基于纹理分析的视觉检测方法与应用研究
|
摘要
机器视觉是用计算机来模拟人的视觉功能,从客观事物的图像中提取信息,进行处理并加以理解,最终用于实际检测、测量和控制的技术。该技术具有显著优势和良好的发展前景,已经取得了广泛的应用。视觉检测是机器视觉理论与技术在检测领域的应用。纹理表面缺陷检测是视觉检测中的重点和难点。论文围绕纹理图像分析及应用,深入研究了纹理特征描述方法、纹理表面缺陷检测方法、纹理分析在视觉检测中的应用等问题。主要内容及创新点如下：
将离散序列的列率特性应用于图像纹理特征描述,提出一种基于局部Walsh谱的多尺度旋转不变纹理特征描述方法。视觉检测中图像旋转会给特征提取带来较大干扰,且单一尺度纹理特征无法全面有效地描述待测物表面纹理。利用离散Walsh变换功率谱的循环移位不变性,实现了图像纹理特征旋转不变性,同时可以有效提取不同尺度纹理特征。基于序列的列率特性,构造了新的两族局部Walsh谱,并揭示了局部Walsh谱与局部二值模式之间的联系。纹理分类和纹理分割实验结果表明：局部Walsh谱具有较好的纹理鉴别能力和良好的旋转不变性,且实现简单,计算量小等优点。
研究用复数小波系数相位信息描述图像纹理特征,提出一种结合双树复数小波系数幅度和相位统计分布模型的特征提取方法。双树复数小波相比传统实数小波的显著特征之一是能够提供相位信息,但在图像纹理分析中小波系数相位信息的利用是一个难点。首先对双树复数小波各子带系数的相位和幅度进行局部差分运算,然后利用循环正态分布和广义高斯分布分别描述子带系数相位和幅度的边缘统计特性,最后用各方向子带系数统计分布模型参数组成特征向量描述纹理特征。纹理分类实验结果表明：综合利用双树复数小波系数幅度和相位的边缘统计分布可以有效描述纹理特征,分类效果显著高于基于实数离散小波的纹理特征提取方法。
从图像空域特征出发,研究利用单类分类的统计方法检测纹理表面缺陷。针对单类分类器模型参数优化选择难的问题,提出一种基于体积占空比的参数优化选择方法,用于支持向量数据描述分类器的核参数选择。通过计算样本影响区域体积和分类域体积之比,得到单类分类器的体积占空比。利用分类器体积占空比描述分类器边界对目标样本分布的拟合程度,指导分类器参数的优化选择。在此基础上,采用支持向量数据描述等单类分类器对砂纸、瓷砖、铸件等随机纹理表面缺陷进行检测。实验结果表明：采用体积占空比可以有效指导支持向量数据描述核参数的优化选择；通过对正常纹理表面样本学习,单类分类器能够有效检测出随机纹理表面缺陷。
从纹理图像频谱特性出发,研究利用频域特征消除图像纹理背景,同时增强局部异常的途径,提出一种基于双树复数小波重构的纹理表面缺陷检测方法。在分析纹理图像双树复数小波各子带系数共生矩阵特性的基础上,设计了分解层数确定和重构系数选择的算法。通过选择合适的分解层数,消除含纹理信息丰富的细节系数,然后进行小波重构,将复杂的纹理表面缺陷检测问题转化为无纹理表面缺陷检测,利用简单的阈值法即可完成检测。实验结果表明,这种方法适合用于各向异性(规则性)纹理表面的缺陷检测,检测效果优于现有的基于实数离散小波的方法。
以某型航空发动机均压孔径向裂纹视觉检测为背景,研究了圆形轮廓检测和孔类部件径向裂纹识别方法。为了保证圆检测的准确性和可靠性,同时降低Hough变换的计算量,提出一种模糊快速Hough变换算法。采用局部梯度信息降低参数空间的维数,对参数空间进行多分辨率分级,由粗到精逐步细化参数空间,减少无用的积累矩阵,降低计算的复杂度,采用模糊投票的方法减少边缘像素位置和梯度方向等误差对检测准确性的干扰。针对孔类部件径向裂纹的视觉检测,提出基于链码分析和基于纹理分析的两种裂纹识别方法,并对两种方法进行实验验证和比较。实验结果表明,模糊快速Hough变换算法可以快速准确地检测出待测目标的圆形轮廓；基于链码分析的方法和基于纹理分析的方法的检测准确率分别为91.2%和95%,由于基于纹理分析的方法综合利用了区域内边缘的密度和方向,其准确率更高,受反光和阴影的干扰小。
针对飞机骨架构件铆接部位、操纵钢索等不易接近且表面特征复杂部位的外场检测需求,设计并实现了相关的基于纹理分析的视觉检测算法。对骨架构件铆接部位表面异常检测时,采用LWS和Radon变换标准差描述铆接兴趣区域的纹理特征,实现图像的旋转不变和缩放不变,同时抑制光照引起的图像灰度等级变化的影响,利用k-NN单类分类器实现检测。对操纵钢索纹理表面缺陷检测时,用纹理分割方法识别钢索边界,并确定兴趣区域,采用一致性检验法和双树复数小波重构法进行检测。实验结果表明：基于纹理分析的视觉检测方法可有效地检测出飞机骨架构件铆接部位和操纵钢索表面的异常与缺陷；该视觉无损检测系统能延伸人眼视距,增大检测的可达范围,对特定部位能够利用机器视觉检测技术辅助检测人员做出判断。以上研究结果表明,在无损检测中应用机器视觉检测技术,可以克服目视检测的局限性,增大检测的可达范围,提高检测的准确性和自动化程度。
Machine vision is a technology of simulating human vision to detect, measure and control objects by using computer image processing systems. It has great advantage and development prospect, and has been widely used in the fields of industry, agriculture, military affairs, and national defense and so on. Vision inspection is an application in detection area by means of machine vision theory and technique. Moreover, texture defect detection is one of important and difficult problems in vision inspection. Several key techniques about texture feature extraction method, texture defect detection method and their applications in real vision inspection systems are studied in this paper. The brief structure of the research and the novel approaches are as follows:
The sequency characteristics of sequence are used to describe the image texture. A new multiresolution and rotation invariant texture descriptor is proposed based on the Local Walsh Spectrum (LWS). In vision inspection, the results are frequently affected by interference of image rotation. Moreover, one-scale texture descriptors can not represent the surface characteristic of detecting targets. The rotation invariant of the proposed texture descriptors can be achieved because of circular-shift-invariant of the discrete Walsh transform power spectrum. Simultaneously, the multiresolution texture features can be obtained by LWS. Furthermore, based on the sequency characteristic, the two-family sequency LWS (TSLWS) descriptor is proposed, and the relationship between LWS and Local Binary Pattern (LBP) is revealed. The results of texture classification and segmentation experiments show that LWS and TSLWS have the satisfying texture discrimination performance and rotation invariant.
The utilization of the phase information of complex wavelet coefficient is studied in image texture feature extraction firstly. A novel method of texture description is presented combining the coefficient phase and amplitude of Dual-Tree Complex Wavelet Transform (DT-CWT). With overcame the shortcomings of classical real Discrete Wavelet Transform (DWT), DT-CWT can express the phase information of decomposition coefficient. But it is difficulty that how to use the phase information to describe image texture. Firstly, a local variance operator is implemented on the amplitude and phase respectively. Secondly, the circular normal distribution and generalized Gaussian distribution are adopted to describe the phase and amplitude. Finally, the texture eigenvectors are composed of the parameters of statistic models. The results of texture classification experiments show that the proposed method can describe texture effectively, and its texture discriminability is better than the traditional methods based on DWT.
Based on the spatial features of texture image, the statistical method of texture defect detection is studied using the one-class classifier. Support vector data description (SVDD) is a robust one-class classification method. However, its performance is strongly under the influence of kernel parameter selection. A novel parameter-optimizing method is proposed based on the volume duty ratio (VDR) of the one-class classifier. VDR can effectively estimate the degree for the SVDD classifier's boundary to be closes to the distribution of object sample. Several texture defect detection experiments on statistical texture surfaces, such as sandpaper, tile, casting, are implemented based on the one-class classifier. The results of experiments show that the suitable kernel parameter of a classifier can be selected by the proposed method, and the one-class classifier can detect the local defect in texture images only using the normal sample.
The approach of texture defect detection is studied based on the frequency-domain characteristic of texture image. A novel method using DT-CWT reconstruction is presented for the inspection of local defects embedded in homogeneously textured surfaces. By properly selecting the smooth subimages and the detail subimages at different resolution levels for image reconstruction, the global repetitive texture pattern can be effectively removed and only local anomalies are preserved in the restored image. This converts the difficult defect detection in complicated textured images into a simple binary thresholding in non-textured images. Experimental results show that the presented method is suitable for the anisotropic (or regular) texture defect detection, and its performance excels DWT's.
The vision inspection approach of aperture's radial crack on an aero-engine labyrinth disc is studied based on industrial videoscope. The circle detection and the radial crack recognition methods are two key components in the inspection algorithm. In order to improve the speed of circle detection and meet accuracy, a new fuzzy fast Hough transform (FFHT) algorithm is proposed. In the FFHT algorithm, the dimension of the parameter space is reduced using the local gradient information. In the parameter space, the coarse-to-fine search technique is used to reduce the computing and storage requirements of the hough transform. The experimental results show that FFHT can detect the circular contour quickly and accurately. Moreover, the fuzzy vote technique is adopted to lessen the uncertainty that arisen from edge pixel position error and gradient direction error. Moreover, two methods of aperture's radial crack detection are proposed based on chain code analysis and texture analysis respectively. The experiment of crack detection is carried out in aperture images, and the results indicate that the detecting accuracy using chain code analysis is 91.2%, and the accuracy with texture analysis is 95%. The texture analysis method has raised the accuracy of detection using edge density and direction in the local region synthetically.
For inspection the defects on the surface defect of the rivet of aircraft frame and the control wire rope, which can not close easily and have complicated surface pattern, several texture defect detecting algorithms are designed and implemented correspondingly with the detecting mode that is "object detection→region of interest→defect identification". To detect the surface defect of the rivet and the control wire rope respectively. The LWS and Rodan transform are used to describe the texture feature of rivet regions, and the k-NN one-class classifier is adopted to identify the abnormality of rivet. In the inspection of the wire rope, wire rope images are segmented by texture feature firstly, then, the method based uniformity of subregion and the method using DT-CWT reconstruction are adopted to detect the surface defects of wire rope. Moreover, a nondestructive inspection system based on machine vision is developed. The results of detection experiments shows that the vision inspection methods based on texture analysis are effective to detect the surface defects of the rivet and the wire rope. These research results above show that the machine vision technology could be used widely in the nondestructive inspection field. The shortcomings of visual inspection with human eyes could be overcome by the machine vision detection technology. Simultaneously, the effective coverage of detection could be extended, and the accuracy and automation level could be improved.
将离散序列的列率特性应用于图像纹理特征描述,提出一种基于局部Walsh谱的多尺度旋转不变纹理特征描述方法。视觉检测中图像旋转会给特征提取带来较大干扰,且单一尺度纹理特征无法全面有效地描述待测物表面纹理。利用离散Walsh变换功率谱的循环移位不变性,实现了图像纹理特征旋转不变性,同时可以有效提取不同尺度纹理特征。基于序列的列率特性,构造了新的两族局部Walsh谱,并揭示了局部Walsh谱与局部二值模式之间的联系。纹理分类和纹理分割实验结果表明：局部Walsh谱具有较好的纹理鉴别能力和良好的旋转不变性,且实现简单,计算量小等优点。
研究用复数小波系数相位信息描述图像纹理特征,提出一种结合双树复数小波系数幅度和相位统计分布模型的特征提取方法。双树复数小波相比传统实数小波的显著特征之一是能够提供相位信息,但在图像纹理分析中小波系数相位信息的利用是一个难点。首先对双树复数小波各子带系数的相位和幅度进行局部差分运算,然后利用循环正态分布和广义高斯分布分别描述子带系数相位和幅度的边缘统计特性,最后用各方向子带系数统计分布模型参数组成特征向量描述纹理特征。纹理分类实验结果表明：综合利用双树复数小波系数幅度和相位的边缘统计分布可以有效描述纹理特征,分类效果显著高于基于实数离散小波的纹理特征提取方法。
从图像空域特征出发,研究利用单类分类的统计方法检测纹理表面缺陷。针对单类分类器模型参数优化选择难的问题,提出一种基于体积占空比的参数优化选择方法,用于支持向量数据描述分类器的核参数选择。通过计算样本影响区域体积和分类域体积之比,得到单类分类器的体积占空比。利用分类器体积占空比描述分类器边界对目标样本分布的拟合程度,指导分类器参数的优化选择。在此基础上,采用支持向量数据描述等单类分类器对砂纸、瓷砖、铸件等随机纹理表面缺陷进行检测。实验结果表明：采用体积占空比可以有效指导支持向量数据描述核参数的优化选择；通过对正常纹理表面样本学习,单类分类器能够有效检测出随机纹理表面缺陷。
从纹理图像频谱特性出发,研究利用频域特征消除图像纹理背景,同时增强局部异常的途径,提出一种基于双树复数小波重构的纹理表面缺陷检测方法。在分析纹理图像双树复数小波各子带系数共生矩阵特性的基础上,设计了分解层数确定和重构系数选择的算法。通过选择合适的分解层数,消除含纹理信息丰富的细节系数,然后进行小波重构,将复杂的纹理表面缺陷检测问题转化为无纹理表面缺陷检测,利用简单的阈值法即可完成检测。实验结果表明,这种方法适合用于各向异性(规则性)纹理表面的缺陷检测,检测效果优于现有的基于实数离散小波的方法。
以某型航空发动机均压孔径向裂纹视觉检测为背景,研究了圆形轮廓检测和孔类部件径向裂纹识别方法。为了保证圆检测的准确性和可靠性,同时降低Hough变换的计算量,提出一种模糊快速Hough变换算法。采用局部梯度信息降低参数空间的维数,对参数空间进行多分辨率分级,由粗到精逐步细化参数空间,减少无用的积累矩阵,降低计算的复杂度,采用模糊投票的方法减少边缘像素位置和梯度方向等误差对检测准确性的干扰。针对孔类部件径向裂纹的视觉检测,提出基于链码分析和基于纹理分析的两种裂纹识别方法,并对两种方法进行实验验证和比较。实验结果表明,模糊快速Hough变换算法可以快速准确地检测出待测目标的圆形轮廓；基于链码分析的方法和基于纹理分析的方法的检测准确率分别为91.2%和95%,由于基于纹理分析的方法综合利用了区域内边缘的密度和方向,其准确率更高,受反光和阴影的干扰小。
针对飞机骨架构件铆接部位、操纵钢索等不易接近且表面特征复杂部位的外场检测需求,设计并实现了相关的基于纹理分析的视觉检测算法。对骨架构件铆接部位表面异常检测时,采用LWS和Radon变换标准差描述铆接兴趣区域的纹理特征,实现图像的旋转不变和缩放不变,同时抑制光照引起的图像灰度等级变化的影响,利用k-NN单类分类器实现检测。对操纵钢索纹理表面缺陷检测时,用纹理分割方法识别钢索边界,并确定兴趣区域,采用一致性检验法和双树复数小波重构法进行检测。实验结果表明：基于纹理分析的视觉检测方法可有效地检测出飞机骨架构件铆接部位和操纵钢索表面的异常与缺陷；该视觉无损检测系统能延伸人眼视距,增大检测的可达范围,对特定部位能够利用机器视觉检测技术辅助检测人员做出判断。以上研究结果表明,在无损检测中应用机器视觉检测技术,可以克服目视检测的局限性,增大检测的可达范围,提高检测的准确性和自动化程度。
Machine vision is a technology of simulating human vision to detect, measure and control objects by using computer image processing systems. It has great advantage and development prospect, and has been widely used in the fields of industry, agriculture, military affairs, and national defense and so on. Vision inspection is an application in detection area by means of machine vision theory and technique. Moreover, texture defect detection is one of important and difficult problems in vision inspection. Several key techniques about texture feature extraction method, texture defect detection method and their applications in real vision inspection systems are studied in this paper. The brief structure of the research and the novel approaches are as follows:
The sequency characteristics of sequence are used to describe the image texture. A new multiresolution and rotation invariant texture descriptor is proposed based on the Local Walsh Spectrum (LWS). In vision inspection, the results are frequently affected by interference of image rotation. Moreover, one-scale texture descriptors can not represent the surface characteristic of detecting targets. The rotation invariant of the proposed texture descriptors can be achieved because of circular-shift-invariant of the discrete Walsh transform power spectrum. Simultaneously, the multiresolution texture features can be obtained by LWS. Furthermore, based on the sequency characteristic, the two-family sequency LWS (TSLWS) descriptor is proposed, and the relationship between LWS and Local Binary Pattern (LBP) is revealed. The results of texture classification and segmentation experiments show that LWS and TSLWS have the satisfying texture discrimination performance and rotation invariant.
The utilization of the phase information of complex wavelet coefficient is studied in image texture feature extraction firstly. A novel method of texture description is presented combining the coefficient phase and amplitude of Dual-Tree Complex Wavelet Transform (DT-CWT). With overcame the shortcomings of classical real Discrete Wavelet Transform (DWT), DT-CWT can express the phase information of decomposition coefficient. But it is difficulty that how to use the phase information to describe image texture. Firstly, a local variance operator is implemented on the amplitude and phase respectively. Secondly, the circular normal distribution and generalized Gaussian distribution are adopted to describe the phase and amplitude. Finally, the texture eigenvectors are composed of the parameters of statistic models. The results of texture classification experiments show that the proposed method can describe texture effectively, and its texture discriminability is better than the traditional methods based on DWT.
Based on the spatial features of texture image, the statistical method of texture defect detection is studied using the one-class classifier. Support vector data description (SVDD) is a robust one-class classification method. However, its performance is strongly under the influence of kernel parameter selection. A novel parameter-optimizing method is proposed based on the volume duty ratio (VDR) of the one-class classifier. VDR can effectively estimate the degree for the SVDD classifier's boundary to be closes to the distribution of object sample. Several texture defect detection experiments on statistical texture surfaces, such as sandpaper, tile, casting, are implemented based on the one-class classifier. The results of experiments show that the suitable kernel parameter of a classifier can be selected by the proposed method, and the one-class classifier can detect the local defect in texture images only using the normal sample.
The approach of texture defect detection is studied based on the frequency-domain characteristic of texture image. A novel method using DT-CWT reconstruction is presented for the inspection of local defects embedded in homogeneously textured surfaces. By properly selecting the smooth subimages and the detail subimages at different resolution levels for image reconstruction, the global repetitive texture pattern can be effectively removed and only local anomalies are preserved in the restored image. This converts the difficult defect detection in complicated textured images into a simple binary thresholding in non-textured images. Experimental results show that the presented method is suitable for the anisotropic (or regular) texture defect detection, and its performance excels DWT's.
The vision inspection approach of aperture's radial crack on an aero-engine labyrinth disc is studied based on industrial videoscope. The circle detection and the radial crack recognition methods are two key components in the inspection algorithm. In order to improve the speed of circle detection and meet accuracy, a new fuzzy fast Hough transform (FFHT) algorithm is proposed. In the FFHT algorithm, the dimension of the parameter space is reduced using the local gradient information. In the parameter space, the coarse-to-fine search technique is used to reduce the computing and storage requirements of the hough transform. The experimental results show that FFHT can detect the circular contour quickly and accurately. Moreover, the fuzzy vote technique is adopted to lessen the uncertainty that arisen from edge pixel position error and gradient direction error. Moreover, two methods of aperture's radial crack detection are proposed based on chain code analysis and texture analysis respectively. The experiment of crack detection is carried out in aperture images, and the results indicate that the detecting accuracy using chain code analysis is 91.2%, and the accuracy with texture analysis is 95%. The texture analysis method has raised the accuracy of detection using edge density and direction in the local region synthetically.
For inspection the defects on the surface defect of the rivet of aircraft frame and the control wire rope, which can not close easily and have complicated surface pattern, several texture defect detecting algorithms are designed and implemented correspondingly with the detecting mode that is "object detection→region of interest→defect identification". To detect the surface defect of the rivet and the control wire rope respectively. The LWS and Rodan transform are used to describe the texture feature of rivet regions, and the k-NN one-class classifier is adopted to identify the abnormality of rivet. In the inspection of the wire rope, wire rope images are segmented by texture feature firstly, then, the method based uniformity of subregion and the method using DT-CWT reconstruction are adopted to detect the surface defects of wire rope. Moreover, a nondestructive inspection system based on machine vision is developed. The results of detection experiments shows that the vision inspection methods based on texture analysis are effective to detect the surface defects of the rivet and the wire rope. These research results above show that the machine vision technology could be used widely in the nondestructive inspection field. The shortcomings of visual inspection with human eyes could be overcome by the machine vision detection technology. Simultaneously, the effective coverage of detection could be extended, and the accuracy and automation level could be improved.
引文
[1]张广军.机器视觉[M].北京：科学出版社,2005：1-10.
[2]马颂德,张正友.计算机视觉—计算理论与算法基础[M].北京：科学出版社,1998：1-9.
[3]曾祥照.无损检测文化概论[J].无损探伤,2002,2：34-37.
[4]李家伟,陈积懋.无损检测手册[M].北京：机械工业出版社,2002：585-595.
[5]《国防科技工业无损检测人员资格鉴定与认证考试培训教材》编审委员会.目视检测[M].北京：机械工业出版社,2006：4-46.
[6]GB/T 20967-2007,无损检测目视检测总则[S].
[7]耿荣生,郑勇.航空无损检测技术发展动态及面临的挑战[J].无损检测,2002,24(1)：1-5.
[8]许占显,李服群,伍艳.无损检测与航空维修[J].无损检测,2000,22(6)：269-271.
[9]谢小荣,杨小林.飞机损伤检测[M].北京：航空工业出版社,2006：247-260.
[10]Paul E M. Introduction to Nondestructive Testing:A Training Guide (2nd edition) [M]. New York:John Wiley & Sons, Inc.,2005:575-638.
[11]Richard 0 G. Visual inspection for aircraft[R]. Federal Aviation Administration research report,1997, AFS-350:1-25.
[12]EN 13018-2001, Non-destructive testing-Visual testing-General principles[S].
[13]EN 13927-2003, Non-destructive testing-Visual testing-Equipment[S].
[14]Yoseph B C. Emerging NDT technologies and challenges at the beginning of the 3rd millennium-part I[J]. Materials Evaluation,2000,58(1):17-30.
[15]Yoseph B C. Emerging NDT technologies and challenges at the beginning of the 3rd millennium-part II[J]. Materials Evaluation,2000,58(2):141-150.
[16]于辉.发动机故障图像的三维测量、视觉重建与识别方法研究[D].南京：南京航空航天大学,2002：1-20.
[17]王仲生.无损检测诊断现场实用技术[M].北京：机械工业出版社,2002：345-354.
[18]Heida J H, Bruinsma A J A. D-Sight technique for rapid impact damage detection on composite aircraft structures[C]. Proceedings of The 7th European Conference on Non-Destructive Testing, Copenhagen, Denmark,1998,5:26-29.
[19]Forsyth D S, Marincak A, Komorowski J P. Edge of Light:a new enhanced optical NDI technique[C]. Proceedings of SPIE, Oakland, USA,1996,2945:178-188.
[20]冯振宇.增强目视检查在飞机维护中的应用[J].无损检测,2009,31(3)： 220-222.
[21]Liu Z, Forsyth D S A, Marincak P V. Automated Measurement of Corrosion Damage Using Edge of Light Technique[J]. Review of progress in quantitative nondestructive evaluation,2004,23(B):1347-1354.
[22]Forsyth D S, Komorowski J P, Marincak A, et al. The Edge of Light NDT Technique:Towards Quantitative Enhanced Visual NDT[DB/OL]. http:// www.ndt.net/news/1999/eol.htm.
[23]Siegel M, Gunatilake P. Enhanced Remote Visual Inspection of Aircraft Skin[C]. Proceedings of the Intelligent NDE Sciences for Aging and Futuristic Aircraft Workshop, El Paso, USA,1997,5:69-78.
[24]Siegel M, Guatilake P, Podnar G. Robotic assistants for aircraft inspectors [J]. IEEE Instrumentation & Measurement Magezine,1998, (3):16-30.
[25]丁鹏,李长有,马齐爽,等.基于小波的航空发动机叶片孔探损伤检测[J].北京航空航天大学学报,2006,32(12)：435-438.
[26]罗云林,孟娇茹.基于小波变换和立体视觉的发动机内窥研究[J].辽宁工程技术大学学报,2005,24(4)：573-576.
[27]李岩,孟娇茹.基于图像识别的发动机内窥智能检测系统研究[J].中国民航学院学报,2005,24(4)：15-19.
[28]陈果,汤洋.基于孔探图像纹理特征的航空发动机损伤识别方法[J].仪器仪表学报,2009,29(8)：1709-1713.
[29]邢志伟,高庆吉,党长河.飞机蒙皮检查机器人系统研究[J].机器人,2007,29(5)：474-478.
[30]贾云得.机器视觉[M].北京：科学出版社,2000：1-9.
[31]Malamas E N, Petrakis E G M, Zervakis M, et.al. A survey on industrial vision systems, applications and tools[J]. Image and Vision Computing 21 (2003) 171-188.
[32]刘焕军.灌装自动化生产线上视觉检测机器人研究[D].长沙：湖南大学,2007：4-9.
[33]韩彦芳.机器视觉中的聚类检测新方法[D].上海：上海交通大学大学,2007：1-14.
[34]Chin R T, Harlow C A. Automated visual inspection:A survey [J]. IEEE Transactions on PatternAnalysis and Machine Intelligence,1989,4(6):557-573.
[35]Newman T S, Jain A K. A survey of automated visual inspection[J]. Computer Vision and Image Understanding,1995,61(2):231-262.
[36]周军,彭培欣.自动磁粉探伤系统中的图像技术[J].仪器仪表学报,2003,24(8)：461-462.
[37]樊红梅.凸轮轴表面缺陷全自动检测中数字图像处理与识别的研究[D].南京：河海大学,2002：1-45.
[38]吴海滨,张杰,陈军,等.基于CCD成像的轮箍表面荧光磁粉探伤方法及缺陷图像处理研究[J].大气与环境光学学报,2006,1(2)：155-160.
[39]张晓光,徐健健,任世锦.基于字典的概率松弛法对焊缝图像中裂纹缺陷的提取[J].华东理工大学学报,2004,30(5)：605-608.
[40]屈稳太,张瑶瑶,颜钢锋.基于Hough变换的焊缝位置检测技术[J].机械工程学报,2005,41(9)：212-216.
[41]黄战华,刘正,朱猛,等.基于统计特征的轮胎纹理缺陷在线检测[J].光学技术,2009,35(1)：60-62.
[42]刘洪臣,陈忠建,冯勇.X射线管道焊缝探伤中的视觉同步技术[J].光电子.激光.2006,17(6)：714-718.
[43]陈堂敏.基于阻尼LSQR算法层析成像技术在铸件缺陷检测中的应用[J].电工技术学报.2006,21(11)：110-115.
[44]Zeng Z W, Liu X, Deng Y M, et al. A parametric study of Magneto-Optic Imaging using Finite-Element Analysis applied to aircraft rivet site inspection[J]. IEEE Transactions on Magnetics,2006,42(11):3737-3744.
[45]高庆吉,胡丹丹,牛国臣,等.基于磁光图像的飞机铆钉缺陷识别[J].中国图象图形学报.2007,12(12)：2179-2183.
[46]Chen C H, Pau L F, Wang P S P. Handbook of Pattern Recognition and Computer Vision (2nd edition) [M]. Singapore:World Scientific,1999:207-248.
[47]Shapiro L G, Stockman G C. Computer vision[M]. Pearson Education Inc.,2001: 235-247.
[48]章毓晋.图像分析[M].北京：清华大学出版社,2005：279-309.
[49]Tsai I, Lin C, Lin J. Applying an artificial neural network to pattern recognition[J]. Textile Research Journal,1995,65(3):123-130.
[50]Ng H. Automatic thresholding for defect detection[J]. Pattern Recognition Letters, 2007,27(14):1644-1649.
[51]Otsu N. A threshold selection method from gray-level histograms[J]. IEEE Transactions on Systems, Man, Cybernetics,1979,9(1):62-66.
[52]Haralick R, Shanmugan K, Dinstein I. Textural features for image classification[J]. IEEE Transactions on Systems, Man, and Cybernetics,1973,3(6):610-621.
[53]Haralick R M. Statistical and Structural Approaches to Texture[C]. Proceedings of the IEEE,1979,67(5):786-804.
[54]Conners R, McMillan C, Lin K, et al.Identifying and locating surface defects in wood:Part of an automated timber processing system[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1983,5 (6):573-583.
[55]Siew L, Hodgson R, Wood E. Texture measures for carpet wear assessment[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1988,10(1): 92-105.
[56]Bodnarova A, Williams J, Bennamoun M, et al. Optimal textural features for flaw detection in textile materials[C]. Proceedings of IEEE on Speech and Image Technologies for Computing and Telecommunications, Brishane, Australia,1997, 1:307-310.
[57]Iivarinen J, Rauhamaa J, Visa A. Unsupervised segmentation of surface defects[C]. Proceedings of the 13th International Conference on Pattern Recognition,1996,4: 356-360.
[58]邹超,朱德森,肖力.基于模糊类别共生矩阵的纹理疵点检测方法[J].中国图象图形学报,2007,12(1)：92-97.
[59]Wood E. Applying Fourier and associated transforms to pattern characterization in textiles[J]. Textile Research Journal,1990,60(4):212-220.
[60]Huang Y, Chan K. Texture decomposition by harmonics extraction from higher order statistics[J]. IEEE Transactions on Image Processing,2004,13(1):1-14.
[61]Ojala T, Pietikainen M. Unsupervised texture segmentation using feature distributions[J]. Pattern Recognition,1999,32(2),477-486.
[62]Ojala T, Pietikainen M, Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(17):971-987.
[63]Monadjemi A. Towards Efficient Texture Classification and Abnormality Detection[D]. United Kingdom:University of Bristol,2004:88-129.
[64]Niskanen M, Silven O, Kauppinen H. Color and texture based wood inspection with nonsupervised clustering[C]. Proceedings of the 12th Scandinavian Conference on Image Analysis, Bergen, Norway,2001:336-342.
[65]Maenpaa T, Turtinen M, Pietikainen M. Real-time surface inspection by texture[J]. Real-Time Imaging,2003,9(5):289-296.
[66]Niskanen M, Kauppinen H, Silven O. Real-time aspects of SOM-based visual surface inspection[C]. Proceedings of SPIE on Machine Vision Applications in Industrial Inspection, San Jose, USA,2002,4664:123-134.
[67]Stojanpvic R, Panagiotis M, Christos K, et al. Real-Time Vision-Based System for Textile Fabric Inspection [J]. Real-Time Imaging,2001,7(6):507-518.
[68]Kumar A. Neural network based detection of local textile defects[J]. Pattern Recognition,2003,36(7):1645-1659.
[69]Chen J, Jain A. A structural approach to identify defects in textured images[C]. Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, 1988,1:29-32.
[70]Bodnarova A, Bennamoun M, Kubik K K. Defect Detection in Textile Materials Based on Aspects of the HVS[C]. Proceeding of IEEE International Conference on System, Man, and Sybernetics,1998,5:4423-4428.
[71]Chetverikov D, Hanbury A. Finding Defects in Texture Using Regularity and Local Orientation[J]. Pattern Recognition,2002,35(10):2165-2180.
[72]Zhi Y X. Discriminative Fabric Defect Detection and Classification Using Adaptive Wavelet[D]. Hong Kong:University of Hong Kong,2003:17-52.
[73]Cross G C, Jain A K. Markov Random Field Texture Models[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1983,5(1):25-39.
[74]Chellappa R, Chatterjee S. Classification of Textures Using Gaussian Markov Random Fields[J]. IEEE Transactions on Acoustic, Speech, and Signal Processing, 1985,33(4):959-963.
[75]Krishnamachari S, Chellappa R. Multiresolution Gauss-Markov Random Field Models for Texture Segmentation[J]. IEEE Transactions on Image Processing, 1997,6(2):251-267.
[76]Comer M, Delp E. Segmentation of textured images using a multiresolution Gaussian autoregressive model[J]. IEEE Transactions on Image Processing,1999, 8(3):408-420.
[77]Liu F, Picard R. Preiodicity, directionality and randomness:Wold features for image modeling and retrieval[J]. IEEE Transaction on Pattern Analysis and Machine Intelligence,1996,18(7):722-733.
[78]Kaplan L M. Extended fractal analysis for texture classification and segmentation[J]. IEEE Transactions on Image Processing,1999,8(11):1572-1585.
[79]Cohen F, Fan Z, Attali S. Automated inspection of textile fabrics using textural models[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1991, 13(8):803-809.
[80]Ozdemir S, Ercil A. Markov random field and Karhunen-Loeve transforms for defect inspection of textile products[C], Proceeding of IEEE Conference on Emerging Technologies and Factory Automation,1996,2:697-703.
[81]Gong Y, Hua J X, Huang X B. Fabric Defect Detection Using GMRF Model[J]. Journal of Donghua University,1999,16(3):10-13.
[82]Ozdemir S, Baykut A, Meylani R, et al. Comparative Evaluation of Texture Analysis Algorithms For Defect Inspection of Textile Products[C]. Proceeding of the IEEE 14th International Conference on Pattern Recognition,1998,2: 1738-1740.
[83]Serafim A F L. Multiresolution Pyramids for Segmentation of Natural Images Based on Autoregressive Models. Application to Calf Leather Classification[C]. Proceeding of International Conference on Industrial Electronics, Control and Instrumentation,1991,3:1842-1847.
[84]Serafim A F L. Segmentation of Natural Images Based on Multiresolution Pyramids Linking of the Parameters of an Autoregressive Rotation Invariant Model. Application to Leather Defect Detection[C]. Proceeding of International Conference on Pattern Recognition,1992,3:41-44.
[85]Pentland A P. Fractal-based description of natural scenes[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1984,6(6):661-674.
[86]Chen S S, Keller J M, Crownover R M. On the calculation of fractal features from images[J]. IEEE Transactions on Pattern Recognition and Machine Intelligence, 1993,15 (10):1087-1090.
[87]Sarkar N, Chaudhuri B B. An efficient differential box-counting approach to compute fractal dimension of image [J]. IEEE Transactions on Systems, Man, and Cybernetics,1994,24 (1):115-120.
[88]Conci A, Proenca C B. A fractal image analysis system for fabric inspection based on a box-counting method[J]. Computer Networks and ISDN Systems,1998, 30(20-21):1887-1895.
[89]Conci A, Proenca C B. A system for real-time fabric inspection and industrial decision[C]. Proceedings of the 14th International Conference on Software Engineering and Knowledge Engineering,2002:707-714.
[90]Wen C Y, Chou S, Liaw J J. Textural defect segmentation using a fourier-domain maximum likelihood estimation method[J]. Textile Research Journal,2002,72(3): 253-258.
[91]Parrinello T, Vaughan R A. Multifractal analysis and feature extraction in satellite imagery[J]. International Journal of Remote Sensing,2002,23(9):1799-1825.
[92]Anh V V, Maeda J, Tieng Q M, et al. Multifractal texture analysis and classification[C]. Proceeding of IEEE International Conference on Image Processing,1999,4:445-449.
[93]Lassouaoui N, Belouchrani A, Hamami M L. On the use of multifractal analysis and genetic algorithms for the segmentation of cervical cell images[J]. International Journal of Pattern Recognition and Artificial Intelligence,2003, 17(7):1227-1244.
[94]Hong G B, Jun W, Xiu B H. Fabric defect detection based on multiple fractal features and support vector data description [J]. Engineering Applications of Artificial Intelligence,2009,22(2):224-235.
[95]Bharati M H, Liu J. J, MacGregor J F. Image texture analysis:methods and comparisons[J]. Chemometrics and Intelligent Laboratory Systems,2004,72(1): 57-71
[96]Arof H, Deravi F. Circular neighbourhood and 1-D DFT features for texture classification and segmentation[J]. IEE Proceedings-Vision, Image and Signal Processing,1998,145(3):167-172.
[97]Grigorescu S E, Petkov N, Kruizinga P. Comparison of Texture Features Based on Gabor Filters[J]. IEEE Transactions on Image Processing,2002,11(10): 1160-1167.
[98]Muneeswaran K, Ganesan, Arumugam S, et.al. Texture image segmentation using combined features from spatial and spectral distribution[J]. Pattern Recognition Letters,2006,27(7):755-764
[99]Campbell J G, Hashim A A, McGinnity T M, et al. Flaw Detection in Woven Textiles by Neural Network[C], Proceeding of the 5th Irish Neural Network Conference,1997,3:1-8.
[100]Chan C H, Pang G K H. Fabric Defect Detection by Fourier Analysis[J]. IEEE Transactions on Industry Applications,2000,36(5):1267-1276.
[101]Tsai D M, Heish C. Automated surface inspection for directional textures[J]. Image and Vision Computing,1999,18(1):49-62.
[102]Tsai I) M, Huang T. Automated surface inspection for statistical textures[J]. Image and Vision Computing,2003,21(4):307-323.
[103]Kumar A, Pang G. Fabric defect segmentation using multichannel blob detectors[J]. Optical Engineering,2000,39(12):3176-3190.
[104]Kumar A, Pang G. Defect detection in textured materials using Gabor filters[J]. IEEE Transactions on Industry Applications,2002,38(2):425-440.
[105]Bodnarova A, Bennamoun M, Latham S. Optimal Gabor filters for textile flaw detection[J]. Pattern Recognition,2002,35(12):2973-2991.
[106]Tsai D M, Wu S K. Automated surface inspection using Gabor filters[J]. The International Journal of Advanced Manufacturing Technology,2000,16(7): 474-482.
[107]Wiltschi K, Pinz A, Lindeberg T. An automatic assessment scheme for steel quality inspection[J]. Machine Vision and Applications,2000,12(3):113-128.
[108]Clelia M, Ettore S, Massimiliano N, et. al. Rail corrugation detection by Gabor filtering[C]. Proceedings of International Conference on Iwage Processing,2001,2: 626-628.
[109]Sarraf H S, Goddard J. Vision systems for on-loom fabric inspection[J]. IEEE Transactions on Industry Applications,1999,35(6):1252-1259.
[110]Amet L L, Ertuzun A, Ercil A. An efficient method for texture defect detection: Subband domain co-occurrence matrices[J]. Image and Vision Computing,2000, 18(6-7):543-553.
[111]Jasper W J, Gamier S J, Potlapalli H. Texture characterization and defect detection using adaptive wavelets[J]. Optical Engineering,1996,35(11): 3140-3149.
[112]李立轻.基于计算机视觉的织物疵点自动检测研究[D].上海：东华大学,2003：49-62.
[113]Yang X, Pang G, Yung N. Robust fabric defect detection and classification using multiple adaptive wavelets[J]. IEE Proceedings-Vision, Image, and Signal Processing,2005,152(6):715-723.
[114]Hu M C, Tsai I S. The inspection of fabric defects by using wavelet transform[J]. Journal of the Textile Institute,2000,91(3):420-433.
[115]钟克洪,丁明跃,周成平.基于小波差分统计特征的纹理缺陷检测方法[J].系统工程与电子技术,2004,26(5)：660-664.
[116]Tsai D M, Hsiao B. Automatic surface inspection using wavelet reconstruction[J]. Pattern Recognition,2001,34(6):1285-1305.
[117]Tsai D M, Chiang C H. Automatic band selection for wavelet reconstruction in the application of defect detection[J]. Image and Vision Computing,2003,21(5): 413-431.
[118]Han Y, Shi P. An adaptive level-selecting wavelet transform for texture defect detection[J]. Image and Vision Computing,2007,25(8):1239-1248.
[119]Fujiwara H, Zhang Z, Hashimoto K. Toward automated inspection of textile surfaces:Removing the textural information by using wavelet shrinkage[C]. Proceeding of IEEE International Conference on Robotics & Automation,2001,4: 3529-3534.
[120]Manjunath B S, Ma W Y. Texture Features for Browsing and Retrieval of Image Data[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996,18(8):837-842.
[121]Heikkila M, Pietikainen M, Schmid C. Description of interest regions with local binary patterns[J]. Pattern Recognition,2009,42(3):425-436.
[122]练秋生,李芹,孔令富.融合圆对称轮廓波统计特征和LBP的纹理图像检索[J].计算机学报,2007,30(12)：2198-2204.
[123]Ahonen T, Hadid A, Pietikainen M. Face Description with Local Binary Patterns:Application to Face Recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2006,28(12):2037-2041.
[124]Zhou H, Wang R S, Wang C. A novel extended local-binary-pattern operator for texture analysis[J]. Information Sciences,2008,178(22):4314-4325.
[125]张志龙,鲁新平,沈振康等.基于LWT的纹理特征提取方法[J].国防科技大学学报,2005,27(3)：86-91.
[126]Nassiri M J, Vafaei A, Monadjemi A. Texture Feature Extraction using Slant-Hadamard Transform [C]. Proceeding of World Academy of Science, Engineering and Technology,2006,17:40-44.
[127]Poesio P. Walsh spectral analysis of binary signals arising from intermittent two-phase flows[J]. International Journal of Multiphase Flow,2008,34(5): 516-522.
[128]李金宗.离散正交变换导论[M].北京：高等教育出版社,1989,342-439.
[129]蔡蕾,王珂,张立保.基于局部二值模式的医学图像检索[J].光电子·激光,2008,19(1)：104-106.
[130]Tax D M J. One-class classification:Concept-learning in the absence of counter-examples[D]. Netherlands:Delft University of Technology,2001:1-84.
[131]Juszczak P. Learning to recognize:A study on one-class classification and active learning [D]. Netherlands:Delft University of Technology,2006:1-85.
[132]Markos M, Sameer S. Novelty detection:a review-part 1:statistical approaches [J]. Signal Processing,2003,83(12):2481-2497.
[133]Markos M, Sameer S. Novelty detection:a review-part 2:neural network based approaches [J]. Signal Processing,2003,83 (12):2499-2521.
[134]Hodge V, Austin J. A Survey of Outlier Detection Methodologies [J]. Artificial Intelligence Review,2004,22(2):85-126.
[135]Zhang Y, Liu X D, Xie F D, et.al. Fault classifier of rotating machinery based on weighted support vector data description [J]. Expert Systems with Applications, 2009,36 (4):7928-7932.
[136]Anthony Brew, Marco Grimaldi, Padraig Cunningham. An evaluation of one-class classification techniques for speaker verification [J]. Artificial Intelligence Review,2007,27(4):295-307.
[137]Kwak N, Oh J. Feature extraction for one-class classification problems: Enhancements to biased discriminant analysis[J]. Pattern Recognition,2009,42 (1):17-26.
[138]Sjostrand K, Hansen M S, Larsson H B, et.al. A path algorithm for the support vector domain description and its application to medical imaging [J]. Medical Image Analysis,2007,11(5):417-428.
[139]Moya M, Koch M, Hostetler L. One-class classifier networks for target recognition applications[C]. Proceedings of the world congress on neural networks, 1993,797-801.
[140]Tax D M J, Duin R P W. Support vector domain description[J]. Pattern Recognition Letters,1999,20(11-13):1191-1199.
[141]Tax D M J, Duin R P W. Support vector data description[J]. Machine Learning, 2004,84(1):45-66.
[142]Andrew R. W. Statistical Pattern recognition (2nd edition) [M]. New York: John Wiley & Sons, Inc.,2002:33-121.
[143]Knorr E M, Ng R T, Tucakov V. Distance-based outliers:algorithms and applications[J]. The VLDB Journal,2000,8(3-4):237-253.
[144]Duda R, Hart P, Stork D. Pattern Classification (2nd Edition) [M]. New York: John Wiley & Sons, Inc.,2001:84-213.
[145]Vapnik V N著,许建华等译.统计学习理论[M].北京：电子工业出版社,2009：1-34.
[146]Brito M R, Chavez E L, Quiroz A J, et.al. Connectivity of the mutual k-nearest-neighbor graph in clustering and outlier detection[J]. Statistics & Probability Letters,1997,35(15):33-42.
[147]Scholkopf B, Platt J C, Shawe-Taylor J, et. al. Estimating the support of a high-dimensional distribution[J]. Neural Computation,2001,13(7):1443-1471.
[148]Tax D M J, Duin R P W. Combing one-class classifiers[C]. Proceedings of the Second International Workshop Multiple Classifier Syatems, Berlin,2001, 299-308.
[149]冯爱民,陈松灿.基于核的单类分类器研究[J].南京师范大学学报(工程技术版),2008,8(4)：1-6.
[150]Tax D, Duin, R. Uniform object generation for optimizing one-class classifiers[J]. Journal of Machine Learning Research,2001,2:155-173.
[151]Tax D M J, Muller K. A consistency-based model selection for one-class classification[C], International Conference on Pattern Recognition, Alamitos,2004, 363-366.
[152]赵峰,张军英,刘敬.一种改善支持向量域描述性能的核优化算法[J].自动化学报.2008.34(9)：1122-1127.
[153]Blake C.L, Merz C.J. UCI repository of machine learning databases[DB/OL]. http://archive.ics.uci.edu/ml/.
[154]Mallat S著,杨力华等译.信号处理的小波导引[M].北京：机械工业出版社,2003：1-11,50-90.
[155]成礼智,王红霞,罗永.小波的理论与应用[M].北京：科学出版社,2004：75-130.
[156]Mallat S. A theory for multiresolution signal decompostion:the wavelet representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989,11(7):674-693.
[157]Chang T, Kuo C. Texture analysis and classification with tree-structured wavelet transform[J]. IEEE Transactions on Image Processing,1993,2(4) 429-441.
[158]Laine A, Fan J. Texture Classification by Wavelet Packet Signatures[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1993,15(11): 1186-1191.
[159]Unser M. Texture classification and segmentation usingwavelet frames[J]. IEEE Transactions on Image Processing,1995,4(11):1549-1560.
[160]Zhang J, Wang D, Tran Q N. A Wavelet-based Multiresolution Statistical Model for Texture[J]. IEEE Transactions on Image Processing,2002,11(2): 146-58.
[161]乔玉龙.小波在纹理图像处理中的应用研究[D].哈尔滨：哈尔滨工业大学,2006：13-86.
[162]严奉霞.复数小波理论及其在图像去噪与增强中的应用研究[D].长沙：国防科技大学,2007：1-9.
[163]Nason G P, Silverman B W. The stationary wavelet transform and some statistical applications[C]. Lecture Notes in Statistics, Springer-Verlag,1995: 281-300.
[164]Beylkin G. On the representation of operators in bases of compactly supported wavelets[J]. SIAM Journal on Numerical Analysis,1992,6(6):1716-1740.
[165]Lina J M, Mayrand M. Complex Daubechies wavelets[J]. Applied and Computational Harmonic Analysis,1995,2(3):219-229.
[166]Kingsbury N. Image processing with complex wavelets[J]. Philosophical transactions of the Royal Society,1999,357(1760):2543-2560.
[167]Kingsbury N. A dual-tree complex wavelet transform with improved orthogonality and symmetry properties [C]. Proceedings of International Conference on Image Processing, Vancouver, Canada,2000,2:375-378.
[168]Kingsbury N. Design of q-shift complex wavelets for image processing using frequency domain energy minimization[C]. Proceedings of International Conference on Image Processing, Barcelona,2003,1:1013-1016.
[169]Selesnick I W, Baraniuk R G, Kingsbury N G. The Dual-Tree Complex Wavelet Transform[J]. IEEE Signal Processing Magazine,2005,22(6):123-151.
[170]Rivaz P. Complex Wavelet Based Image Analysis and Synthesis[D]. United Kingdom, University of Cambridge,2000:53-92.
[171]尚赵伟,张明新,赵平,等.基于不同复小波变换方法的纹理检索和相似计算[J].计算机研究与发展,2005,42(10)：1746-1751.
[172]Selesnick I W. Hilbert transform pairs of wavelet bases[J]. IEEE Signal Processing Letters,2001,8(6):170-173.
[173]Ozkaramanli H, Yu R. On the phase condition and its solution for Hilbert transform pairs of wavelet bases[J]. IEEE Trans on Signal Processing,2003, 51(12):3293-3294.
[174]Yu R, Ozkaramanli H. Hilbert transform pairs of orthogonal wavelet bases: Necessary and sufficient condition[J]. IEEE Trans on Signal Processing,2005, 53(12):4723-4725.
[175]Do M N, Vetterli M. Wavelet-Based Texture Retrieval Using Generalized Gaussian Density and Kullback-Leibler Distance[J]. IEEE Transactions on Image Processing,2002,11(2):146-158.
[176]Huang J, Mumford D. Statistics of natural images and models[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Fort Collins, USA,1999,1:541-547.
[177]Grenander U, Srivastava A. Probability models for clutter in natural images[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2001,23(4): 424-429.
[178]Achim A, Bezerianos A, Tsakalides P. Novel bayesian multiscale method for speckle removal in medical ultrasound images[J]. IEEE Transactions on Medical Imaging,2001,20(5):772-783.
[179]Chipman H A, Kolacryk E D, McCulloch R E. Signal de-noising using adaptive Bayesian wavelet shrinkage [C]. Proceedings of IEEE International Symposium on Time-Frequency and Time-Scale Analysis, Paris, France,1996,1: 225-228.
[180]Mihcak M K, Kozintsev L, Ramchandran K. Low complexity image denoising based on statistical modeling of wavelet coeffcients[J]. IEEE Signal Processing Letter,2003,6(12):300-303.
[181]蔡蕾,张春雨,李斌,等.基于Q-shift双树复数小波系数的纹理图像检索[J].光电子·激光,2009,20(9)：1252-125.
[182]Celik T, Tjahjadi T. Multiscale texture classification using dual-tree complex wavelet transform [J]. Pattern Recognition Letters,2009,30(3):331-339.
[183]Qiao Y L, Zhao C H, Song C Y. Complex wavelet based texture classification[J]. Neurocomputing,2009,72(16-18):3957-3963.
[184]Kokare M, Biswas P K, Chatterji B N. Texture image retrieval using new rotated complex wavelet filters[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B:Cybernetics,2005,35(6):1168-1178.
[185]Jammalamadaka S R, SenGupta A. Topics in circular statistics[M]. Singapore: World Scientific Publishing Co. Pte. Ltd.2001:25-52.
[186]Mardia K V, Jupp P E. Directional Statistics[M]. New York:John Wiley & Sons Ltd.2000:32-52.
[187]汪太月,李志明.一种广义高斯分布的参数快速估计法[J].工程地球物理学报,2006,3(3)：172-176.
[188]Illingworth J, Kittler J. A Survey of the Hough Transform [J]. Computer Vision, Graphics, and Image Processing,1988,44(1):87-116.
[189]Chen T C, Chung K L. An Efficient Randomized Algorithm for Detecting Circles[J]. Computer Vision and Image Understanding,2001,83(2):172-191.
[190]Noriaki S, Eiji U, Kanae H. Generalized fuzzy Hough transform for detecting arbitrary shapes in a vague and noisy image[J]. Soft Computing,2006,10(1): 1161-1168.
[191]Wang Q, Chen H, Xu X. A Newly Modified Algorithm of Hough Transform for Line Detection[J]. International Journal of Image and Graphics,2005,5(4): 715-727.
[192]崔继文,谭久彬,敖磊,等.约束抽样Hough变换的光纤端面精确定位[J].光学精密工程,2007,15(1)：9-15.
[193]Davies E R. Machine Vision:Theory, Alforithms, Practicalities[M]. San Fracisco:Morgan Kaufmann Publishers Inc.,2004:265-337.
[194]Xu L, Oja E, Kultanen P. A new curve detection method:Randomized Hough transform[J]. Pattern Recognition Letters,1990,11(8):331-338.
[195]Kiryati N, Kalviainen H, Alaoutinen S. Randomized or probabilistic Hough transform:unified performance evaluation[J]. Pattern Recognition Letters,2000, 21(11):1157-1164.
[196]Strauss O. Use the Fuzzy Hough transform towards reduction of the precision-uncertainty duality[J]. Pattern Recognition,1999,32(12):1911-1922.
[197]Basak J., Pal S K. Theoretical quantification of shape distortion in fuzzy Hough transform[J]. Fuzzy Sets and Systems,2005,154(24):227-250.
[198]Li H., Lavin M A. Master R L. Fast Hough transform:A hierarchical approach[J]. Computer Vision, Graphics, and Image Processing,1986,36(23): 139-161.
[199]蔡晋辉,蒋庆,周泽魁,等.圆检测HOUGH变换的改进算法研究[J].模式识别与人工智能,2004,17(1)：72-76.
[200]Soodamani R, Liu Z Q. A Fuzzy Hough Transform Approach to Shape Description[J]. International Journal of Image and Graphics,2002,2(4):603-616.
[201]张玉华,罗飞路,孙慧贤,等.一种用于盘孔裂纹检测的差动式涡流探头的设计与实现[J].传感技术学报,2008,21(6)：1079-1083.
[202]盛业华,唐宏,杜培军,等.一种保形的快速图像形态细化算法[J].中国图象图形学报,2000,5(2)：89-93.
[203]张鹏飞.虹膜识别若干关键技术的研究[D].哈尔滨：哈尔滨工业大学,2005：19-36.
[204]代永朝,郑立胜.飞机结构检修[M].北京：航空工业出版社,2006：56-87.
[205]Cui P L, Li J H, Pan Q, et.al. Rotation and scaling invariant texture classification based on Radon transform and multiscale analysis[J]. Pattern Recognition Letters,2006,27 (5):408-413.
[206]Kourosh J K, Hamid S Z. Radon Transform Orientation Estimation for Rotation Invariant Texture Analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(6):1004-1008.
[207]武维新,张楠.典型飞行事故调查与分析方法[M].北京：国防工业出版社,2008：134-168.
[208]Gu W, Chu J X. A Transducer Made Up of Fluxgate Sensors for Testing Wire Rope Defects[J]. IEEE Transactions on Instrumentation and Measurement,2002, 51(1):120-124.
[2]马颂德,张正友.计算机视觉—计算理论与算法基础[M].北京：科学出版社,1998：1-9.
[3]曾祥照.无损检测文化概论[J].无损探伤,2002,2：34-37.
[4]李家伟,陈积懋.无损检测手册[M].北京：机械工业出版社,2002：585-595.
[5]《国防科技工业无损检测人员资格鉴定与认证考试培训教材》编审委员会.目视检测[M].北京：机械工业出版社,2006：4-46.
[6]GB/T 20967-2007,无损检测目视检测总则[S].
[7]耿荣生,郑勇.航空无损检测技术发展动态及面临的挑战[J].无损检测,2002,24(1)：1-5.
[8]许占显,李服群,伍艳.无损检测与航空维修[J].无损检测,2000,22(6)：269-271.
[9]谢小荣,杨小林.飞机损伤检测[M].北京：航空工业出版社,2006：247-260.
[10]Paul E M. Introduction to Nondestructive Testing:A Training Guide (2nd edition) [M]. New York:John Wiley & Sons, Inc.,2005:575-638.
[11]Richard 0 G. Visual inspection for aircraft[R]. Federal Aviation Administration research report,1997, AFS-350:1-25.
[12]EN 13018-2001, Non-destructive testing-Visual testing-General principles[S].
[13]EN 13927-2003, Non-destructive testing-Visual testing-Equipment[S].
[14]Yoseph B C. Emerging NDT technologies and challenges at the beginning of the 3rd millennium-part I[J]. Materials Evaluation,2000,58(1):17-30.
[15]Yoseph B C. Emerging NDT technologies and challenges at the beginning of the 3rd millennium-part II[J]. Materials Evaluation,2000,58(2):141-150.
[16]于辉.发动机故障图像的三维测量、视觉重建与识别方法研究[D].南京：南京航空航天大学,2002：1-20.
[17]王仲生.无损检测诊断现场实用技术[M].北京：机械工业出版社,2002：345-354.
[18]Heida J H, Bruinsma A J A. D-Sight technique for rapid impact damage detection on composite aircraft structures[C]. Proceedings of The 7th European Conference on Non-Destructive Testing, Copenhagen, Denmark,1998,5:26-29.
[19]Forsyth D S, Marincak A, Komorowski J P. Edge of Light:a new enhanced optical NDI technique[C]. Proceedings of SPIE, Oakland, USA,1996,2945:178-188.
[20]冯振宇.增强目视检查在飞机维护中的应用[J].无损检测,2009,31(3)： 220-222.
[21]Liu Z, Forsyth D S A, Marincak P V. Automated Measurement of Corrosion Damage Using Edge of Light Technique[J]. Review of progress in quantitative nondestructive evaluation,2004,23(B):1347-1354.
[22]Forsyth D S, Komorowski J P, Marincak A, et al. The Edge of Light NDT Technique:Towards Quantitative Enhanced Visual NDT[DB/OL]. http:// www.ndt.net/news/1999/eol.htm.
[23]Siegel M, Gunatilake P. Enhanced Remote Visual Inspection of Aircraft Skin[C]. Proceedings of the Intelligent NDE Sciences for Aging and Futuristic Aircraft Workshop, El Paso, USA,1997,5:69-78.
[24]Siegel M, Guatilake P, Podnar G. Robotic assistants for aircraft inspectors [J]. IEEE Instrumentation & Measurement Magezine,1998, (3):16-30.
[25]丁鹏,李长有,马齐爽,等.基于小波的航空发动机叶片孔探损伤检测[J].北京航空航天大学学报,2006,32(12)：435-438.
[26]罗云林,孟娇茹.基于小波变换和立体视觉的发动机内窥研究[J].辽宁工程技术大学学报,2005,24(4)：573-576.
[27]李岩,孟娇茹.基于图像识别的发动机内窥智能检测系统研究[J].中国民航学院学报,2005,24(4)：15-19.
[28]陈果,汤洋.基于孔探图像纹理特征的航空发动机损伤识别方法[J].仪器仪表学报,2009,29(8)：1709-1713.
[29]邢志伟,高庆吉,党长河.飞机蒙皮检查机器人系统研究[J].机器人,2007,29(5)：474-478.
[30]贾云得.机器视觉[M].北京：科学出版社,2000：1-9.
[31]Malamas E N, Petrakis E G M, Zervakis M, et.al. A survey on industrial vision systems, applications and tools[J]. Image and Vision Computing 21 (2003) 171-188.
[32]刘焕军.灌装自动化生产线上视觉检测机器人研究[D].长沙：湖南大学,2007：4-9.
[33]韩彦芳.机器视觉中的聚类检测新方法[D].上海：上海交通大学大学,2007：1-14.
[34]Chin R T, Harlow C A. Automated visual inspection:A survey [J]. IEEE Transactions on PatternAnalysis and Machine Intelligence,1989,4(6):557-573.
[35]Newman T S, Jain A K. A survey of automated visual inspection[J]. Computer Vision and Image Understanding,1995,61(2):231-262.
[36]周军,彭培欣.自动磁粉探伤系统中的图像技术[J].仪器仪表学报,2003,24(8)：461-462.
[37]樊红梅.凸轮轴表面缺陷全自动检测中数字图像处理与识别的研究[D].南京：河海大学,2002：1-45.
[38]吴海滨,张杰,陈军,等.基于CCD成像的轮箍表面荧光磁粉探伤方法及缺陷图像处理研究[J].大气与环境光学学报,2006,1(2)：155-160.
[39]张晓光,徐健健,任世锦.基于字典的概率松弛法对焊缝图像中裂纹缺陷的提取[J].华东理工大学学报,2004,30(5)：605-608.
[40]屈稳太,张瑶瑶,颜钢锋.基于Hough变换的焊缝位置检测技术[J].机械工程学报,2005,41(9)：212-216.
[41]黄战华,刘正,朱猛,等.基于统计特征的轮胎纹理缺陷在线检测[J].光学技术,2009,35(1)：60-62.
[42]刘洪臣,陈忠建,冯勇.X射线管道焊缝探伤中的视觉同步技术[J].光电子.激光.2006,17(6)：714-718.
[43]陈堂敏.基于阻尼LSQR算法层析成像技术在铸件缺陷检测中的应用[J].电工技术学报.2006,21(11)：110-115.
[44]Zeng Z W, Liu X, Deng Y M, et al. A parametric study of Magneto-Optic Imaging using Finite-Element Analysis applied to aircraft rivet site inspection[J]. IEEE Transactions on Magnetics,2006,42(11):3737-3744.
[45]高庆吉,胡丹丹,牛国臣,等.基于磁光图像的飞机铆钉缺陷识别[J].中国图象图形学报.2007,12(12)：2179-2183.
[46]Chen C H, Pau L F, Wang P S P. Handbook of Pattern Recognition and Computer Vision (2nd edition) [M]. Singapore:World Scientific,1999:207-248.
[47]Shapiro L G, Stockman G C. Computer vision[M]. Pearson Education Inc.,2001: 235-247.
[48]章毓晋.图像分析[M].北京：清华大学出版社,2005：279-309.
[49]Tsai I, Lin C, Lin J. Applying an artificial neural network to pattern recognition[J]. Textile Research Journal,1995,65(3):123-130.
[50]Ng H. Automatic thresholding for defect detection[J]. Pattern Recognition Letters, 2007,27(14):1644-1649.
[51]Otsu N. A threshold selection method from gray-level histograms[J]. IEEE Transactions on Systems, Man, Cybernetics,1979,9(1):62-66.
[52]Haralick R, Shanmugan K, Dinstein I. Textural features for image classification[J]. IEEE Transactions on Systems, Man, and Cybernetics,1973,3(6):610-621.
[53]Haralick R M. Statistical and Structural Approaches to Texture[C]. Proceedings of the IEEE,1979,67(5):786-804.
[54]Conners R, McMillan C, Lin K, et al.Identifying and locating surface defects in wood:Part of an automated timber processing system[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1983,5 (6):573-583.
[55]Siew L, Hodgson R, Wood E. Texture measures for carpet wear assessment[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1988,10(1): 92-105.
[56]Bodnarova A, Williams J, Bennamoun M, et al. Optimal textural features for flaw detection in textile materials[C]. Proceedings of IEEE on Speech and Image Technologies for Computing and Telecommunications, Brishane, Australia,1997, 1:307-310.
[57]Iivarinen J, Rauhamaa J, Visa A. Unsupervised segmentation of surface defects[C]. Proceedings of the 13th International Conference on Pattern Recognition,1996,4: 356-360.
[58]邹超,朱德森,肖力.基于模糊类别共生矩阵的纹理疵点检测方法[J].中国图象图形学报,2007,12(1)：92-97.
[59]Wood E. Applying Fourier and associated transforms to pattern characterization in textiles[J]. Textile Research Journal,1990,60(4):212-220.
[60]Huang Y, Chan K. Texture decomposition by harmonics extraction from higher order statistics[J]. IEEE Transactions on Image Processing,2004,13(1):1-14.
[61]Ojala T, Pietikainen M. Unsupervised texture segmentation using feature distributions[J]. Pattern Recognition,1999,32(2),477-486.
[62]Ojala T, Pietikainen M, Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(17):971-987.
[63]Monadjemi A. Towards Efficient Texture Classification and Abnormality Detection[D]. United Kingdom:University of Bristol,2004:88-129.
[64]Niskanen M, Silven O, Kauppinen H. Color and texture based wood inspection with nonsupervised clustering[C]. Proceedings of the 12th Scandinavian Conference on Image Analysis, Bergen, Norway,2001:336-342.
[65]Maenpaa T, Turtinen M, Pietikainen M. Real-time surface inspection by texture[J]. Real-Time Imaging,2003,9(5):289-296.
[66]Niskanen M, Kauppinen H, Silven O. Real-time aspects of SOM-based visual surface inspection[C]. Proceedings of SPIE on Machine Vision Applications in Industrial Inspection, San Jose, USA,2002,4664:123-134.
[67]Stojanpvic R, Panagiotis M, Christos K, et al. Real-Time Vision-Based System for Textile Fabric Inspection [J]. Real-Time Imaging,2001,7(6):507-518.
[68]Kumar A. Neural network based detection of local textile defects[J]. Pattern Recognition,2003,36(7):1645-1659.
[69]Chen J, Jain A. A structural approach to identify defects in textured images[C]. Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, 1988,1:29-32.
[70]Bodnarova A, Bennamoun M, Kubik K K. Defect Detection in Textile Materials Based on Aspects of the HVS[C]. Proceeding of IEEE International Conference on System, Man, and Sybernetics,1998,5:4423-4428.
[71]Chetverikov D, Hanbury A. Finding Defects in Texture Using Regularity and Local Orientation[J]. Pattern Recognition,2002,35(10):2165-2180.
[72]Zhi Y X. Discriminative Fabric Defect Detection and Classification Using Adaptive Wavelet[D]. Hong Kong:University of Hong Kong,2003:17-52.
[73]Cross G C, Jain A K. Markov Random Field Texture Models[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1983,5(1):25-39.
[74]Chellappa R, Chatterjee S. Classification of Textures Using Gaussian Markov Random Fields[J]. IEEE Transactions on Acoustic, Speech, and Signal Processing, 1985,33(4):959-963.
[75]Krishnamachari S, Chellappa R. Multiresolution Gauss-Markov Random Field Models for Texture Segmentation[J]. IEEE Transactions on Image Processing, 1997,6(2):251-267.
[76]Comer M, Delp E. Segmentation of textured images using a multiresolution Gaussian autoregressive model[J]. IEEE Transactions on Image Processing,1999, 8(3):408-420.
[77]Liu F, Picard R. Preiodicity, directionality and randomness:Wold features for image modeling and retrieval[J]. IEEE Transaction on Pattern Analysis and Machine Intelligence,1996,18(7):722-733.
[78]Kaplan L M. Extended fractal analysis for texture classification and segmentation[J]. IEEE Transactions on Image Processing,1999,8(11):1572-1585.
[79]Cohen F, Fan Z, Attali S. Automated inspection of textile fabrics using textural models[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1991, 13(8):803-809.
[80]Ozdemir S, Ercil A. Markov random field and Karhunen-Loeve transforms for defect inspection of textile products[C], Proceeding of IEEE Conference on Emerging Technologies and Factory Automation,1996,2:697-703.
[81]Gong Y, Hua J X, Huang X B. Fabric Defect Detection Using GMRF Model[J]. Journal of Donghua University,1999,16(3):10-13.
[82]Ozdemir S, Baykut A, Meylani R, et al. Comparative Evaluation of Texture Analysis Algorithms For Defect Inspection of Textile Products[C]. Proceeding of the IEEE 14th International Conference on Pattern Recognition,1998,2: 1738-1740.
[83]Serafim A F L. Multiresolution Pyramids for Segmentation of Natural Images Based on Autoregressive Models. Application to Calf Leather Classification[C]. Proceeding of International Conference on Industrial Electronics, Control and Instrumentation,1991,3:1842-1847.
[84]Serafim A F L. Segmentation of Natural Images Based on Multiresolution Pyramids Linking of the Parameters of an Autoregressive Rotation Invariant Model. Application to Leather Defect Detection[C]. Proceeding of International Conference on Pattern Recognition,1992,3:41-44.
[85]Pentland A P. Fractal-based description of natural scenes[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1984,6(6):661-674.
[86]Chen S S, Keller J M, Crownover R M. On the calculation of fractal features from images[J]. IEEE Transactions on Pattern Recognition and Machine Intelligence, 1993,15 (10):1087-1090.
[87]Sarkar N, Chaudhuri B B. An efficient differential box-counting approach to compute fractal dimension of image [J]. IEEE Transactions on Systems, Man, and Cybernetics,1994,24 (1):115-120.
[88]Conci A, Proenca C B. A fractal image analysis system for fabric inspection based on a box-counting method[J]. Computer Networks and ISDN Systems,1998, 30(20-21):1887-1895.
[89]Conci A, Proenca C B. A system for real-time fabric inspection and industrial decision[C]. Proceedings of the 14th International Conference on Software Engineering and Knowledge Engineering,2002:707-714.
[90]Wen C Y, Chou S, Liaw J J. Textural defect segmentation using a fourier-domain maximum likelihood estimation method[J]. Textile Research Journal,2002,72(3): 253-258.
[91]Parrinello T, Vaughan R A. Multifractal analysis and feature extraction in satellite imagery[J]. International Journal of Remote Sensing,2002,23(9):1799-1825.
[92]Anh V V, Maeda J, Tieng Q M, et al. Multifractal texture analysis and classification[C]. Proceeding of IEEE International Conference on Image Processing,1999,4:445-449.
[93]Lassouaoui N, Belouchrani A, Hamami M L. On the use of multifractal analysis and genetic algorithms for the segmentation of cervical cell images[J]. International Journal of Pattern Recognition and Artificial Intelligence,2003, 17(7):1227-1244.
[94]Hong G B, Jun W, Xiu B H. Fabric defect detection based on multiple fractal features and support vector data description [J]. Engineering Applications of Artificial Intelligence,2009,22(2):224-235.
[95]Bharati M H, Liu J. J, MacGregor J F. Image texture analysis:methods and comparisons[J]. Chemometrics and Intelligent Laboratory Systems,2004,72(1): 57-71
[96]Arof H, Deravi F. Circular neighbourhood and 1-D DFT features for texture classification and segmentation[J]. IEE Proceedings-Vision, Image and Signal Processing,1998,145(3):167-172.
[97]Grigorescu S E, Petkov N, Kruizinga P. Comparison of Texture Features Based on Gabor Filters[J]. IEEE Transactions on Image Processing,2002,11(10): 1160-1167.
[98]Muneeswaran K, Ganesan, Arumugam S, et.al. Texture image segmentation using combined features from spatial and spectral distribution[J]. Pattern Recognition Letters,2006,27(7):755-764
[99]Campbell J G, Hashim A A, McGinnity T M, et al. Flaw Detection in Woven Textiles by Neural Network[C], Proceeding of the 5th Irish Neural Network Conference,1997,3:1-8.
[100]Chan C H, Pang G K H. Fabric Defect Detection by Fourier Analysis[J]. IEEE Transactions on Industry Applications,2000,36(5):1267-1276.
[101]Tsai D M, Heish C. Automated surface inspection for directional textures[J]. Image and Vision Computing,1999,18(1):49-62.
[102]Tsai I) M, Huang T. Automated surface inspection for statistical textures[J]. Image and Vision Computing,2003,21(4):307-323.
[103]Kumar A, Pang G. Fabric defect segmentation using multichannel blob detectors[J]. Optical Engineering,2000,39(12):3176-3190.
[104]Kumar A, Pang G. Defect detection in textured materials using Gabor filters[J]. IEEE Transactions on Industry Applications,2002,38(2):425-440.
[105]Bodnarova A, Bennamoun M, Latham S. Optimal Gabor filters for textile flaw detection[J]. Pattern Recognition,2002,35(12):2973-2991.
[106]Tsai D M, Wu S K. Automated surface inspection using Gabor filters[J]. The International Journal of Advanced Manufacturing Technology,2000,16(7): 474-482.
[107]Wiltschi K, Pinz A, Lindeberg T. An automatic assessment scheme for steel quality inspection[J]. Machine Vision and Applications,2000,12(3):113-128.
[108]Clelia M, Ettore S, Massimiliano N, et. al. Rail corrugation detection by Gabor filtering[C]. Proceedings of International Conference on Iwage Processing,2001,2: 626-628.
[109]Sarraf H S, Goddard J. Vision systems for on-loom fabric inspection[J]. IEEE Transactions on Industry Applications,1999,35(6):1252-1259.
[110]Amet L L, Ertuzun A, Ercil A. An efficient method for texture defect detection: Subband domain co-occurrence matrices[J]. Image and Vision Computing,2000, 18(6-7):543-553.
[111]Jasper W J, Gamier S J, Potlapalli H. Texture characterization and defect detection using adaptive wavelets[J]. Optical Engineering,1996,35(11): 3140-3149.
[112]李立轻.基于计算机视觉的织物疵点自动检测研究[D].上海：东华大学,2003：49-62.
[113]Yang X, Pang G, Yung N. Robust fabric defect detection and classification using multiple adaptive wavelets[J]. IEE Proceedings-Vision, Image, and Signal Processing,2005,152(6):715-723.
[114]Hu M C, Tsai I S. The inspection of fabric defects by using wavelet transform[J]. Journal of the Textile Institute,2000,91(3):420-433.
[115]钟克洪,丁明跃,周成平.基于小波差分统计特征的纹理缺陷检测方法[J].系统工程与电子技术,2004,26(5)：660-664.
[116]Tsai D M, Hsiao B. Automatic surface inspection using wavelet reconstruction[J]. Pattern Recognition,2001,34(6):1285-1305.
[117]Tsai D M, Chiang C H. Automatic band selection for wavelet reconstruction in the application of defect detection[J]. Image and Vision Computing,2003,21(5): 413-431.
[118]Han Y, Shi P. An adaptive level-selecting wavelet transform for texture defect detection[J]. Image and Vision Computing,2007,25(8):1239-1248.
[119]Fujiwara H, Zhang Z, Hashimoto K. Toward automated inspection of textile surfaces:Removing the textural information by using wavelet shrinkage[C]. Proceeding of IEEE International Conference on Robotics & Automation,2001,4: 3529-3534.
[120]Manjunath B S, Ma W Y. Texture Features for Browsing and Retrieval of Image Data[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996,18(8):837-842.
[121]Heikkila M, Pietikainen M, Schmid C. Description of interest regions with local binary patterns[J]. Pattern Recognition,2009,42(3):425-436.
[122]练秋生,李芹,孔令富.融合圆对称轮廓波统计特征和LBP的纹理图像检索[J].计算机学报,2007,30(12)：2198-2204.
[123]Ahonen T, Hadid A, Pietikainen M. Face Description with Local Binary Patterns:Application to Face Recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2006,28(12):2037-2041.
[124]Zhou H, Wang R S, Wang C. A novel extended local-binary-pattern operator for texture analysis[J]. Information Sciences,2008,178(22):4314-4325.
[125]张志龙,鲁新平,沈振康等.基于LWT的纹理特征提取方法[J].国防科技大学学报,2005,27(3)：86-91.
[126]Nassiri M J, Vafaei A, Monadjemi A. Texture Feature Extraction using Slant-Hadamard Transform [C]. Proceeding of World Academy of Science, Engineering and Technology,2006,17:40-44.
[127]Poesio P. Walsh spectral analysis of binary signals arising from intermittent two-phase flows[J]. International Journal of Multiphase Flow,2008,34(5): 516-522.
[128]李金宗.离散正交变换导论[M].北京：高等教育出版社,1989,342-439.
[129]蔡蕾,王珂,张立保.基于局部二值模式的医学图像检索[J].光电子·激光,2008,19(1)：104-106.
[130]Tax D M J. One-class classification:Concept-learning in the absence of counter-examples[D]. Netherlands:Delft University of Technology,2001:1-84.
[131]Juszczak P. Learning to recognize:A study on one-class classification and active learning [D]. Netherlands:Delft University of Technology,2006:1-85.
[132]Markos M, Sameer S. Novelty detection:a review-part 1:statistical approaches [J]. Signal Processing,2003,83(12):2481-2497.
[133]Markos M, Sameer S. Novelty detection:a review-part 2:neural network based approaches [J]. Signal Processing,2003,83 (12):2499-2521.
[134]Hodge V, Austin J. A Survey of Outlier Detection Methodologies [J]. Artificial Intelligence Review,2004,22(2):85-126.
[135]Zhang Y, Liu X D, Xie F D, et.al. Fault classifier of rotating machinery based on weighted support vector data description [J]. Expert Systems with Applications, 2009,36 (4):7928-7932.
[136]Anthony Brew, Marco Grimaldi, Padraig Cunningham. An evaluation of one-class classification techniques for speaker verification [J]. Artificial Intelligence Review,2007,27(4):295-307.
[137]Kwak N, Oh J. Feature extraction for one-class classification problems: Enhancements to biased discriminant analysis[J]. Pattern Recognition,2009,42 (1):17-26.
[138]Sjostrand K, Hansen M S, Larsson H B, et.al. A path algorithm for the support vector domain description and its application to medical imaging [J]. Medical Image Analysis,2007,11(5):417-428.
[139]Moya M, Koch M, Hostetler L. One-class classifier networks for target recognition applications[C]. Proceedings of the world congress on neural networks, 1993,797-801.
[140]Tax D M J, Duin R P W. Support vector domain description[J]. Pattern Recognition Letters,1999,20(11-13):1191-1199.
[141]Tax D M J, Duin R P W. Support vector data description[J]. Machine Learning, 2004,84(1):45-66.
[142]Andrew R. W. Statistical Pattern recognition (2nd edition) [M]. New York: John Wiley & Sons, Inc.,2002:33-121.
[143]Knorr E M, Ng R T, Tucakov V. Distance-based outliers:algorithms and applications[J]. The VLDB Journal,2000,8(3-4):237-253.
[144]Duda R, Hart P, Stork D. Pattern Classification (2nd Edition) [M]. New York: John Wiley & Sons, Inc.,2001:84-213.
[145]Vapnik V N著,许建华等译.统计学习理论[M].北京：电子工业出版社,2009：1-34.
[146]Brito M R, Chavez E L, Quiroz A J, et.al. Connectivity of the mutual k-nearest-neighbor graph in clustering and outlier detection[J]. Statistics & Probability Letters,1997,35(15):33-42.
[147]Scholkopf B, Platt J C, Shawe-Taylor J, et. al. Estimating the support of a high-dimensional distribution[J]. Neural Computation,2001,13(7):1443-1471.
[148]Tax D M J, Duin R P W. Combing one-class classifiers[C]. Proceedings of the Second International Workshop Multiple Classifier Syatems, Berlin,2001, 299-308.
[149]冯爱民,陈松灿.基于核的单类分类器研究[J].南京师范大学学报(工程技术版),2008,8(4)：1-6.
[150]Tax D, Duin, R. Uniform object generation for optimizing one-class classifiers[J]. Journal of Machine Learning Research,2001,2:155-173.
[151]Tax D M J, Muller K. A consistency-based model selection for one-class classification[C], International Conference on Pattern Recognition, Alamitos,2004, 363-366.
[152]赵峰,张军英,刘敬.一种改善支持向量域描述性能的核优化算法[J].自动化学报.2008.34(9)：1122-1127.
[153]Blake C.L, Merz C.J. UCI repository of machine learning databases[DB/OL]. http://archive.ics.uci.edu/ml/.
[154]Mallat S著,杨力华等译.信号处理的小波导引[M].北京：机械工业出版社,2003：1-11,50-90.
[155]成礼智,王红霞,罗永.小波的理论与应用[M].北京：科学出版社,2004：75-130.
[156]Mallat S. A theory for multiresolution signal decompostion:the wavelet representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989,11(7):674-693.
[157]Chang T, Kuo C. Texture analysis and classification with tree-structured wavelet transform[J]. IEEE Transactions on Image Processing,1993,2(4) 429-441.
[158]Laine A, Fan J. Texture Classification by Wavelet Packet Signatures[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1993,15(11): 1186-1191.
[159]Unser M. Texture classification and segmentation usingwavelet frames[J]. IEEE Transactions on Image Processing,1995,4(11):1549-1560.
[160]Zhang J, Wang D, Tran Q N. A Wavelet-based Multiresolution Statistical Model for Texture[J]. IEEE Transactions on Image Processing,2002,11(2): 146-58.
[161]乔玉龙.小波在纹理图像处理中的应用研究[D].哈尔滨：哈尔滨工业大学,2006：13-86.
[162]严奉霞.复数小波理论及其在图像去噪与增强中的应用研究[D].长沙：国防科技大学,2007：1-9.
[163]Nason G P, Silverman B W. The stationary wavelet transform and some statistical applications[C]. Lecture Notes in Statistics, Springer-Verlag,1995: 281-300.
[164]Beylkin G. On the representation of operators in bases of compactly supported wavelets[J]. SIAM Journal on Numerical Analysis,1992,6(6):1716-1740.
[165]Lina J M, Mayrand M. Complex Daubechies wavelets[J]. Applied and Computational Harmonic Analysis,1995,2(3):219-229.
[166]Kingsbury N. Image processing with complex wavelets[J]. Philosophical transactions of the Royal Society,1999,357(1760):2543-2560.
[167]Kingsbury N. A dual-tree complex wavelet transform with improved orthogonality and symmetry properties [C]. Proceedings of International Conference on Image Processing, Vancouver, Canada,2000,2:375-378.
[168]Kingsbury N. Design of q-shift complex wavelets for image processing using frequency domain energy minimization[C]. Proceedings of International Conference on Image Processing, Barcelona,2003,1:1013-1016.
[169]Selesnick I W, Baraniuk R G, Kingsbury N G. The Dual-Tree Complex Wavelet Transform[J]. IEEE Signal Processing Magazine,2005,22(6):123-151.
[170]Rivaz P. Complex Wavelet Based Image Analysis and Synthesis[D]. United Kingdom, University of Cambridge,2000:53-92.
[171]尚赵伟,张明新,赵平,等.基于不同复小波变换方法的纹理检索和相似计算[J].计算机研究与发展,2005,42(10)：1746-1751.
[172]Selesnick I W. Hilbert transform pairs of wavelet bases[J]. IEEE Signal Processing Letters,2001,8(6):170-173.
[173]Ozkaramanli H, Yu R. On the phase condition and its solution for Hilbert transform pairs of wavelet bases[J]. IEEE Trans on Signal Processing,2003, 51(12):3293-3294.
[174]Yu R, Ozkaramanli H. Hilbert transform pairs of orthogonal wavelet bases: Necessary and sufficient condition[J]. IEEE Trans on Signal Processing,2005, 53(12):4723-4725.
[175]Do M N, Vetterli M. Wavelet-Based Texture Retrieval Using Generalized Gaussian Density and Kullback-Leibler Distance[J]. IEEE Transactions on Image Processing,2002,11(2):146-158.
[176]Huang J, Mumford D. Statistics of natural images and models[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Fort Collins, USA,1999,1:541-547.
[177]Grenander U, Srivastava A. Probability models for clutter in natural images[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2001,23(4): 424-429.
[178]Achim A, Bezerianos A, Tsakalides P. Novel bayesian multiscale method for speckle removal in medical ultrasound images[J]. IEEE Transactions on Medical Imaging,2001,20(5):772-783.
[179]Chipman H A, Kolacryk E D, McCulloch R E. Signal de-noising using adaptive Bayesian wavelet shrinkage [C]. Proceedings of IEEE International Symposium on Time-Frequency and Time-Scale Analysis, Paris, France,1996,1: 225-228.
[180]Mihcak M K, Kozintsev L, Ramchandran K. Low complexity image denoising based on statistical modeling of wavelet coeffcients[J]. IEEE Signal Processing Letter,2003,6(12):300-303.
[181]蔡蕾,张春雨,李斌,等.基于Q-shift双树复数小波系数的纹理图像检索[J].光电子·激光,2009,20(9)：1252-125.
[182]Celik T, Tjahjadi T. Multiscale texture classification using dual-tree complex wavelet transform [J]. Pattern Recognition Letters,2009,30(3):331-339.
[183]Qiao Y L, Zhao C H, Song C Y. Complex wavelet based texture classification[J]. Neurocomputing,2009,72(16-18):3957-3963.
[184]Kokare M, Biswas P K, Chatterji B N. Texture image retrieval using new rotated complex wavelet filters[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B:Cybernetics,2005,35(6):1168-1178.
[185]Jammalamadaka S R, SenGupta A. Topics in circular statistics[M]. Singapore: World Scientific Publishing Co. Pte. Ltd.2001:25-52.
[186]Mardia K V, Jupp P E. Directional Statistics[M]. New York:John Wiley & Sons Ltd.2000:32-52.
[187]汪太月,李志明.一种广义高斯分布的参数快速估计法[J].工程地球物理学报,2006,3(3)：172-176.
[188]Illingworth J, Kittler J. A Survey of the Hough Transform [J]. Computer Vision, Graphics, and Image Processing,1988,44(1):87-116.
[189]Chen T C, Chung K L. An Efficient Randomized Algorithm for Detecting Circles[J]. Computer Vision and Image Understanding,2001,83(2):172-191.
[190]Noriaki S, Eiji U, Kanae H. Generalized fuzzy Hough transform for detecting arbitrary shapes in a vague and noisy image[J]. Soft Computing,2006,10(1): 1161-1168.
[191]Wang Q, Chen H, Xu X. A Newly Modified Algorithm of Hough Transform for Line Detection[J]. International Journal of Image and Graphics,2005,5(4): 715-727.
[192]崔继文,谭久彬,敖磊,等.约束抽样Hough变换的光纤端面精确定位[J].光学精密工程,2007,15(1)：9-15.
[193]Davies E R. Machine Vision:Theory, Alforithms, Practicalities[M]. San Fracisco:Morgan Kaufmann Publishers Inc.,2004:265-337.
[194]Xu L, Oja E, Kultanen P. A new curve detection method:Randomized Hough transform[J]. Pattern Recognition Letters,1990,11(8):331-338.
[195]Kiryati N, Kalviainen H, Alaoutinen S. Randomized or probabilistic Hough transform:unified performance evaluation[J]. Pattern Recognition Letters,2000, 21(11):1157-1164.
[196]Strauss O. Use the Fuzzy Hough transform towards reduction of the precision-uncertainty duality[J]. Pattern Recognition,1999,32(12):1911-1922.
[197]Basak J., Pal S K. Theoretical quantification of shape distortion in fuzzy Hough transform[J]. Fuzzy Sets and Systems,2005,154(24):227-250.
[198]Li H., Lavin M A. Master R L. Fast Hough transform:A hierarchical approach[J]. Computer Vision, Graphics, and Image Processing,1986,36(23): 139-161.
[199]蔡晋辉,蒋庆,周泽魁,等.圆检测HOUGH变换的改进算法研究[J].模式识别与人工智能,2004,17(1)：72-76.
[200]Soodamani R, Liu Z Q. A Fuzzy Hough Transform Approach to Shape Description[J]. International Journal of Image and Graphics,2002,2(4):603-616.
[201]张玉华,罗飞路,孙慧贤,等.一种用于盘孔裂纹检测的差动式涡流探头的设计与实现[J].传感技术学报,2008,21(6)：1079-1083.
[202]盛业华,唐宏,杜培军,等.一种保形的快速图像形态细化算法[J].中国图象图形学报,2000,5(2)：89-93.
[203]张鹏飞.虹膜识别若干关键技术的研究[D].哈尔滨：哈尔滨工业大学,2005：19-36.
[204]代永朝,郑立胜.飞机结构检修[M].北京：航空工业出版社,2006：56-87.
[205]Cui P L, Li J H, Pan Q, et.al. Rotation and scaling invariant texture classification based on Radon transform and multiscale analysis[J]. Pattern Recognition Letters,2006,27 (5):408-413.
[206]Kourosh J K, Hamid S Z. Radon Transform Orientation Estimation for Rotation Invariant Texture Analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(6):1004-1008.
[207]武维新,张楠.典型飞行事故调查与分析方法[M].北京：国防工业出版社,2008：134-168.
[208]Gu W, Chu J X. A Transducer Made Up of Fluxgate Sensors for Testing Wire Rope Defects[J]. IEEE Transactions on Instrumentation and Measurement,2002, 51(1):120-124.