基于小波的纹理分析及其在FPC金面缺陷检测中的应用
|
摘要
随着计算机视觉的发展,纹理分析技术的研究和应用也越来越深入和广泛。然而由于自然界纹理的复杂性,目前还没发现一种通用的方法能够完美实现各种类型的纹理分析任务。在诸多纹理分析方法中基于小波的纹理分析方法是近年发展起来的较有潜力的一类方法。因小波具有较好的时频局部化能力且种类多样,这使得它特别适合对纹理图像进行处理。本文旨在研究运用小波域的特征提取方法来解决纹理分析技术中部分关键问题,并期望实现挠性印刷电路板(FPC)金面纹理缺陷的高效率自动检测。论文主要创造性研究成果如下:
针对纹理分割时小波子带能量与熵特征忽略了纹理方向及局部像素邻域关系的问题,提出基于小波包框架分解子带互补特征提取的纹理分割方案,此方案首次将梯度方向直方图描述子引入到小波包框架子带的特征提取中。在特征提取阶段,待分割的纹理图像首先利用小波包框架进行分解,然后采用所设计改进型小波包子带剔除与保留算法选择合适子带,计算出保留子带的平均绝对偏差和梯度方向直方图均值及标准差两类特征。Fisher线性区别分析与纹理分割实验显示这两类特征的组合比其中任一类单一特征的纹理区分能力都强,从而表明这两类特征具有一定互补性。在像素聚类阶段,针对纹理交界处像素误分割量较多问题,提出一种改进的空间模糊c均值聚类算法,该算法考虑了聚类像素邻域的其他像素特征值标准差的分布,实验结果显示该聚类方法在一定程度上减少了纹理交界处的误分割率。基于像素分类的纹理分割与基于像素分类的纹理缺陷检测过程相似,本文将此纹理分割方案中的关键技术应用到FPC金面缺陷检测中并实现了较准确的检测结果。
针对纹理检索时小波域的能量参数、广义高斯分布(GGD)模型及广义伽马分布(GΓD)模型方法描述某些纹理缺乏准确性而实数离散小波变换又具有平移变化性和弱方向选择性,提出一种基于复小波域局部二值模式(CLBP)算子的纹理图像检索与FPC金面缺陷检测方法。所提出的CLBP算子简单高效,能捕获局部纹理基元的结构属性。纹理检索或FPC金面缺陷检测时,采用双树复小波变换对纹理图像进行分解,对得到的复系数利用CLBP算子进行相应处理,并将处理结果的统计直方图作为纹理图像的特征矢量,最后通过计算对称Kullback-Leibler距离实现纹理检索或FPC金面缺陷检测。纹理检索与FPC金面缺陷检测两种实验结果表明,该方法较一级复小波分解的GGD及GΓD模型方法的检索准确率分别提高8.66%和5.48%,检测准确率分别提高8.75%和6.25%。
为降低特征维数构建健壮的特征,本文基于CLBP算子提出一种组合特征的纹理检索与FPC金面缺陷检测方法,将CLBP输出结果直方图降维后与复小波子带系数幅值的GΓD模型参数进行组合可形成纹理区分能力更强的特征。实验表明该组合特征方法在纹理检索和FPC金面缺陷检测准确率方面比现有的多种方法均高,同时所采用的组合特征具有旋转不变性,特征维数不高,是一种较具有实际应用价值的方法。
提出一种基于Gabor滤波器与Mean Shift聚类的完全无监督FPC金面缺陷检测方法。此方法的特点是检测过程中无需预知标准FPC金面的纹理类型及缺陷的纹理类型。检测时待检图像先经过Gabor滤波、形态学开运算、高斯平滑滤波及降维处理,然后采用Mean-shift算法对特征数据进行聚类,将聚类后的数据二值化后就得到最终检测结果。因Mean-shift算法是一种无监督的聚类方法,所以此处理过程不需将背景与缺陷纹理种类总数做为参数。实验证明此方法能检测出各种类型缺陷且对背景纹理的微小变化不敏感。
最后本文在各种FPC金面缺陷检测算法研究的基础上设计了FPC金面缺陷自动检测系统实验平台。根据FPC金面图像的特点,为顺利实现FPC金面缺陷的自动检测本文对其中的每个子系统都进行了针对性的设计。本文还为不同的检测目的提出了FPC金面检测过程的三阶段检测法,用户可根据不同要求选择合适的检测阶段与检测算法,这使程序使用的灵活性大大增加。采用大量的实验来检验整个系统的性能,各种对比性实验结果显示系统检测准确率较高,在速度、效率及稳定性方面基本胜任FPC工厂检测任务。
总之,本文针对纹理属性的捕获问题,基于小波的方法提出了几种特征抽取方案并将其成功应用到FPC金面缺陷检测的任务中。这些技术的研究对纹理分析技术理论及应用的提高具有非常重要的现实意义和参考价值。
With the development of computer vision, research and application of texture analysis technology become more and more intensive and extensive. However, due to the complexity of natural textures, not a general method which is well qualified for various types of texture analysis tasks has been found now. In the majority of texture analysis methods the wavelet-based texture analysis has been developed as a more promising method in recent years. A wavelet has good time-frequency localization ability and species diversity, which makes it particularly suitable for texture image processing. This dissertation mainly studies applying some features of wavelet domain to solve the part key problem of the current texture analysis techniques with expectation of implementing efficient defect detection for FPC gold surface. The main innovative achievements are as follows:
A special scheme of texture segmentation is proposed based on complementary feature extraction from wavelet packet frame decomposition sub-bands to solve such problem as energy and entropy features ignored the texture orientations and the relations among local neighborhood pixels in texture segmentation, where histogram of oriented gradient descriptor is firstly introduced into the feature extraction of wavelet packet frame decomposition sub-bands. In feature extraction stages of texture segmentations, the texture image to be segmented is firstly decomposed by wavelet packet frame, and then the appropriate sub-band coefficients are selected using the designed sub-band reservation/removing algorithm, and two type of features which include the average absolute deviation and the mean and standard deviation of histogram of oriented gradient are calculated from the selected sub-band coefficients. Fisher Linear Discriminate Analysis and texture segmentation experiments both show that the combination of these two types of features has stronger ability to distinguish textures than any single one, thus which suggest that these two types of features have a certain degree of complementarities between them. In pixel clustering stages of texture segmentations, an improved spatial fuzzy c means clustering method was designed for solving the problem of higher error pixel segmentation rates near the border of textures. This method takes the standard deviation distribution of neighborhood pixel features into account. Texture segmentation results show that this clustering method can reduce segment error rate of pixels near the texture border to some degree. Pixel-based texture segmentation is similar with pixel-based texture defect detection process, therefore key technologies in texture segmentation scheme is applied to defect detection of FPC gold surfaces and more accurate defect results is obtained.
A complex local binary pattern operator (CLBP) in complex wavelet domain with simplicity and efficiency is proposed for implementing the texture image retrieval and the defect detection of FPC gold surface in consideration of the reason that the existing methods using energy parameters, generalized Gaussian distribution (GGD) model parameters and generalized gamma distribution (GΓD) model parameters in wavelet domain to represent some textures lack accuracy and real discrete wavelet transform have a shift variability and weak direction selectivity. The extracted features using the simple and efficient CLBP operator can capture local texture primitive structure property to some extent. Tasks of the texture retrieval and FPC gold surface detection are both implemented firstly by decomposing texture images using dual-tree complex wavelet, and then by treating the obtained complex coefficients with CLBP whose outputting result histogram serve as a texture feature vector, and finally by calculating symmetric Kullback-Leibler divergence. Texture retrieval and FPC gold surface defect detection experiments both show that the new method is compared with GGD and GΓD model approaches of a complex wavelet decomposition to respectively improve the search accuracy rate of 8.66% and 5.48%, to respectively improve detection accuracy rate of 8.75% and 6.25%.
To further reduce the feature dimension and build more robust features a feature combination method is proposed based on CLBP operator for texture retrieval and FPC gold surface defect detection, where the reduced CLBP outputting histogram is combined with GΓD model parameters of complex wavelet coefficient amplitude to form a set of features with stronger discriminatory power. Further experiments show that this method of combing features can achieve higher retrieval and detecting accuracy than the existing variety of methods. This method has practical significance and application prospects on account of the combined feature set with characteristics of rotation invariant and low dimensions.
A completely unsupervised defect detection method for FPC gold surface based on Gabor filters and Mean-shift cluster is put forward. This method is characterized by the detecting process not requiring prior knowledge of the standard FPC gold surface texture type and defect texture type. The image to be detected pass Gabor filters, morphological open operation, a Gaussian smoothing filter and dimensionality reduction operation, and then Mean-shift clustering is applied to feature data and final detecting results are obtained by binarying the clustering outputting data. Mean-shift algorithm is an unsupervised clustering method, so this process need not take the total number of background and defect texture types as parameters. Experiments show that the designed method can detect various types of defects without sensitiveness to small variation in the background texture.
Finally, an automatic inspection system for FPC gold surface is designed based on a variety of defect detection algorithms for FPC gold surface. Each subsystem has been specially designed for smoothly implementing the automatic defect detection of FPC Gold surfaces according to characteristics of the captured image. The detection process with three stages for the purpose of the different detection is proposed so that users can choose detection stages and detection algorithms according to different practical requirements for their detection task. This kind of design makes the program more flexible for using. In this dissertation, a large number of experiments have been done to test the overall system performance, a variety of comparative experimental results show that the system have higher detecting accuracy, efficiency and stability and is basically competent for inspection tasks of FPC factories.
In short, to solve the problem of capturing the texture properties, several wavelet-based feature extraction methods are proposed and successfully apply them to defect detection tasks for the FPC gold surface. The study on these key technologies possesses important practical significance and brings important reference value for improving the theory and application of texture analysis techniques.
针对纹理分割时小波子带能量与熵特征忽略了纹理方向及局部像素邻域关系的问题,提出基于小波包框架分解子带互补特征提取的纹理分割方案,此方案首次将梯度方向直方图描述子引入到小波包框架子带的特征提取中。在特征提取阶段,待分割的纹理图像首先利用小波包框架进行分解,然后采用所设计改进型小波包子带剔除与保留算法选择合适子带,计算出保留子带的平均绝对偏差和梯度方向直方图均值及标准差两类特征。Fisher线性区别分析与纹理分割实验显示这两类特征的组合比其中任一类单一特征的纹理区分能力都强,从而表明这两类特征具有一定互补性。在像素聚类阶段,针对纹理交界处像素误分割量较多问题,提出一种改进的空间模糊c均值聚类算法,该算法考虑了聚类像素邻域的其他像素特征值标准差的分布,实验结果显示该聚类方法在一定程度上减少了纹理交界处的误分割率。基于像素分类的纹理分割与基于像素分类的纹理缺陷检测过程相似,本文将此纹理分割方案中的关键技术应用到FPC金面缺陷检测中并实现了较准确的检测结果。
针对纹理检索时小波域的能量参数、广义高斯分布(GGD)模型及广义伽马分布(GΓD)模型方法描述某些纹理缺乏准确性而实数离散小波变换又具有平移变化性和弱方向选择性,提出一种基于复小波域局部二值模式(CLBP)算子的纹理图像检索与FPC金面缺陷检测方法。所提出的CLBP算子简单高效,能捕获局部纹理基元的结构属性。纹理检索或FPC金面缺陷检测时,采用双树复小波变换对纹理图像进行分解,对得到的复系数利用CLBP算子进行相应处理,并将处理结果的统计直方图作为纹理图像的特征矢量,最后通过计算对称Kullback-Leibler距离实现纹理检索或FPC金面缺陷检测。纹理检索与FPC金面缺陷检测两种实验结果表明,该方法较一级复小波分解的GGD及GΓD模型方法的检索准确率分别提高8.66%和5.48%,检测准确率分别提高8.75%和6.25%。
为降低特征维数构建健壮的特征,本文基于CLBP算子提出一种组合特征的纹理检索与FPC金面缺陷检测方法,将CLBP输出结果直方图降维后与复小波子带系数幅值的GΓD模型参数进行组合可形成纹理区分能力更强的特征。实验表明该组合特征方法在纹理检索和FPC金面缺陷检测准确率方面比现有的多种方法均高,同时所采用的组合特征具有旋转不变性,特征维数不高,是一种较具有实际应用价值的方法。
提出一种基于Gabor滤波器与Mean Shift聚类的完全无监督FPC金面缺陷检测方法。此方法的特点是检测过程中无需预知标准FPC金面的纹理类型及缺陷的纹理类型。检测时待检图像先经过Gabor滤波、形态学开运算、高斯平滑滤波及降维处理,然后采用Mean-shift算法对特征数据进行聚类,将聚类后的数据二值化后就得到最终检测结果。因Mean-shift算法是一种无监督的聚类方法,所以此处理过程不需将背景与缺陷纹理种类总数做为参数。实验证明此方法能检测出各种类型缺陷且对背景纹理的微小变化不敏感。
最后本文在各种FPC金面缺陷检测算法研究的基础上设计了FPC金面缺陷自动检测系统实验平台。根据FPC金面图像的特点,为顺利实现FPC金面缺陷的自动检测本文对其中的每个子系统都进行了针对性的设计。本文还为不同的检测目的提出了FPC金面检测过程的三阶段检测法,用户可根据不同要求选择合适的检测阶段与检测算法,这使程序使用的灵活性大大增加。采用大量的实验来检验整个系统的性能,各种对比性实验结果显示系统检测准确率较高,在速度、效率及稳定性方面基本胜任FPC工厂检测任务。
总之,本文针对纹理属性的捕获问题,基于小波的方法提出了几种特征抽取方案并将其成功应用到FPC金面缺陷检测的任务中。这些技术的研究对纹理分析技术理论及应用的提高具有非常重要的现实意义和参考价值。
With the development of computer vision, research and application of texture analysis technology become more and more intensive and extensive. However, due to the complexity of natural textures, not a general method which is well qualified for various types of texture analysis tasks has been found now. In the majority of texture analysis methods the wavelet-based texture analysis has been developed as a more promising method in recent years. A wavelet has good time-frequency localization ability and species diversity, which makes it particularly suitable for texture image processing. This dissertation mainly studies applying some features of wavelet domain to solve the part key problem of the current texture analysis techniques with expectation of implementing efficient defect detection for FPC gold surface. The main innovative achievements are as follows:
A special scheme of texture segmentation is proposed based on complementary feature extraction from wavelet packet frame decomposition sub-bands to solve such problem as energy and entropy features ignored the texture orientations and the relations among local neighborhood pixels in texture segmentation, where histogram of oriented gradient descriptor is firstly introduced into the feature extraction of wavelet packet frame decomposition sub-bands. In feature extraction stages of texture segmentations, the texture image to be segmented is firstly decomposed by wavelet packet frame, and then the appropriate sub-band coefficients are selected using the designed sub-band reservation/removing algorithm, and two type of features which include the average absolute deviation and the mean and standard deviation of histogram of oriented gradient are calculated from the selected sub-band coefficients. Fisher Linear Discriminate Analysis and texture segmentation experiments both show that the combination of these two types of features has stronger ability to distinguish textures than any single one, thus which suggest that these two types of features have a certain degree of complementarities between them. In pixel clustering stages of texture segmentations, an improved spatial fuzzy c means clustering method was designed for solving the problem of higher error pixel segmentation rates near the border of textures. This method takes the standard deviation distribution of neighborhood pixel features into account. Texture segmentation results show that this clustering method can reduce segment error rate of pixels near the texture border to some degree. Pixel-based texture segmentation is similar with pixel-based texture defect detection process, therefore key technologies in texture segmentation scheme is applied to defect detection of FPC gold surfaces and more accurate defect results is obtained.
A complex local binary pattern operator (CLBP) in complex wavelet domain with simplicity and efficiency is proposed for implementing the texture image retrieval and the defect detection of FPC gold surface in consideration of the reason that the existing methods using energy parameters, generalized Gaussian distribution (GGD) model parameters and generalized gamma distribution (GΓD) model parameters in wavelet domain to represent some textures lack accuracy and real discrete wavelet transform have a shift variability and weak direction selectivity. The extracted features using the simple and efficient CLBP operator can capture local texture primitive structure property to some extent. Tasks of the texture retrieval and FPC gold surface detection are both implemented firstly by decomposing texture images using dual-tree complex wavelet, and then by treating the obtained complex coefficients with CLBP whose outputting result histogram serve as a texture feature vector, and finally by calculating symmetric Kullback-Leibler divergence. Texture retrieval and FPC gold surface defect detection experiments both show that the new method is compared with GGD and GΓD model approaches of a complex wavelet decomposition to respectively improve the search accuracy rate of 8.66% and 5.48%, to respectively improve detection accuracy rate of 8.75% and 6.25%.
To further reduce the feature dimension and build more robust features a feature combination method is proposed based on CLBP operator for texture retrieval and FPC gold surface defect detection, where the reduced CLBP outputting histogram is combined with GΓD model parameters of complex wavelet coefficient amplitude to form a set of features with stronger discriminatory power. Further experiments show that this method of combing features can achieve higher retrieval and detecting accuracy than the existing variety of methods. This method has practical significance and application prospects on account of the combined feature set with characteristics of rotation invariant and low dimensions.
A completely unsupervised defect detection method for FPC gold surface based on Gabor filters and Mean-shift cluster is put forward. This method is characterized by the detecting process not requiring prior knowledge of the standard FPC gold surface texture type and defect texture type. The image to be detected pass Gabor filters, morphological open operation, a Gaussian smoothing filter and dimensionality reduction operation, and then Mean-shift clustering is applied to feature data and final detecting results are obtained by binarying the clustering outputting data. Mean-shift algorithm is an unsupervised clustering method, so this process need not take the total number of background and defect texture types as parameters. Experiments show that the designed method can detect various types of defects without sensitiveness to small variation in the background texture.
Finally, an automatic inspection system for FPC gold surface is designed based on a variety of defect detection algorithms for FPC gold surface. Each subsystem has been specially designed for smoothly implementing the automatic defect detection of FPC Gold surfaces according to characteristics of the captured image. The detection process with three stages for the purpose of the different detection is proposed so that users can choose detection stages and detection algorithms according to different practical requirements for their detection task. This kind of design makes the program more flexible for using. In this dissertation, a large number of experiments have been done to test the overall system performance, a variety of comparative experimental results show that the system have higher detecting accuracy, efficiency and stability and is basically competent for inspection tasks of FPC factories.
In short, to solve the problem of capturing the texture properties, several wavelet-based feature extraction methods are proposed and successfully apply them to defect detection tasks for the FPC gold surface. The study on these key technologies possesses important practical significance and brings important reference value for improving the theory and application of texture analysis techniques.
引文
[1]马莉,范影乐.纹理图像分析[M].北京:科学出版社,2009:1-8.
[2] Campbell F.W, Robson J.G. Application of Fourier Analysis to the Visibility of Gratings [J]. Journal of Physiology. 1968, 197(3):551-566.
[3] De Valois R.L, Albrecht D.G., Thorell L.G. Spatial-Frequency Selectivity of Cells in Macaque Visual Cortex[J]. Vision Research. 1982, 22(5):545-559.
[4] Hawkins J.K. Textural Properties for Pattern Recognition[M]. New York: Academic Press. 1970:347-370.
[5] Haralick R.M. Statistical and Structural Approaches to Texture[A]. Proceedings of the IEEE[C] IEEE 1979,67(5):786-804
[6] Tamura H, Mori S, Yamawaki T. Textural Features Corresponding to Visual Perception [J] . IEEE Transactions on Systems, Man, and Cybernetics. 1978,SMC-8(6): 460-473.
[7] Xie X.H. A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques [J]. Electronic Letters on Computer Vision and Image Analysis, 2008, 7(3):1-22.
[8] Wong W.K, Yuen C.W.M, Fan D.D. Stitching Defect Detection and Classification Using Wavelet Transform and BP Neural Network[J]. Expert Systems with Applications. 2009,36(2): 3845-3856
[9] Ngan H.Y.T, Pang G.K.H, Yung S.P. Wavelet Based Methods on Patterned Fabric Defect Detection[J]. Pattern Recognition. 2005, 38(4):559-576.
[10] Julesz B,Gilbert E.N, Shepp L.A,et al. Inability of Humans to Diseriminate Between Visual Textures That Agree Insecond-Order Statisties-Revisited[J]. Perception. 1973,2(4): 391-405.
[11] Boukouvalas C, Kittler J, Marik R, et al. Color Grading of Randomly Textured Ceramic Tiles Using Color Histograms[J]. IEEE Transactions on Industry Electronics, 1999, 46(1): 219-226.
[12] Broadhurst R, Stough J, Pizer S, et al. Histogram statistics of local model-relative image regions [A]. 1st International Workshop on Deep Structure, Singularities, and Computer Vision[C]. Berlin:Springer Verlag 2005:72-83
[13] Pietikainen M, Maenpaa T, and Viertola J. Color Texture Classification with Color Histograms and Local Binary Patterns[A]. In International Workshop on Texture Analysis and Synthesis[C]. Copenhagen, 2002:109-112.
[14] Xie X, Mirmehdi M, Thomas B. Colour Tonality Inspection Using Eigenspace Features [J]. Machine Vision and Applications. 2006, 16(6):364-373.
[15] Tremeau A, Bousigue J, Laget B, Co-Occurrence Shape Descriptors Applied to Texture Classification and Segmentation[J]. SPIE 1996, 2665: 135-147.
[16] Soh L, Tsatsoulis C. Texture Analysis of SAR Sea Ice Imagery[J]. IEEE Transactions On Geoscience And Remote Sensing, 1999, 37(2) :780-795.
[17] Conners R.W, McMillan C. W, Lin K, et al. Identifying and Locating Surface Defects in Wood: Part of An Automated Lumber Processing System[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1983, 6(10): 573-583.
[18] Tsai I, Lin C, Lin J. Applying An Artificial Neural Network to Pattern Recognition in Fabric Defects[J]. Textile Research Journal. 1995,65(3): 123-130.
[19] R?sler R.N.U. Defect Detection in Fabrics By Image Processing[J]. Mellind Textilberi -chte, 1992, 73:635-636.
[20] Ojala T, Pietik?inen M, Harwood D, A Comparative Study of Texture Measures with Classification Based on Feature Distributions [J]. Pattern Recognition 1996, 29(1):51-59.
[21] Ojala T, Pietik?inen M, M?enp?? T.T, Multiresolution Gray-Scale and Rotation Invariant Texture Classification With Local Binary Pattern[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2002, 24(7): 971-987.
[22] Guo Z, Zhang L, Zhang D. Rotation Invariant Texture Classification Using LBP Variance (LBPV) with Global Matching[J]. Pattern Recognition. 2010, 43(3) :706-719.
[23] Guo Z, Zhang L, Zhang D. A Completed Modeling of Local Binary Pattern Operator for Texture Classification[J]. IEEE Transactions on Image Processing, 2011, 19 (6):1657-1663.
[24] Li M, Staunton* R.C. Optimum Gabor Filter Design and Local Binary Patterns for Texture Segmentation[J]. Pattern Recognition Letters, 2008, 29(5) : 664-672.
[25] Niskanen M, Silv′en O, Kauppinen H. Color and Texture Based Wood Inspection with Nonsupervised Clustering[A]. In Proceedings of the 12th Scandinavian Conference on Image Analysis[C]. Bergen, 2001:336-342
[26] Niskanen M, Kauppinen H, Silv′en O. Real-Time Aspects of SOM-Based Visual Surface Inspection[A].Proceedings SPIE Machine Vision Applications in Industrial. Inspection[C]. San Jose :SPIE 2002:123-134.
[27] Tuceryan M. Moment Based Texture Segmentation[J]. Pattern Recognition Letters, 1994, 4(7):659-668.
[28] Khotanzad A, Hong Y.H, Invariant Image Recognition by Zernike Moments[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1990, 12 (5):289-497
[29] Hu M, Visual Pattern Recognition by Moment Invariants[J]. Information Theory, IRE Transactions on. 1962, 8(2):179-187.
[30] Bailey R.R, Srinath M. Orthogonal Moment Features for Use With Parametric and Non-Parametric Classiers[J]. IEEE.Trans. on Pattern Analysis and Machine Intelligence. 1996, 18 (4):389-399.
[31] Teh C.-H, Chin R.T. on Image Analysis by the Methods of Moments[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 10 (1988) 496-513.
[32] Wang L, Healey G. Using Zernike Moments for the Illumination and Geometry Invariant Classification of Multispectral Texture[J]. IEEE Trans. Image Process. 1998, 7 (2) :196-203.
[33] Vilnrotter F, Nevatia R, Price K. Structural Analysis of Natural Textures[J]. IEEE Transaction. on Pattern Analysis and Machine Intelligence, 1986, 8(1):76-89
[34] Hong T, Dyer C, Rosenfeld A. Texture Primitive Extraction Using An Edge-Based Approach[J]. IEEE Transactions on Systems, Man and Cybernetics. 1980,10(10): 659-675
[35] Lu S.Y, Fu K.S. A Syntactic Approach to Texture Analysis[J]. Computer Graphics Image Processing. 1978, 7(3): 303-330
[36] Hsu J, Liu L.C, Li C. Determination of Structure Component in Image Texture Using Wavelet Analysis[A]. International Conference on Image Processing[C]. Thessaloniki :IEEE, 2001,3:166-169
[37] Umarani C, Ganesan L, Radhakrishnan S. Combined Statistical and Structural Approach for Unsupervised Texture Classification[J]. International Journal Of Imaging Science And Engineering 2008,2(1):162-165.
[38] Chen J, Jain A. A Structural Approach to Identify Defects in Textured Images[A]. IEEE International Conference on Systems, Man, and Cybernetics[C]. Beijing:IEEE, 1988, 1: 29–32.
[39] Besag J.E. Spatial Interaction and The Statistical Analysis Of Lattice Systems[J]. Journal of the Royal Statistical Socciety. 1974, 36:192-326.
[40] Derin H, Elliott H. Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1987,9 (1):39-55
[41] Manjunath B.S, Chellappa R. Unsupervised Texture Segmentation Using Markov Random Field Models[J]. IEEE Transaction on Pattern Analysis and Machine Intelligence, 1991, 13(5):478-482
[42] Kato Z, Pong T.C. A Markov Random Field Image Segmentation Model for Color Textured images[J]. Image and Vision Computing 2006, 24:1103-1114.
[43] Noda H, Shirazi M, Kawaguchi E. MRF-Based Texture Segmentation Using Wavelet Decomposed Images[J]. Pattern Recognition 2002, 35(4):771-782.
[44] 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,.
[45] Mandelbrot B, The Fractal Geometry of Nature[M]. New York: W. H. Freeman and Company. 1982.
[46] Field D.J. Relations between the statistics of natural images and the response properties of cortical cells[J]. Journal Optical Society America, 1987, 4(12):2379-2394
[47] Attali S. F, Cohen F. S. Surface Inspection Based on Stochastic Modeling[A]. Proc. SPIE [C] Bellingham:SPIE,1986, 665: 46-52.
[48] Francos J.M, Meiri A.Z, Porat B. A Unified Texture Model Based on A 2-D Wold-Like Decomposition[J]. IEEE Transactions on Signal Processing. 1993, 41(8):2665– 2678.
[49] Fracos J.M, Narasimhan A, Woods J.W. Maximum Likelihood Parameter Estimation of Textures Using A Wold-Decomposition Based Model[J]. IEEE Trans. Image Process. 1995, 4 (12) :1655-1666
[50] Liu F, Picard R.W. Periodicity, Directionality, And Randomness: Wold Features For Perceptional Pattern Recognition[A]. Proceedings of the International Conference on Pattern Recognition[C]. Los Alamitos :IEEE,1994, 11:184-185.
[51] Liu F, Picard R.W. Periodicity, Directionality, And Randomness: Wold Features Of Image And Modeling And Retrieval[J]. IEEE Trans. Pattern Anal. Mach. Intell. 1996, 18 (7):722–733
[52] Laws K.I. Rapid Texture Identification[A]. Proc.of the SPIE Conference on Image Processing for Missile Guidance[C]. Bellingham:SPIE, 1980, 376-380
[53] Rachidi M, Marchadier A, Gadois C. Laws' Masks Descriptors Applied to Bone Texture Analysis: An Innovative And Discriminant Tool in Osteoporosis[J]. Skeletal Radiol. 2008, 37(6):541-8.
[54] Du Buf J.M.H, Spann M, Kardan M. Texture Feature Performance for Image Segmen-tation[J]. Pattern Recognition. 1990, 23(3-4): 291-309
[55] Gabor D. Theory of Communications[J]. Journal of International Electrical Engineers, 1946, 93:427–457
[56] Granlund G. H. In Search of A General Picture Processing Operator[J]. Computer Graphics and Image Processing, 1978, 8(2):155–173
[57] Daugman J.G. Uncertainty Relation for Resolution in Pace, Spatial-Frequency, and Orientation Optimized by Two Dimensional Visual Cortical Filters[J]. Journal of the Optical Society of America A: Optics, Image Science, and Vision. 1985, 2(7):1160 -1169.
[58] Devalois R.L. Spatial-Frequency Selectivity Of Cells In Macaque Visual Cortex[J]. Vision Research. 1982, 22(5):545-559
[59] Jain A.K, Farrokhnia F. Unsupervised Texture Segmentation Using Gabor Filters[J]. Pattern Recognition. 1991, 24(12):1167-1186
[60] Weldon T.P, Higgins W.E, Dunn D.F. Efficient Gabor Filter Design for Texture Segmentation[J]. Pattern Recognition. 1996, 29(12): 2005-2015
[61]马江林,赵忠明,汪承义等.一种新的基于Gabor小波的非监督纹理分割方法[J].武汉大学学报·信息科学版2009, 34(1) :11-18
[62]王敏琴,韩国强,涂泳秋.新型的无监督纹理分割方法[J].电子科技大学学报2010, 39(1):11-15
[63] Han J, Ma K-K. Rotation-Invariant and Scale-Invariant Gabor Features for Texture Image Retrieval[J]. Image and Vision Computing 2007, 25(9):1474–1481
[64] Bianconi F, Fernandez A. Evaluation Of The Effects of Gabor Filter Parameters on Texture Classification[J]. Pattern Recognition. 2007, 40(12):3325-3335
[65] Mak K.L, Peng P. An Automated Inspection System for Textile Fabrics Based On Gabor Filters[J]. Robotics and Computer-Integrated Manufacturing. 2008, 24(3):359–369
[66] Kumar A, Pang G. Defect Detection in Textured Materials Using Gabor Filters[J]. IEEE Transactions on Industry Applications. 2002, 38(2):425–440
[67] Tsai D.M, Lin C.P. Fast Defect Detection in Textured Surfaces Using 1D Gabor Filters[J]. The International Journal of Advanced Manufacturing Technology. 2002, 20(9):664-675
[68] Bodnarova A, Bennamoun M, Latham S. Optimal Gabor Filters For Textile Flaw Detection [J]. Pattern Recognition. 2002,35(12):2973–2991,.
[69] Yun J P, Choi S, Kim J-W, et al. Automatic Detection Of Cracks in Raw Steel BlockUsing Gabor Filter Optimized by Univariate Dynamic Encoding Algorithm for Searches (uDEAS) [J]. NDT&E International. 2009, 42(5):389–397
[70] Kumar A, Pang G. Fabric Defect Segmentation Using Multichannel Blob Detectors[J]. Optical Engineering, 2000, 39(12):3176–3190
[71] Wiltschi K, Pinz A., Lindeberg T. An Automatic Assessment Scheme for Steel Quality Inspection[J]. Machine Vision and Applications. 2000, 12(3):113–128
[72] Bennamoun M, Bodnarova A. Digital Image Processing Techniques For Automatic Textile Quality Control[J]. Systems Analysis Modelling Simulation, 2003,43(11): 1581-1614
[73] Tsai D, Lin C, Huang K. Defect Detection in Coloured Texture Surfaces Using Gabor Filters [J]. Imaging Science Journal, 2005,53(1):27–37,. [ 74 ] Mallat S.G. A Theory for Multiresolution Signal Decomposition: the Wavelet Representation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1989, 11(7):674-693
[75] Wang J.W, Chen C.H, Chien W.M. Etc. Texture Classification Using Non-Separable Two-Dimensional Wavelets[J]. Pattern Recognition Letters. 1998, 19(13):1225-1234.
[76] Turkoglu I, Avci E. Comparison Of Wavelet-SVM and Wavelet-Adaptive Network Based Fuzzy Inference System for Texture Classification[J]. Digital Signal Processing 2008,18(1) :15–24
[77] Hiremath P.S, Shivashankar S. Wavelet Based Co-Occurrence Histogram Features for Texture Classification with An Application to Script Identification in A Document Image[J]. Pattern Recognition Letters, 2008, 29(9):1182–1189
[78] Bashar M.K, Matsumoto T, Ohnishi N. Wavelet Transform-Based Locally Orderless Images For Texture Segmentation[J]. Pattern Recognition Letters, 2003, 24:2633–2650.
[79] Muneeswaran K, Ganesan L, Arumugam S. et al. Texture Classification with Combined Rotation and Scale Invariant Wavelet Features[J]. Pattern Recognition, 2005, 38(10):1495-1506.
[80] Yu G, Kamarthi S.V. A Cluster-Based Wavelet Feature Extraction Method and Its Application[J]. Engineering Applications of Artificial Intelligence. 2010, 23(2):196–202.
[81] Avci E, Sengur A, Hanbay D. An Optimum Feature Extraction Method for Texture Classification[J]. Expert Systems with Applications, 2009, 36(3):6036-6043
[82] Unser M. Texture Classification and Segmentation Using Wavelet Frames[J]. IEEE Trans. Image Process, 1995, 4(11):1549–1560.
[83] Avci E. Comparison Of Wavelet Families for Texture Classification by Using Wavelet Packet Entropy Adaptive Network Based Fuzzy Inference System[J]. Applied Soft Computing 2008, 8(1):225–231
[84] Sengur A, Turkoglu I, Ince M.C. Wavelet Packet Neural Networks for Texture Classification[J]. Expert Systems with Applications, 2007, 32(2):527–533.
[85] Jiang X W, Zhao R.C. Texture Segmentation Based on Incomplete Wavelet Packet Frame [A]. Proceedings of the Second International Conference on Machine Learning and Cybernetics, Wan[C]. Xi’an:IEEE, 2003:2-5.
[86] Acharyya M, De R.K., Kundu M.K. Extraction of Features Using M-Band Wavelet Packet Frame and Their Neuro-Fuzzy Evaluation for Multitexture Segmentation[J]. IEEE Transactions on Pattern Analysis And Machine Intelligence, 2003, 25(12): 1639-1644
[87] Huang K, Aviyente S. Information-Theoretic Wavelet Packet Subband Selection for Texture Classification[J]. Signal Processing, 2006, 86(7):1410–1420
[88] Kim S.C, Kang T.J. Texture Classification and Segmentation Using Wavelet Packet Frame and Gaussian Mixture Model[J]. Pattern Recognition, 2007, 40(4):1207-1221.
[89] Wouwer G. V, Scheunders P, Dyck D. V. Statistical Texture Characterization from Discrete Wavelet Representations[J]. IEEE Trans. Image Processing, 1999, 8(4): 592-598.
[90] Do M.N, Vetterli M. Wavelet-based Texture Retrieval Using Generalized Gaussian Density and Kullback-Leibler Distance[J]. IEEE Trans. Image Process. 2002, 11(2):146-158.
[91] Choy S. K, Tong C. S. Supervised Texture Classification Using Characteristic Generalized Gaussian Density[J]. J. Math. Imag. Vis. 2007, 29(1):35–47.
[92] Pi M, Tong C.S, Choy S. K, et al.A Fast and Effective Model for Wavelet Subband Histograms and Its Application in Texture Image Retrieval[J]. IEEE Trans. Image Process. 2006, 15(10): 3078-3088.
[93] Choy S. K. Tong C. S. Statistical Properties Of Bit-Plane Probability Model and Its Application in Supervised Texture Classification[J]. IEEE Trans. Image Process. 2008,17(8): 1399-1405.
[94] Romberg J. K, Choi H, Baraniuk R. G. Bayesian Tree-Structured Image Modeling Using Wavelet-Domain Hidden Markov Models[J]. IEEE Trans. Image Process,2001,10(7):1056-1068
[95] Do M.N, Vetterli M. Rotation Invariant Texture Characterization And Retrieval Using Steerable Wavelet-Domain Hidden Markov Models[J]. IEEE Trans. Multimedia, 2002, 4,(12): 517-527
[96] Choi H, Baraniuk R.G. Multiscale Texture Segmentation Using Wavelet-Domain Hidden Markov Models[J]. IEEE Trans. Image Process., 2001,10(9):1309-1321
[97] Choy S.K, Tong C.S. Statistical Wavelet Subband Characterization Based on Generalized Gamma Density and Its Application in Texture Retrieval[J]. IEEE Transactions on Image Processing, 2010, 19(2): 281-289.
[98] Benkuider A, Aarab A. Content Based Image Retrieval Using the Generalized Gamma Density to Model BEMD’s IMFs[J]. Journal Of Computers, 2011, 6(6):1168-1174.
[99] Celik T, Tjahjadi T. Multiscale Texture Classification Using Dual-Tree Complex Wavelet Transform[J]. Pattern Recognition Letters, 2009, 30(3):331-339.
[100]尚赵伟,唐远炎,房斌等.基于相对熵和复小波变换的纹理图像检索[J].重庆大学学报, 2008, 31(5):244-247
[101] Qiao Y.L, Zhao C.H, Song C.Y. Complex Wavelet Based Texture Classification[J]. Neuro Computing, 2009, 72(16-18):3957-3963
[102] Vo A, Oraintara S. A Study of Relative Phase In Complex Wavelet Domain: Property, Statistics And Applications In Texture Image Retrieval And Segmentation[J]. Signal Processing: Image Communication, 2010, 25(1): 28-46.
[103] Sari-Sarraf H, Goddard J. Vision Systems for On-Loom Fabric Inspection[J]. IEEE Transactions on Industry Applications, 1999, 35(6):1252-1259.
[104] Kim S, Lee M, Woo K. Wavelet Analysis to Fabric Defects Detection in Weaving Processes[A]. In Proceedings of IEEE International Symposium on Industrial Electronics[C]. New Jercy:IEEE 1999, 3:1406–1409.
[105] Yang X, Pang G, Yung N. Robust Fabric Defect Detection And Classification Using Multiple Adaptive Wavelets[A]. IEE Proceedings Vision, Image Processing[C]. IET, 2005, 152(6):715–723.
[106] Han Y.F, Shi P.F. An Adaptive Level-Selecting Wavelet Transform for Texture Defect Detection[J]. Image and Vision Computing, 2007, 25(8):1239–1248.
[107] Yang X, Pang G, Yung N. Fabric Defect Classification Using Wavelet Frames And Minimum Classification Error Training[A]. In 37th IAS Annual Meeting. ConferenceRecord of the Industry Applications Conference[C]. Pittsburgh:IEEE. 2002,1:290-292,.
[108] Arivazhagan S, Ganesan L, Subash Kumar T. G. Texture classification using Curvelet Statistical and Co-occurrence Features[A]. 18th International Conference on Pattern Recognition (ICPR'06) [C]. New Jersey:IEEE, 2006,2:938-941
[109] Zhao Y. F, Xia L.Z. Contourlet-Based Feature Extraction on Texture Images[J]. Interna -tional Conference on Computer Science and Software Engineering. 2008, 6:221-224
[110]曲怀敬, Contourlet变换在纹理图像检索和医学图像分割中的应用研究[D].山东大学博士学位论文, 2009
[111] He Z.Y, You X.G, YuanY. Texture Image Retrieval Based on Non-Tensor Product Wavelet Filter Banks[J]. Signal Processing, 2009, 89(8):1501–1510
[112] Charalampidis D, Kasparis T. Wavelt-Based Rotational Invalriant Roughness Features for Texture Classification and Segmentation[J]. IEEE Trans. Image Processing, 2002, 11(8): 825-837
[113] Laine A, Fan J. Texture Classification by Wavelet Packet Signatures[J]. IEEE Transaction on Pattern Analysis and Machine Intelligence, 1993, 15(11):1186-1191
[114] Kingsbury N.G. The Dual-Tree Complex Wavelet Transform: A New Efficient Tool For Image Restoration And Enhancement[J]. in Proc. European Signal Processing Conf., Rhodes, 1998:319–322.
[115] 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
[116] Kingsbury N.G. A Dual-Tree Complex Wavelet Transform with Improved Orthogonality And Symmetry Properties[A]. In Proc. IEEE Int. Conf. Image Processing, Vancouver, BC, Canada[C]. Vancouver:IEEE, 2000, 2: 375–378.
[117] Bezdec J.C. Pattern Recognition with Fuzzy Objective Function Algorithms[M]. New York:Plenum Press, 1981:10-30;
[118] Ahmed, M.N, Yamany S.M, Mohamed N, et al. A Modified Fuzzy C-Means Algorithm for Bias Field Estimation And Segmentation of MRI Data[J]. IEEE Trans. Med. Imaging, 2002, 21(3):193–199
[119] Chen S.C, Zhang D.Q. Robust Image Segmentation Using FCM with Spatial Constraints Based on New Kernel-Induced Distance Measure[J]. IEEE Transactions on Systems Man and Cybernetics, 2004, 34(4):1907-1916.
[120] Dalai N, Triggs B. Histograms of Oriented Gradients for Human Detection[A]. IEEEComputer Society Conference on Computer Vision and Pattern Recognition[C]. San Diego:IEEE 2005, 1:886-893.
[121] Bertozzi M, Broggi A, Del Rose M. A Pedestrian Detector Using Histograms of Oriented Gradients and a Support Vector Machine Classifier[A]. 10th International IEEE Conference on Intelligent Transportation Systems, ITSC2007[C]. New Jersey: IEEE, 2007, 3:143-148
[122] Zhu Q, Avidan S, Yeh M.C. Fast Human Detection Using a Cascade of Histograms of Oriented Gradients[A]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition[C] 2006, 2:1491-1498.
[123] Chuang C.H, Huang S.S, Fu L.C. Monocular Multi-Human Detection Using Aug- mented Histograms of Oriented Gradients[A]. 2008 19th International Conference on Pattern Recognition [C]. New Jersey:IEEE, 2008:1-4
[124] Acharyya M, Kundu M.K. Adaptive Basis Selection for Multitexture Segmentation by M-Band Wavelet Packet Frame[A]. Proc. 2001 Int’l Conf.Image Processing[C]. Thessaloniki:IEEE, 2001, 2:622-625
[125] Thomaz C.E, Boardman J P , Counsell S, et al. A Multivariate Statistical Analysis of The Developing Human Brain in Preterm Infants[J]. Image and Vision Computing, 2007, 25 (6) :981-994
[126] Lin H.D, Chung C.Y, Lin W.T. Principal Component Analysis Based on Wavelet Characteristics Applied to Automated Surface Defect Inspection[J]. WS EA S Transac -tions on Computer Research, 2008, 3(4):193-202.
[127] Sharifi K, Leon-Garcia A. Estimation of Shape Parameter for Generalized Gaussian Distributions in Subband Decompositions of Video[J]. IEEE Trans. Circuits Syst. Video Technol, 1995, 5(1):52–56
[128] Moulin P, Liu J. Analysis of Multiresolution Image Denoising Schemes Using Generalized Gaussian And Complexity Priors[J]. IEEE Trans. Inform. Theory, 1999, 45(3): 909-919.
[129] Varanasi M.K, Aazhang B. Parametric Generalized Gaussian Density Estimation[J] J. Acoust. Soc. Amer. 1989, 86(4):1404–1415.
[130] Stacy E.W. A Generalization of The Gamma Distribution[J]. Ann. Math. Statist. 1962, 33(13): 1187-1192
[131] Shin J.W, Chang J.H, Kim N.S. Statistical Modeling of Speech Signals Based on Generalized Gamma Distribution[J]. IEEE SignalProcess. Lett., 2005,12(3):258-261
[132]高丛珊,张红,王超等.广义Gamma模型及自适应KI阈值分割的SAR图像变化检测[J].遥感学报, 2010, 14(4):718-732
[133] Comaniciu D, Ramesh V, Meer P. Real-Time Tracking of Non-Rigid Objects using Mean Shift[A]. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition[C]. California: IEEE, 2000, 2:142-149.
[134] D. Comaniciu, P. Meer: Mean Shift: A Robust Approach toward Feature Space Analysis[J]. IEEE Trans. Pattern Analysis Machine Intell., 2002, 24(5), 603-619
[135] Zhand J.G, Tan T.M. Invariant Texture Segmentation Via Cirular Gabor Filter[A]. 16th International Conference on Pattern Recognition, Austalian, Computer Society[C]. 2002: 901-904
[136]袁端磊,宋寅卯.基于最优Gabor滤波器的织物缺陷检测[J].中国图象图形学报, 2006,11(7):954-958.
[137]徐胜海.印刷电路板在线自动视觉检测系统的分析研究[D].合肥工业大学硕士学位论文, 2001
[138]刘良江.先进电子制造生产线机器视觉检测方法与技术研究[D].湖南大学博士学位论文, 2009.10
[139] Tsai D.M, Lin B.T. Defect Detection Of Gold-Plated Surfaces On Pcbs Using Entropy Measures[J]. The International Journal of Advanced Manufacturing Technology, 2002, 20(6):420-428
[2] Campbell F.W, Robson J.G. Application of Fourier Analysis to the Visibility of Gratings [J]. Journal of Physiology. 1968, 197(3):551-566.
[3] De Valois R.L, Albrecht D.G., Thorell L.G. Spatial-Frequency Selectivity of Cells in Macaque Visual Cortex[J]. Vision Research. 1982, 22(5):545-559.
[4] Hawkins J.K. Textural Properties for Pattern Recognition[M]. New York: Academic Press. 1970:347-370.
[5] Haralick R.M. Statistical and Structural Approaches to Texture[A]. Proceedings of the IEEE[C] IEEE 1979,67(5):786-804
[6] Tamura H, Mori S, Yamawaki T. Textural Features Corresponding to Visual Perception [J] . IEEE Transactions on Systems, Man, and Cybernetics. 1978,SMC-8(6): 460-473.
[7] Xie X.H. A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques [J]. Electronic Letters on Computer Vision and Image Analysis, 2008, 7(3):1-22.
[8] Wong W.K, Yuen C.W.M, Fan D.D. Stitching Defect Detection and Classification Using Wavelet Transform and BP Neural Network[J]. Expert Systems with Applications. 2009,36(2): 3845-3856
[9] Ngan H.Y.T, Pang G.K.H, Yung S.P. Wavelet Based Methods on Patterned Fabric Defect Detection[J]. Pattern Recognition. 2005, 38(4):559-576.
[10] Julesz B,Gilbert E.N, Shepp L.A,et al. Inability of Humans to Diseriminate Between Visual Textures That Agree Insecond-Order Statisties-Revisited[J]. Perception. 1973,2(4): 391-405.
[11] Boukouvalas C, Kittler J, Marik R, et al. Color Grading of Randomly Textured Ceramic Tiles Using Color Histograms[J]. IEEE Transactions on Industry Electronics, 1999, 46(1): 219-226.
[12] Broadhurst R, Stough J, Pizer S, et al. Histogram statistics of local model-relative image regions [A]. 1st International Workshop on Deep Structure, Singularities, and Computer Vision[C]. Berlin:Springer Verlag 2005:72-83
[13] Pietikainen M, Maenpaa T, and Viertola J. Color Texture Classification with Color Histograms and Local Binary Patterns[A]. In International Workshop on Texture Analysis and Synthesis[C]. Copenhagen, 2002:109-112.
[14] Xie X, Mirmehdi M, Thomas B. Colour Tonality Inspection Using Eigenspace Features [J]. Machine Vision and Applications. 2006, 16(6):364-373.
[15] Tremeau A, Bousigue J, Laget B, Co-Occurrence Shape Descriptors Applied to Texture Classification and Segmentation[J]. SPIE 1996, 2665: 135-147.
[16] Soh L, Tsatsoulis C. Texture Analysis of SAR Sea Ice Imagery[J]. IEEE Transactions On Geoscience And Remote Sensing, 1999, 37(2) :780-795.
[17] Conners R.W, McMillan C. W, Lin K, et al. Identifying and Locating Surface Defects in Wood: Part of An Automated Lumber Processing System[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1983, 6(10): 573-583.
[18] Tsai I, Lin C, Lin J. Applying An Artificial Neural Network to Pattern Recognition in Fabric Defects[J]. Textile Research Journal. 1995,65(3): 123-130.
[19] R?sler R.N.U. Defect Detection in Fabrics By Image Processing[J]. Mellind Textilberi -chte, 1992, 73:635-636.
[20] Ojala T, Pietik?inen M, Harwood D, A Comparative Study of Texture Measures with Classification Based on Feature Distributions [J]. Pattern Recognition 1996, 29(1):51-59.
[21] Ojala T, Pietik?inen M, M?enp?? T.T, Multiresolution Gray-Scale and Rotation Invariant Texture Classification With Local Binary Pattern[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2002, 24(7): 971-987.
[22] Guo Z, Zhang L, Zhang D. Rotation Invariant Texture Classification Using LBP Variance (LBPV) with Global Matching[J]. Pattern Recognition. 2010, 43(3) :706-719.
[23] Guo Z, Zhang L, Zhang D. A Completed Modeling of Local Binary Pattern Operator for Texture Classification[J]. IEEE Transactions on Image Processing, 2011, 19 (6):1657-1663.
[24] Li M, Staunton* R.C. Optimum Gabor Filter Design and Local Binary Patterns for Texture Segmentation[J]. Pattern Recognition Letters, 2008, 29(5) : 664-672.
[25] Niskanen M, Silv′en O, Kauppinen H. Color and Texture Based Wood Inspection with Nonsupervised Clustering[A]. In Proceedings of the 12th Scandinavian Conference on Image Analysis[C]. Bergen, 2001:336-342
[26] Niskanen M, Kauppinen H, Silv′en O. Real-Time Aspects of SOM-Based Visual Surface Inspection[A].Proceedings SPIE Machine Vision Applications in Industrial. Inspection[C]. San Jose :SPIE 2002:123-134.
[27] Tuceryan M. Moment Based Texture Segmentation[J]. Pattern Recognition Letters, 1994, 4(7):659-668.
[28] Khotanzad A, Hong Y.H, Invariant Image Recognition by Zernike Moments[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1990, 12 (5):289-497
[29] Hu M, Visual Pattern Recognition by Moment Invariants[J]. Information Theory, IRE Transactions on. 1962, 8(2):179-187.
[30] Bailey R.R, Srinath M. Orthogonal Moment Features for Use With Parametric and Non-Parametric Classiers[J]. IEEE.Trans. on Pattern Analysis and Machine Intelligence. 1996, 18 (4):389-399.
[31] Teh C.-H, Chin R.T. on Image Analysis by the Methods of Moments[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 10 (1988) 496-513.
[32] Wang L, Healey G. Using Zernike Moments for the Illumination and Geometry Invariant Classification of Multispectral Texture[J]. IEEE Trans. Image Process. 1998, 7 (2) :196-203.
[33] Vilnrotter F, Nevatia R, Price K. Structural Analysis of Natural Textures[J]. IEEE Transaction. on Pattern Analysis and Machine Intelligence, 1986, 8(1):76-89
[34] Hong T, Dyer C, Rosenfeld A. Texture Primitive Extraction Using An Edge-Based Approach[J]. IEEE Transactions on Systems, Man and Cybernetics. 1980,10(10): 659-675
[35] Lu S.Y, Fu K.S. A Syntactic Approach to Texture Analysis[J]. Computer Graphics Image Processing. 1978, 7(3): 303-330
[36] Hsu J, Liu L.C, Li C. Determination of Structure Component in Image Texture Using Wavelet Analysis[A]. International Conference on Image Processing[C]. Thessaloniki :IEEE, 2001,3:166-169
[37] Umarani C, Ganesan L, Radhakrishnan S. Combined Statistical and Structural Approach for Unsupervised Texture Classification[J]. International Journal Of Imaging Science And Engineering 2008,2(1):162-165.
[38] Chen J, Jain A. A Structural Approach to Identify Defects in Textured Images[A]. IEEE International Conference on Systems, Man, and Cybernetics[C]. Beijing:IEEE, 1988, 1: 29–32.
[39] Besag J.E. Spatial Interaction and The Statistical Analysis Of Lattice Systems[J]. Journal of the Royal Statistical Socciety. 1974, 36:192-326.
[40] Derin H, Elliott H. Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1987,9 (1):39-55
[41] Manjunath B.S, Chellappa R. Unsupervised Texture Segmentation Using Markov Random Field Models[J]. IEEE Transaction on Pattern Analysis and Machine Intelligence, 1991, 13(5):478-482
[42] Kato Z, Pong T.C. A Markov Random Field Image Segmentation Model for Color Textured images[J]. Image and Vision Computing 2006, 24:1103-1114.
[43] Noda H, Shirazi M, Kawaguchi E. MRF-Based Texture Segmentation Using Wavelet Decomposed Images[J]. Pattern Recognition 2002, 35(4):771-782.
[44] 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,.
[45] Mandelbrot B, The Fractal Geometry of Nature[M]. New York: W. H. Freeman and Company. 1982.
[46] Field D.J. Relations between the statistics of natural images and the response properties of cortical cells[J]. Journal Optical Society America, 1987, 4(12):2379-2394
[47] Attali S. F, Cohen F. S. Surface Inspection Based on Stochastic Modeling[A]. Proc. SPIE [C] Bellingham:SPIE,1986, 665: 46-52.
[48] Francos J.M, Meiri A.Z, Porat B. A Unified Texture Model Based on A 2-D Wold-Like Decomposition[J]. IEEE Transactions on Signal Processing. 1993, 41(8):2665– 2678.
[49] Fracos J.M, Narasimhan A, Woods J.W. Maximum Likelihood Parameter Estimation of Textures Using A Wold-Decomposition Based Model[J]. IEEE Trans. Image Process. 1995, 4 (12) :1655-1666
[50] Liu F, Picard R.W. Periodicity, Directionality, And Randomness: Wold Features For Perceptional Pattern Recognition[A]. Proceedings of the International Conference on Pattern Recognition[C]. Los Alamitos :IEEE,1994, 11:184-185.
[51] Liu F, Picard R.W. Periodicity, Directionality, And Randomness: Wold Features Of Image And Modeling And Retrieval[J]. IEEE Trans. Pattern Anal. Mach. Intell. 1996, 18 (7):722–733
[52] Laws K.I. Rapid Texture Identification[A]. Proc.of the SPIE Conference on Image Processing for Missile Guidance[C]. Bellingham:SPIE, 1980, 376-380
[53] Rachidi M, Marchadier A, Gadois C. Laws' Masks Descriptors Applied to Bone Texture Analysis: An Innovative And Discriminant Tool in Osteoporosis[J]. Skeletal Radiol. 2008, 37(6):541-8.
[54] Du Buf J.M.H, Spann M, Kardan M. Texture Feature Performance for Image Segmen-tation[J]. Pattern Recognition. 1990, 23(3-4): 291-309
[55] Gabor D. Theory of Communications[J]. Journal of International Electrical Engineers, 1946, 93:427–457
[56] Granlund G. H. In Search of A General Picture Processing Operator[J]. Computer Graphics and Image Processing, 1978, 8(2):155–173
[57] Daugman J.G. Uncertainty Relation for Resolution in Pace, Spatial-Frequency, and Orientation Optimized by Two Dimensional Visual Cortical Filters[J]. Journal of the Optical Society of America A: Optics, Image Science, and Vision. 1985, 2(7):1160 -1169.
[58] Devalois R.L. Spatial-Frequency Selectivity Of Cells In Macaque Visual Cortex[J]. Vision Research. 1982, 22(5):545-559
[59] Jain A.K, Farrokhnia F. Unsupervised Texture Segmentation Using Gabor Filters[J]. Pattern Recognition. 1991, 24(12):1167-1186
[60] Weldon T.P, Higgins W.E, Dunn D.F. Efficient Gabor Filter Design for Texture Segmentation[J]. Pattern Recognition. 1996, 29(12): 2005-2015
[61]马江林,赵忠明,汪承义等.一种新的基于Gabor小波的非监督纹理分割方法[J].武汉大学学报·信息科学版2009, 34(1) :11-18
[62]王敏琴,韩国强,涂泳秋.新型的无监督纹理分割方法[J].电子科技大学学报2010, 39(1):11-15
[63] Han J, Ma K-K. Rotation-Invariant and Scale-Invariant Gabor Features for Texture Image Retrieval[J]. Image and Vision Computing 2007, 25(9):1474–1481
[64] Bianconi F, Fernandez A. Evaluation Of The Effects of Gabor Filter Parameters on Texture Classification[J]. Pattern Recognition. 2007, 40(12):3325-3335
[65] Mak K.L, Peng P. An Automated Inspection System for Textile Fabrics Based On Gabor Filters[J]. Robotics and Computer-Integrated Manufacturing. 2008, 24(3):359–369
[66] Kumar A, Pang G. Defect Detection in Textured Materials Using Gabor Filters[J]. IEEE Transactions on Industry Applications. 2002, 38(2):425–440
[67] Tsai D.M, Lin C.P. Fast Defect Detection in Textured Surfaces Using 1D Gabor Filters[J]. The International Journal of Advanced Manufacturing Technology. 2002, 20(9):664-675
[68] Bodnarova A, Bennamoun M, Latham S. Optimal Gabor Filters For Textile Flaw Detection [J]. Pattern Recognition. 2002,35(12):2973–2991,.
[69] Yun J P, Choi S, Kim J-W, et al. Automatic Detection Of Cracks in Raw Steel BlockUsing Gabor Filter Optimized by Univariate Dynamic Encoding Algorithm for Searches (uDEAS) [J]. NDT&E International. 2009, 42(5):389–397
[70] Kumar A, Pang G. Fabric Defect Segmentation Using Multichannel Blob Detectors[J]. Optical Engineering, 2000, 39(12):3176–3190
[71] Wiltschi K, Pinz A., Lindeberg T. An Automatic Assessment Scheme for Steel Quality Inspection[J]. Machine Vision and Applications. 2000, 12(3):113–128
[72] Bennamoun M, Bodnarova A. Digital Image Processing Techniques For Automatic Textile Quality Control[J]. Systems Analysis Modelling Simulation, 2003,43(11): 1581-1614
[73] Tsai D, Lin C, Huang K. Defect Detection in Coloured Texture Surfaces Using Gabor Filters [J]. Imaging Science Journal, 2005,53(1):27–37,. [ 74 ] Mallat S.G. A Theory for Multiresolution Signal Decomposition: the Wavelet Representation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1989, 11(7):674-693
[75] Wang J.W, Chen C.H, Chien W.M. Etc. Texture Classification Using Non-Separable Two-Dimensional Wavelets[J]. Pattern Recognition Letters. 1998, 19(13):1225-1234.
[76] Turkoglu I, Avci E. Comparison Of Wavelet-SVM and Wavelet-Adaptive Network Based Fuzzy Inference System for Texture Classification[J]. Digital Signal Processing 2008,18(1) :15–24
[77] Hiremath P.S, Shivashankar S. Wavelet Based Co-Occurrence Histogram Features for Texture Classification with An Application to Script Identification in A Document Image[J]. Pattern Recognition Letters, 2008, 29(9):1182–1189
[78] Bashar M.K, Matsumoto T, Ohnishi N. Wavelet Transform-Based Locally Orderless Images For Texture Segmentation[J]. Pattern Recognition Letters, 2003, 24:2633–2650.
[79] Muneeswaran K, Ganesan L, Arumugam S. et al. Texture Classification with Combined Rotation and Scale Invariant Wavelet Features[J]. Pattern Recognition, 2005, 38(10):1495-1506.
[80] Yu G, Kamarthi S.V. A Cluster-Based Wavelet Feature Extraction Method and Its Application[J]. Engineering Applications of Artificial Intelligence. 2010, 23(2):196–202.
[81] Avci E, Sengur A, Hanbay D. An Optimum Feature Extraction Method for Texture Classification[J]. Expert Systems with Applications, 2009, 36(3):6036-6043
[82] Unser M. Texture Classification and Segmentation Using Wavelet Frames[J]. IEEE Trans. Image Process, 1995, 4(11):1549–1560.
[83] Avci E. Comparison Of Wavelet Families for Texture Classification by Using Wavelet Packet Entropy Adaptive Network Based Fuzzy Inference System[J]. Applied Soft Computing 2008, 8(1):225–231
[84] Sengur A, Turkoglu I, Ince M.C. Wavelet Packet Neural Networks for Texture Classification[J]. Expert Systems with Applications, 2007, 32(2):527–533.
[85] Jiang X W, Zhao R.C. Texture Segmentation Based on Incomplete Wavelet Packet Frame [A]. Proceedings of the Second International Conference on Machine Learning and Cybernetics, Wan[C]. Xi’an:IEEE, 2003:2-5.
[86] Acharyya M, De R.K., Kundu M.K. Extraction of Features Using M-Band Wavelet Packet Frame and Their Neuro-Fuzzy Evaluation for Multitexture Segmentation[J]. IEEE Transactions on Pattern Analysis And Machine Intelligence, 2003, 25(12): 1639-1644
[87] Huang K, Aviyente S. Information-Theoretic Wavelet Packet Subband Selection for Texture Classification[J]. Signal Processing, 2006, 86(7):1410–1420
[88] Kim S.C, Kang T.J. Texture Classification and Segmentation Using Wavelet Packet Frame and Gaussian Mixture Model[J]. Pattern Recognition, 2007, 40(4):1207-1221.
[89] Wouwer G. V, Scheunders P, Dyck D. V. Statistical Texture Characterization from Discrete Wavelet Representations[J]. IEEE Trans. Image Processing, 1999, 8(4): 592-598.
[90] Do M.N, Vetterli M. Wavelet-based Texture Retrieval Using Generalized Gaussian Density and Kullback-Leibler Distance[J]. IEEE Trans. Image Process. 2002, 11(2):146-158.
[91] Choy S. K, Tong C. S. Supervised Texture Classification Using Characteristic Generalized Gaussian Density[J]. J. Math. Imag. Vis. 2007, 29(1):35–47.
[92] Pi M, Tong C.S, Choy S. K, et al.A Fast and Effective Model for Wavelet Subband Histograms and Its Application in Texture Image Retrieval[J]. IEEE Trans. Image Process. 2006, 15(10): 3078-3088.
[93] Choy S. K. Tong C. S. Statistical Properties Of Bit-Plane Probability Model and Its Application in Supervised Texture Classification[J]. IEEE Trans. Image Process. 2008,17(8): 1399-1405.
[94] Romberg J. K, Choi H, Baraniuk R. G. Bayesian Tree-Structured Image Modeling Using Wavelet-Domain Hidden Markov Models[J]. IEEE Trans. Image Process,2001,10(7):1056-1068
[95] Do M.N, Vetterli M. Rotation Invariant Texture Characterization And Retrieval Using Steerable Wavelet-Domain Hidden Markov Models[J]. IEEE Trans. Multimedia, 2002, 4,(12): 517-527
[96] Choi H, Baraniuk R.G. Multiscale Texture Segmentation Using Wavelet-Domain Hidden Markov Models[J]. IEEE Trans. Image Process., 2001,10(9):1309-1321
[97] Choy S.K, Tong C.S. Statistical Wavelet Subband Characterization Based on Generalized Gamma Density and Its Application in Texture Retrieval[J]. IEEE Transactions on Image Processing, 2010, 19(2): 281-289.
[98] Benkuider A, Aarab A. Content Based Image Retrieval Using the Generalized Gamma Density to Model BEMD’s IMFs[J]. Journal Of Computers, 2011, 6(6):1168-1174.
[99] Celik T, Tjahjadi T. Multiscale Texture Classification Using Dual-Tree Complex Wavelet Transform[J]. Pattern Recognition Letters, 2009, 30(3):331-339.
[100]尚赵伟,唐远炎,房斌等.基于相对熵和复小波变换的纹理图像检索[J].重庆大学学报, 2008, 31(5):244-247
[101] Qiao Y.L, Zhao C.H, Song C.Y. Complex Wavelet Based Texture Classification[J]. Neuro Computing, 2009, 72(16-18):3957-3963
[102] Vo A, Oraintara S. A Study of Relative Phase In Complex Wavelet Domain: Property, Statistics And Applications In Texture Image Retrieval And Segmentation[J]. Signal Processing: Image Communication, 2010, 25(1): 28-46.
[103] Sari-Sarraf H, Goddard J. Vision Systems for On-Loom Fabric Inspection[J]. IEEE Transactions on Industry Applications, 1999, 35(6):1252-1259.
[104] Kim S, Lee M, Woo K. Wavelet Analysis to Fabric Defects Detection in Weaving Processes[A]. In Proceedings of IEEE International Symposium on Industrial Electronics[C]. New Jercy:IEEE 1999, 3:1406–1409.
[105] Yang X, Pang G, Yung N. Robust Fabric Defect Detection And Classification Using Multiple Adaptive Wavelets[A]. IEE Proceedings Vision, Image Processing[C]. IET, 2005, 152(6):715–723.
[106] Han Y.F, Shi P.F. An Adaptive Level-Selecting Wavelet Transform for Texture Defect Detection[J]. Image and Vision Computing, 2007, 25(8):1239–1248.
[107] Yang X, Pang G, Yung N. Fabric Defect Classification Using Wavelet Frames And Minimum Classification Error Training[A]. In 37th IAS Annual Meeting. ConferenceRecord of the Industry Applications Conference[C]. Pittsburgh:IEEE. 2002,1:290-292,.
[108] Arivazhagan S, Ganesan L, Subash Kumar T. G. Texture classification using Curvelet Statistical and Co-occurrence Features[A]. 18th International Conference on Pattern Recognition (ICPR'06) [C]. New Jersey:IEEE, 2006,2:938-941
[109] Zhao Y. F, Xia L.Z. Contourlet-Based Feature Extraction on Texture Images[J]. Interna -tional Conference on Computer Science and Software Engineering. 2008, 6:221-224
[110]曲怀敬, Contourlet变换在纹理图像检索和医学图像分割中的应用研究[D].山东大学博士学位论文, 2009
[111] He Z.Y, You X.G, YuanY. Texture Image Retrieval Based on Non-Tensor Product Wavelet Filter Banks[J]. Signal Processing, 2009, 89(8):1501–1510
[112] Charalampidis D, Kasparis T. Wavelt-Based Rotational Invalriant Roughness Features for Texture Classification and Segmentation[J]. IEEE Trans. Image Processing, 2002, 11(8): 825-837
[113] Laine A, Fan J. Texture Classification by Wavelet Packet Signatures[J]. IEEE Transaction on Pattern Analysis and Machine Intelligence, 1993, 15(11):1186-1191
[114] Kingsbury N.G. The Dual-Tree Complex Wavelet Transform: A New Efficient Tool For Image Restoration And Enhancement[J]. in Proc. European Signal Processing Conf., Rhodes, 1998:319–322.
[115] 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
[116] Kingsbury N.G. A Dual-Tree Complex Wavelet Transform with Improved Orthogonality And Symmetry Properties[A]. In Proc. IEEE Int. Conf. Image Processing, Vancouver, BC, Canada[C]. Vancouver:IEEE, 2000, 2: 375–378.
[117] Bezdec J.C. Pattern Recognition with Fuzzy Objective Function Algorithms[M]. New York:Plenum Press, 1981:10-30;
[118] Ahmed, M.N, Yamany S.M, Mohamed N, et al. A Modified Fuzzy C-Means Algorithm for Bias Field Estimation And Segmentation of MRI Data[J]. IEEE Trans. Med. Imaging, 2002, 21(3):193–199
[119] Chen S.C, Zhang D.Q. Robust Image Segmentation Using FCM with Spatial Constraints Based on New Kernel-Induced Distance Measure[J]. IEEE Transactions on Systems Man and Cybernetics, 2004, 34(4):1907-1916.
[120] Dalai N, Triggs B. Histograms of Oriented Gradients for Human Detection[A]. IEEEComputer Society Conference on Computer Vision and Pattern Recognition[C]. San Diego:IEEE 2005, 1:886-893.
[121] Bertozzi M, Broggi A, Del Rose M. A Pedestrian Detector Using Histograms of Oriented Gradients and a Support Vector Machine Classifier[A]. 10th International IEEE Conference on Intelligent Transportation Systems, ITSC2007[C]. New Jersey: IEEE, 2007, 3:143-148
[122] Zhu Q, Avidan S, Yeh M.C. Fast Human Detection Using a Cascade of Histograms of Oriented Gradients[A]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition[C] 2006, 2:1491-1498.
[123] Chuang C.H, Huang S.S, Fu L.C. Monocular Multi-Human Detection Using Aug- mented Histograms of Oriented Gradients[A]. 2008 19th International Conference on Pattern Recognition [C]. New Jersey:IEEE, 2008:1-4
[124] Acharyya M, Kundu M.K. Adaptive Basis Selection for Multitexture Segmentation by M-Band Wavelet Packet Frame[A]. Proc. 2001 Int’l Conf.Image Processing[C]. Thessaloniki:IEEE, 2001, 2:622-625
[125] Thomaz C.E, Boardman J P , Counsell S, et al. A Multivariate Statistical Analysis of The Developing Human Brain in Preterm Infants[J]. Image and Vision Computing, 2007, 25 (6) :981-994
[126] Lin H.D, Chung C.Y, Lin W.T. Principal Component Analysis Based on Wavelet Characteristics Applied to Automated Surface Defect Inspection[J]. WS EA S Transac -tions on Computer Research, 2008, 3(4):193-202.
[127] Sharifi K, Leon-Garcia A. Estimation of Shape Parameter for Generalized Gaussian Distributions in Subband Decompositions of Video[J]. IEEE Trans. Circuits Syst. Video Technol, 1995, 5(1):52–56
[128] Moulin P, Liu J. Analysis of Multiresolution Image Denoising Schemes Using Generalized Gaussian And Complexity Priors[J]. IEEE Trans. Inform. Theory, 1999, 45(3): 909-919.
[129] Varanasi M.K, Aazhang B. Parametric Generalized Gaussian Density Estimation[J] J. Acoust. Soc. Amer. 1989, 86(4):1404–1415.
[130] Stacy E.W. A Generalization of The Gamma Distribution[J]. Ann. Math. Statist. 1962, 33(13): 1187-1192
[131] Shin J.W, Chang J.H, Kim N.S. Statistical Modeling of Speech Signals Based on Generalized Gamma Distribution[J]. IEEE SignalProcess. Lett., 2005,12(3):258-261
[132]高丛珊,张红,王超等.广义Gamma模型及自适应KI阈值分割的SAR图像变化检测[J].遥感学报, 2010, 14(4):718-732
[133] Comaniciu D, Ramesh V, Meer P. Real-Time Tracking of Non-Rigid Objects using Mean Shift[A]. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition[C]. California: IEEE, 2000, 2:142-149.
[134] D. Comaniciu, P. Meer: Mean Shift: A Robust Approach toward Feature Space Analysis[J]. IEEE Trans. Pattern Analysis Machine Intell., 2002, 24(5), 603-619
[135] Zhand J.G, Tan T.M. Invariant Texture Segmentation Via Cirular Gabor Filter[A]. 16th International Conference on Pattern Recognition, Austalian, Computer Society[C]. 2002: 901-904
[136]袁端磊,宋寅卯.基于最优Gabor滤波器的织物缺陷检测[J].中国图象图形学报, 2006,11(7):954-958.
[137]徐胜海.印刷电路板在线自动视觉检测系统的分析研究[D].合肥工业大学硕士学位论文, 2001
[138]刘良江.先进电子制造生产线机器视觉检测方法与技术研究[D].湖南大学博士学位论文, 2009.10
[139] Tsai D.M, Lin B.T. Defect Detection Of Gold-Plated Surfaces On Pcbs Using Entropy Measures[J]. The International Journal of Advanced Manufacturing Technology, 2002, 20(6):420-428