li Guoliang, Fu Qiang, Grey Relational Analysis Model Based on Weighted Entropy and Its Application[C],//International Conference on Wireless Communications, Networking and Mobile Computing,2007:5500-5503
[106]王博,熵理论在证券投资中的模型及应用研究[D],合肥：合肥工业大学,2008.
[107]程亮,最大熵原理与最小熵方法在测量数据处理中的应用[D],成都：电子科技大学,2008
[108]周桂萍,基于熵AHP法的经济发展评价[D],大连：大连理工大学,2007.
[109]高跃文,基于熵-灰色关联-理想解法的城市道路建设时序规划研究[D],北京：北京交通大学,2009.
[110]刘勇,基于热力学熵和混沌理论的城市道路交通系统的研究[D],西安：长安大学,2009.
[111]孙淑绒,基于熵的深海资源图像处理算法研究与应用[D],长沙：中南大学,20008.
[112]赵凤,基于模糊熵理论的若干图象分割方法研究[D],西安：西安邮电学院,2007.
[113]吴成茂,模糊熵的修改及其在图像分割中的应用[J],微电子学与计算机,2008,25(1)：25-28
[114]Zhang Qishan, HanWeiyou, DengJulong, Information Entropy of Discrete Grey number[J], Journal of Grey Systems,1994,6(4):303-314
[115]张岐山,李锡纯,邓聚龙,不确定型决策的灰熵方法[J],决策借鉴,1995(6)：37-39
[116]杨玉中,吴立云,张强,基于灰熵的不确定型决策方法及其应用[J],工业工程与管理,2006(2)：92-94
[117]骆文辉,杨建军,基于灰熵方法的综合评估[J],指挥控制与仿真,2008,30(2)：74-77
[118]李朝霞,牛文娟,系统多层次灰色熵优选理论及其应用[J],系统工程理论与实践,2007(8)：49-55
[119]Miao Ma, Yanning Zhang, Hongpeng Tian, et al. A Fast SAR Image Segmentation Algorithm based on Particle Swarm Optimization and Grey Entropy[C],//Fourth International Conference on Natural Computation,2008,4:8-12
[120]Ma Lihua, Yan Huizhe, Study on Gray Entropy Comprehensive Evaluation Model of the Economic Benefit in Mining Enterprises[C],//ICCSIT2009:317-320.
[121]YANG Yuzhong, WU Liyun, Grey Entropy Method for Green Supplier Selection[C],//WICOM2007:4682-4685
[122]Guan Yeqing; Liu Si-Feng, An Approach of the Grey Modeling Based on Cotangent Function Transformation [C],//4th International Conference on Wireless Communications, Networking and Mobile Computing,2008:1-4
[123]Cuifeng Li, Wenzhan Dai, GM(1,1) Model and Its Application Based on Transformation Function sinx[C].//Proceedings of the 6th world congress on intelligent control and automation, June 21-23,2006,Dalian,China:4682-4685
[124]Xin-ping XIAO, SHI-KUAN TONG, SHU-HUA MAO, research on ill-condition problem in grey prediction control model[C],//Proceedings of the third international conference on machine learning and cybernetics, Shanghai,26-29 August 2004:2010-2013
[125]毛树华.灰色预测控制模型的病态性研究[D],武汉：武汉理工大学,2004. [126] Dou YU, Yong WEI, New algorithms of initial development coefficients and optimization of grey model [C],//Proceedings of 2007 IEEE international conference on grey systems and intelligent services, Novermber 18-20,2007,Nanjing,China: 602-606 [127] JI Peirong,Luo Xianju, Zou Hongbo, A study on properties of GM(1,1) Model and direct GM(1,1) model[C],//Proceedings of 2007 IEEE International conference on grey systems and intelligent services, November 18-20,2007, Nanjing China:399-403 [128] LI Mao-lin, WEI Yong, DONG Yan-qiu, A new Method to estimate parameter of grey model [C],//Proceedings of 2007 IEEE International conference on grey systems and intelligent services, November 18-20,2007, Nanjing China:619-623 [129] Ping Xueliang,Zhou Rurong, Liu Shenglan, Two-dimensional data sequence and its grey generation[C],//2004 IEEE international conference on systems,man and cybernetics:2425-2430 [130] Junfeng Li,Aiping Yang, Wenzhan Dai, New approach of building GM(1,1) background value and its application[C],//Proceedings of the IEEE international conference on automation and logistics, August 18-21,2007, Jinan,China:16-19 [131] Changyi Zhang,Wenzhan Dai, Improverment of background value and its application in Non-equidistance GM(1,1) modeling[C],//Prceedings of the 7th·world congress on intelligent control and automation, June 25-27,2008,Chongqing,china:209-212 [132] Songyun Xie, Pengwei Wang,Yubin Xie, New image denoising algorithm based on improved grey prediction model[C],'//2008 congress on image and. signal Processing.:367-371 [133] Junfeng Li, Wenzhan DAI, research of image edge detection and application based on grey predication model[C],//Proceedings of the 2007 international conference on Wavelet analysis and pattern recognition, Beijing, China,2-4 Nov.2007:286-291 [134] Dongzhu Feng,Xin Wang, Bin zhang, A Novel Filter Based on the Grey Modeling[C], //ASID 2008:249-253 [135] Zhiyu zhou,Jianxin Zhang, Object Detection and tracking based on adaptive canny operator and GM(1,1) Model[C],//Proceedings of 2007 IEEE International conference on grey systems and intelligent services, November 18-20,2007, Nanjing China: 434-439[136]胡鹏,付仲良,李炳生,基于灰预测模型的边缘检测新方法[J],计算机工程,2006,32(22)：175-177[137]谢松云,王鹏伟,谢玉斌,基于灰预测模型的图像边缘检测新方法研究[J],西北工业大学学报,2008,26(5)：603-606[138]何仁贵,灰色预测模型在数字图像处理中的应用[D],西安：西北工业大学,2005.
[139]谢松云,谢玉斌,杨威,等.基于GM(1,1)图像边缘检测改进算法研究[J].西北工业大学学报,2009,27(6)：817-821
[140]谢松云,张坤,张海军,基于灰预测的图象去噪新方法[J],微电子学与计算机,2006(12),151-153
[141]沈秀娥,高等级公路沥青混凝土路面裂缝原因分析与防治措施[J],四川建材,2009(4)：47-50
[142]胡雅琴,断裂力学理论在路面反射裂缝分析中的应用[J],西安公路学院学报,1990,10(2)：15-20
[143]谢晨之,汤伟,基于力学分析法的沥青路面疲劳开裂研究[J],公路与汽运,2008(4)：82-84
[144]啜二勇,国外路面自动检测系统发展综述[J],交通标准化,2009(17)：96-99
[145]啜二勇,国内路面自动检测系统研究历程及展望[J],中国高新技术企业,2009(19)：195-196
[146]高建贞,基于图像分析的道路病害自动检测研究[D],南京：南京理工大学,2003：4
[147]郭玉堂,吕皖丽,罗斌,基于模糊熵和结构特征的边缘检测方法[J],华南理工大学学报(自然科学版),2008,36(5)：89-94
[148]唐松云,陈绵云,插入值方法的灰色建模[J],武汉理工大学学报,2005,27(2)：87-89
[149]黄晨华,谢存禧,张铁,基于灰模型白化响应的边缘检测算法[J],华南理工大学学报(自然科学版),2008,36(8)：33-36
[150]Abdel-Ouahab BOUDRAA and El-Hadji Samba DIOP, Image contrast enhancement based on 2D teager-kaiser operator[C],//ICIP2008:3180-3183
[151]A. Beghdadi and A. L. Negrate, Contrast enhancement technique based on local detection of edges [J], Comput.Vis. Graph. Imag. Proc., vol.46,1989:162-174,
[152]高岚,基于模糊边缘判决的自适应图像增弹算法[D],武汉：武汉科技大学,2004：18-25
[153]Y Nakagawa, A Rosenfeld. A note on the use of local min and max operations in digital picture processing [J]. IEEE Trans, Syst, Man, Cybern,1978,8(8):632-635
[154]李久贤,孙伟,夏良正,一种新的模糊对比度增强算法[J].南京：东南大学学报(自然科学版),2004,34(5)：675-677