羽毛杆微小折痕检测技术研究
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摘要
羽毛片作为羽毛球生产的主要原料,其质量直接影响到成品羽毛球的品质。羽毛杆有折痕会使得角质层受损从而降低耐打度。我国羽毛球产业仍然依赖密集人工完成折痕检测,检测速度和检测质量受个人主观因素影响很大。当然高强度的检测也对检测者视力造成伤害。因此利用机器视觉进行目标采集,通过特征提取完成折痕缺陷自动检测有着必要性,具有重要的工程实际意义和经济效益。毛杆的细长结构和拱度弯度加剧了光照不均,再加上折痕和背景界限模糊,这些因素都对折痕特征识别造成干扰。本文在国内外相关研究进展的基础上,对实验装置、图像分割提取、去噪增强、灰度校正以及折痕特征的度量、分类等多个方面进行了深入的研究,为羽毛球产业从人工密集型向高新科技型转变进行了有益的探索。
本文完成的主要研究工作和成果如下：
(1)毛杆边缘处理。由于羽毛杆具有较大的长宽比和不同的拱度弯度,这使得毛杆边缘噪声造成较大干扰,会增加对毛杆折痕的误判与错判的潜在可能。因此需要消除羽毛杆边缘残余绒毛以减少干扰。利用Ncut分割获得羽毛杆的初始轮廓,提出窄带单向膨胀方法完成初始轮廓的局部修正。为了改善过度分割,利用羽毛杆左右端顶点作为约束种子；然后将轮廓线两侧膨胀改为单向外侧膨胀,改善相邻轮廓线邻域重叠问题,完成初始轮廓的局部修正。实验结果表明该方法具有无需人工干预能够准确有效的实现羽毛杆分割。
(2)经过对毛杆折痕统计分析表明大部分毛杆是无折痕的,有折毛杆往往也只有一到两个折痕。如果对二维图像遍历搜索缺陷会增加大量好的羽毛杆被判定为缺陷毛杆的潜在可能。这就要求检测方法在减少非折痕误测率基础上提高折痕检测。针对这一特点,本文首先将二维毛杆图像简化为一维灰度均值信号,利用小波变换的局部模极大值和信号奇异点之间的关系完成奇异点位置检测,确定缺陷存在位置,并完成缺陷子图像分割提取。
(3)光线校正。侧光照明加剧了光照不均现象,再加上毛杆不同的弯度和拱度,这就难以使用校准模板进行光线校正。毛杆特有结构使得光线的衰减可以看作是个缓慢的过程。在此引入基线漂移概念,通过小波变换对灰度均值变化曲线进行多尺度分解,利用分解后所得的逼近信号充分逼近光线的变化,从而完成光线校正。
(4)图像降噪。子图像分割后需要进行判断是否存在折痕缺陷。由于微小折痕容易受到噪声干扰,对折痕识别造成不利影响,为了改善图像质量,降低图像中存在的大量随机分布噪声的影响,以流形上的热导方程理论为工具对含噪数据流形上的概率测度进行了分析,通过引入热核并对流形上热核唯一性和正则性进行讨论,得到了羽毛杆含噪数据的密度函数与干净数据上的概率密度之间的对应关系,并设计了基于图的热导方程去噪算法。
(5)通过对各种表面折痕缺陷的灰度特征、形状特征、分布特征的研究,提出一种改进的Radon变换用于毛杆折痕表面缺陷的识别算法。引入局部投影技术消除羽毛杆生理纹理干扰,通过改变尺度因子获得矩不变量矩阵,并采用奇异值分解获得特征不变量用于分类识别,从而在灰度域和梯度域得到两个单模态识别结果；最后采用决策级融合方法得到双模态融合识别结果,提高了非折痕识别率。
(6)基于黎曼流形的毛杆折痕识别。利用协方差描述子对图像特征进行描述,重点研究了Log-Euclidean黎曼度量下协方差矩阵的李群结构,推导了黎曼流形中内积空间的度量形式,给出流形核函数表达；在仿射不变度量下讨论了流形上类间散度和类内散度计算,利用黎曼指数映射得到了样本的最佳映射空间,从而实现非线性空间的标签判别。在改进的局部保持投影算法中,运用流形核函数与局部保持投影相结合的方法进行毛杆特征提取：首先基于区域图像构造协方差矩阵作为图像特征,利用仿射不变度量作为样本点的距离测度,研究了流形上近邻点的选择以及包含数据类别信息的核矩阵构建；最后利用改进的局部保持投影算法对毛杆图像进行降维。
(7)提出黎曼流形上的核稀疏表示方法进行毛杆折痕识别。首先利用Bregman散度作为黎曼流形上的距离度量,并以此构造了流形上的核函数,把协方差矩阵映射到再生核希尔伯特空间。通过稀疏表示理论建立了字典学习的数学模型,结合凸理论给出了黎曼流形上的核稀疏最大化的字典学习算法。最后利用SRC分类器完成测试样本鉴别。实验结果证实了所提方法的有效性。
论文最后阐述了研究的创新点及主要研究成果、展望。
Feather as the main raw material of badminton production, its quality directly affects the quality of the finished product badminton.Creases of feather quill occur when the cuticle is damaged and the fibres of the cortex unravel.However, the quality testing of badminton industry relies a lot on manual work in our country, which results in that the detecting speed and quality are influenced by personal. Actually high strength testing is harmful to eyesight of the workers. Consequently, it's of great engineering significances and potential economic effect to study crease defect automatic detection by feature extraction using machine vision for testing image acquisition.Due to slender structure of feather quill with variable width, camber and curvature, in addition, boundary between crease of feather quill and background is fuzzy, all of these factors cause disturbance to the crease feature recognition.Based on detailed research development from home and abroad, this thesis explores experimental device,image segmentation,denoising,graylevel correction and crease features classification, etc.,deeply.There are some helpful researches about badminton industry from labour-intensive industry to technology-intensive industry.
The related work and harvest in this paper as follows:
(1) Image edge processing. Due to slender structure of feather quill with variable width, camber and curvature, this makes marginal noise caused disturbance to the crease feature recognition to increase the potential for misunderstanding and miscalculation. So need to eliminate the residual edge villi of feather quill, edge to reduce noise.The method partitions a feather image into clusters with normalized cut method to obtain an initial contour of the feather quill. The proposed narrow unidirectional expansion method with a region-scalable fitting term is used to adjust the initial boundary for the final result. This paper presents a method of image segmentation by combining certain hard constraints for segment by indicating certain seeds with unidirectional expansion. The method utilizes left and right vertex of feather quill as seeds which are passed by active contour to better over segmentation; changes bidirection dilation to inside direction dilation to improve the overlap of adjacent contour neighborhoods and reduce the computation scale. Experiment results show that the proposed method without human intervention can effectively realize the image segmentation effectively.
(2) Statistical analysis of feather quill crease shows that most of feather quill is no crease or some feather quill only have one or two crease. Exhaustive searches of two-dimensional image can increase the potential for misunderstanding and miscalculation. As most of feather quill without creases in actual production,detection method requires not only effective feature extraction, but also a high degree of non-crease detection accuracy. Aiming at this characteristic, a detection method of feather quill crease is proposed. The feather quill image is transformed into one dimensional signal, using the relationship between wavelet transform modulus maxima and singular point,the crease coordinate can be prejudged. Subimage can be extracted through the coordinate to reduce misjudgement caused by image traversal.
(3)Light correction.Side lighting exacerbated the uneven illumination phenomenon, combined with different camber and camber feather quill, so it is difficult to use correction calibration template to correction light. Due to the unique structure of feather quill, the attenuation of light can be seen as a slow process. The concept of "baseline drift" is introduced,and multi-scale decompositions are carried out for mean gray level curve. Using wavelet approximation coefficients to close characteristic of baseline drift completes light correction.
(4) Image denoising.Tiny creases of feather quill is easily disturbed by noise that causing adverse effects on recognition performance. For the shortcomings of the traditional noise reduction algorithm in the process of image, i.e., it is sensitive to the noise resulting in the problem of weakened creases characteristics, this paper discusses probability measure of noisy image on manifolds based on heat equation theory, analyzes regularity and uniqueness of heat kernels on manifolds,and gets the corresponding relationship between density function of noisy data and probability density of clean data. A denoising algorithm of feather quill based on the heat equation of graph is proposed. In the method, de-noised image can be obtained through iterative solution of equation based on feather quill image representing as an undirected weighted graph. The Experimental results show that this suggested method can get better effect comparing with other transform domain algorithms.
(5) Appropriate shape description method. According to the surface defect characteristics such as shape, size and gray distribution,a novel method based on improved Radon transform is proposed. In order to solve the scaling and translation sensitivity of Radon transformation, an improved Radon transformation is used to extract moment invariants of target region and introduces local projection technology to eliminate interference physiological texture of feather quill. Obtaining invariants matrix by changing the scale factor, singular value decomposition is provided here to obtain feature invariant for classification and recognition. Finally,the final recognition result of the system is achieved by the fusion of identification results of the gray domain and gradient domain at the decision level to overcome the limitations of single-modal and reduce the misjudgment of non-crease effectively.
(6) Feather quill crease detection method based on Riemannian manifold.Research is mainly focused on the covariance matrix lie group structure with Log-Euclidean metric with double invariance properties and manifold kernel function expression with covariance matrices as the crease descriptors of feather quill;then this article designs algorithm with metrics of inner product space and manifold kernel function expression which are deduced. An affine invariance metric which is adopted to make the space meet the requirement of Riemannian manifold is used to adjust class variance and within class variance. Finally, in order to implement the discrimination in nonlinear space, the best projection space of samples is gotten using the Riemannian index mapping. A feather quill crease recognition method based on locality preserving projection and manifold kernel function is proposed for feature extraction. Firstly, covariance matrices are computed as the crease descriptors of feather quill, and an affine invariance metric which is adopted to make the space meet the requirement of Riemannian manifold is used to measure the distance between the two samples. Secondly, the neighbors of a selected point can be determined by the proposed manifold kernel function to make choice of the nearest neighboring points in line with the hypothesis of data distribution with non-linear manifold. The kernel matrix is defined based on the manifold distance and category labels. Finally, the locality preserving projections algorithm is used to reduce the dimensionality of the feather quill images.
(7) A feather quill crease recognition method based on sparse representation on Riemannian manifold is proposed for feature recognition. Firstly, Bregman divergence which is adopted to make the space meet the requirement of Riemannian manifold is used to measure the distance between the two samples, and the kernel function on Riemannian manifold is constructed. Secondly, the sample datasets are mapped into the reproducing kernel Hilbert space by kernel method, and kernel sparse representation coefficient is obtained. Finally, a mathematical model of dictionary learning is constructed and an efficient algorithm is proposed for dictionary learning according to the convex theory. The simulated experimental results verify the effectiveness of the proposed method which achieves better performance compared with many popular recognition algorithms.
At the end of this dissertation, the main research is summarized. It makes out the main innovations and research achievements, and also points out the problems and issues which need to further research.
本文完成的主要研究工作和成果如下：
(1)毛杆边缘处理。由于羽毛杆具有较大的长宽比和不同的拱度弯度,这使得毛杆边缘噪声造成较大干扰,会增加对毛杆折痕的误判与错判的潜在可能。因此需要消除羽毛杆边缘残余绒毛以减少干扰。利用Ncut分割获得羽毛杆的初始轮廓,提出窄带单向膨胀方法完成初始轮廓的局部修正。为了改善过度分割,利用羽毛杆左右端顶点作为约束种子；然后将轮廓线两侧膨胀改为单向外侧膨胀,改善相邻轮廓线邻域重叠问题,完成初始轮廓的局部修正。实验结果表明该方法具有无需人工干预能够准确有效的实现羽毛杆分割。
(2)经过对毛杆折痕统计分析表明大部分毛杆是无折痕的,有折毛杆往往也只有一到两个折痕。如果对二维图像遍历搜索缺陷会增加大量好的羽毛杆被判定为缺陷毛杆的潜在可能。这就要求检测方法在减少非折痕误测率基础上提高折痕检测。针对这一特点,本文首先将二维毛杆图像简化为一维灰度均值信号,利用小波变换的局部模极大值和信号奇异点之间的关系完成奇异点位置检测,确定缺陷存在位置,并完成缺陷子图像分割提取。
(3)光线校正。侧光照明加剧了光照不均现象,再加上毛杆不同的弯度和拱度,这就难以使用校准模板进行光线校正。毛杆特有结构使得光线的衰减可以看作是个缓慢的过程。在此引入基线漂移概念,通过小波变换对灰度均值变化曲线进行多尺度分解,利用分解后所得的逼近信号充分逼近光线的变化,从而完成光线校正。
(4)图像降噪。子图像分割后需要进行判断是否存在折痕缺陷。由于微小折痕容易受到噪声干扰,对折痕识别造成不利影响,为了改善图像质量,降低图像中存在的大量随机分布噪声的影响,以流形上的热导方程理论为工具对含噪数据流形上的概率测度进行了分析,通过引入热核并对流形上热核唯一性和正则性进行讨论,得到了羽毛杆含噪数据的密度函数与干净数据上的概率密度之间的对应关系,并设计了基于图的热导方程去噪算法。
(5)通过对各种表面折痕缺陷的灰度特征、形状特征、分布特征的研究,提出一种改进的Radon变换用于毛杆折痕表面缺陷的识别算法。引入局部投影技术消除羽毛杆生理纹理干扰,通过改变尺度因子获得矩不变量矩阵,并采用奇异值分解获得特征不变量用于分类识别,从而在灰度域和梯度域得到两个单模态识别结果；最后采用决策级融合方法得到双模态融合识别结果,提高了非折痕识别率。
(6)基于黎曼流形的毛杆折痕识别。利用协方差描述子对图像特征进行描述,重点研究了Log-Euclidean黎曼度量下协方差矩阵的李群结构,推导了黎曼流形中内积空间的度量形式,给出流形核函数表达；在仿射不变度量下讨论了流形上类间散度和类内散度计算,利用黎曼指数映射得到了样本的最佳映射空间,从而实现非线性空间的标签判别。在改进的局部保持投影算法中,运用流形核函数与局部保持投影相结合的方法进行毛杆特征提取：首先基于区域图像构造协方差矩阵作为图像特征,利用仿射不变度量作为样本点的距离测度,研究了流形上近邻点的选择以及包含数据类别信息的核矩阵构建；最后利用改进的局部保持投影算法对毛杆图像进行降维。
(7)提出黎曼流形上的核稀疏表示方法进行毛杆折痕识别。首先利用Bregman散度作为黎曼流形上的距离度量,并以此构造了流形上的核函数,把协方差矩阵映射到再生核希尔伯特空间。通过稀疏表示理论建立了字典学习的数学模型,结合凸理论给出了黎曼流形上的核稀疏最大化的字典学习算法。最后利用SRC分类器完成测试样本鉴别。实验结果证实了所提方法的有效性。
论文最后阐述了研究的创新点及主要研究成果、展望。
Feather as the main raw material of badminton production, its quality directly affects the quality of the finished product badminton.Creases of feather quill occur when the cuticle is damaged and the fibres of the cortex unravel.However, the quality testing of badminton industry relies a lot on manual work in our country, which results in that the detecting speed and quality are influenced by personal. Actually high strength testing is harmful to eyesight of the workers. Consequently, it's of great engineering significances and potential economic effect to study crease defect automatic detection by feature extraction using machine vision for testing image acquisition.Due to slender structure of feather quill with variable width, camber and curvature, in addition, boundary between crease of feather quill and background is fuzzy, all of these factors cause disturbance to the crease feature recognition.Based on detailed research development from home and abroad, this thesis explores experimental device,image segmentation,denoising,graylevel correction and crease features classification, etc.,deeply.There are some helpful researches about badminton industry from labour-intensive industry to technology-intensive industry.
The related work and harvest in this paper as follows:
(1) Image edge processing. Due to slender structure of feather quill with variable width, camber and curvature, this makes marginal noise caused disturbance to the crease feature recognition to increase the potential for misunderstanding and miscalculation. So need to eliminate the residual edge villi of feather quill, edge to reduce noise.The method partitions a feather image into clusters with normalized cut method to obtain an initial contour of the feather quill. The proposed narrow unidirectional expansion method with a region-scalable fitting term is used to adjust the initial boundary for the final result. This paper presents a method of image segmentation by combining certain hard constraints for segment by indicating certain seeds with unidirectional expansion. The method utilizes left and right vertex of feather quill as seeds which are passed by active contour to better over segmentation; changes bidirection dilation to inside direction dilation to improve the overlap of adjacent contour neighborhoods and reduce the computation scale. Experiment results show that the proposed method without human intervention can effectively realize the image segmentation effectively.
(2) Statistical analysis of feather quill crease shows that most of feather quill is no crease or some feather quill only have one or two crease. Exhaustive searches of two-dimensional image can increase the potential for misunderstanding and miscalculation. As most of feather quill without creases in actual production,detection method requires not only effective feature extraction, but also a high degree of non-crease detection accuracy. Aiming at this characteristic, a detection method of feather quill crease is proposed. The feather quill image is transformed into one dimensional signal, using the relationship between wavelet transform modulus maxima and singular point,the crease coordinate can be prejudged. Subimage can be extracted through the coordinate to reduce misjudgement caused by image traversal.
(3)Light correction.Side lighting exacerbated the uneven illumination phenomenon, combined with different camber and camber feather quill, so it is difficult to use correction calibration template to correction light. Due to the unique structure of feather quill, the attenuation of light can be seen as a slow process. The concept of "baseline drift" is introduced,and multi-scale decompositions are carried out for mean gray level curve. Using wavelet approximation coefficients to close characteristic of baseline drift completes light correction.
(4) Image denoising.Tiny creases of feather quill is easily disturbed by noise that causing adverse effects on recognition performance. For the shortcomings of the traditional noise reduction algorithm in the process of image, i.e., it is sensitive to the noise resulting in the problem of weakened creases characteristics, this paper discusses probability measure of noisy image on manifolds based on heat equation theory, analyzes regularity and uniqueness of heat kernels on manifolds,and gets the corresponding relationship between density function of noisy data and probability density of clean data. A denoising algorithm of feather quill based on the heat equation of graph is proposed. In the method, de-noised image can be obtained through iterative solution of equation based on feather quill image representing as an undirected weighted graph. The Experimental results show that this suggested method can get better effect comparing with other transform domain algorithms.
(5) Appropriate shape description method. According to the surface defect characteristics such as shape, size and gray distribution,a novel method based on improved Radon transform is proposed. In order to solve the scaling and translation sensitivity of Radon transformation, an improved Radon transformation is used to extract moment invariants of target region and introduces local projection technology to eliminate interference physiological texture of feather quill. Obtaining invariants matrix by changing the scale factor, singular value decomposition is provided here to obtain feature invariant for classification and recognition. Finally,the final recognition result of the system is achieved by the fusion of identification results of the gray domain and gradient domain at the decision level to overcome the limitations of single-modal and reduce the misjudgment of non-crease effectively.
(6) Feather quill crease detection method based on Riemannian manifold.Research is mainly focused on the covariance matrix lie group structure with Log-Euclidean metric with double invariance properties and manifold kernel function expression with covariance matrices as the crease descriptors of feather quill;then this article designs algorithm with metrics of inner product space and manifold kernel function expression which are deduced. An affine invariance metric which is adopted to make the space meet the requirement of Riemannian manifold is used to adjust class variance and within class variance. Finally, in order to implement the discrimination in nonlinear space, the best projection space of samples is gotten using the Riemannian index mapping. A feather quill crease recognition method based on locality preserving projection and manifold kernel function is proposed for feature extraction. Firstly, covariance matrices are computed as the crease descriptors of feather quill, and an affine invariance metric which is adopted to make the space meet the requirement of Riemannian manifold is used to measure the distance between the two samples. Secondly, the neighbors of a selected point can be determined by the proposed manifold kernel function to make choice of the nearest neighboring points in line with the hypothesis of data distribution with non-linear manifold. The kernel matrix is defined based on the manifold distance and category labels. Finally, the locality preserving projections algorithm is used to reduce the dimensionality of the feather quill images.
(7) A feather quill crease recognition method based on sparse representation on Riemannian manifold is proposed for feature recognition. Firstly, Bregman divergence which is adopted to make the space meet the requirement of Riemannian manifold is used to measure the distance between the two samples, and the kernel function on Riemannian manifold is constructed. Secondly, the sample datasets are mapped into the reproducing kernel Hilbert space by kernel method, and kernel sparse representation coefficient is obtained. Finally, a mathematical model of dictionary learning is constructed and an efficient algorithm is proposed for dictionary learning according to the convex theory. The simulated experimental results verify the effectiveness of the proposed method which achieves better performance compared with many popular recognition algorithms.
At the end of this dissertation, the main research is summarized. It makes out the main innovations and research achievements, and also points out the problems and issues which need to further research.
引文
[1]邾继贵,王仲,周富强,等IVECO车身视觉检测系统现场溯源研究[J].计量学报,2000,21(1)：28-33.
[2]敖波,邬冠华,邓翠贞,等.航空精密铸件射线CR成像检测研究[J].铸造,2011,60(9)：884-887.
[3]郑雪莲,杨北革,逯海军,等.基于改进多尺度CB形态学的输电线路覆冰厚度检测[J].电测与仪表,2013(4)：45-49.
[4]Schmidberger E J, Ahlers R J. Quality control with a robot-guided electro-optical sensor[C]//Proc. of Ⅳ Conf. on Robot Vision and Sensory Control. London.2006:27-36.
[5]Lu R S, Li Y F, Yu Q. On-line measurement of the straightness of seamless steel pipes using machine vision technique[J].Sensors and Actuators A:Physical,2001,94(1):95-101.
[6]周烜.微机在工件不圆度自动测量中的应用[J].基础自动化,2010,8(6)：50-52.
[7]廖强,米林,周忆,等.微尺寸视觉精密检测系统设计[J].重庆大学学报(自然科学版),2007,12(6)：21-23.
[8]熊海林,陈香萍.圆度误差的一种微机图像测量法[J].航空计测技术,1998,18(4)：25-28.
[9]郭海涛,孙大军.数字图象目标圆的计算方法[J].黑龙江大学自然科学学报,2011,18(2)：53-55.
[10]Athanas P M, Abbott A L. Real-time image processing on a custom computing platform [J]. Computer,1995,28(2):16-25.
[11]Kleynen O, Leemans V, Destain M F. Development of a multi-spectral vision system for the detection of defects on apples[J]. Journal of Food Engineering,2005,69(1):41-49.
[12]Brosnan T, Sun D W. Inspection and grading of agricultural and food products by computer vision systems—a review[J]. Computers and electronics in agriculture,2002,36(2): 193-213.
[13]应义斌,付峰.水果品质机器视觉检测中的图像颜色变换模型[J].农业机械学报,2004,35(1)：85-89.
[14]邱道尹,张红涛,刘新宇,等.基于机器视觉的大田害虫检测系统[J].农业机械学报, 2007,38(1)：120-122.
[15]左奇,史忠科.基于机器视觉的胶囊完整性检测系统研究[J].西安交通大学学报,2002,36(12)：1262-1265.
[16]Lee B Y, Tarng Y S. Surface roughness inspection by computer vision in turning operations[J].International Journal of Machine Tools and Manufacture,2001,41(9): 1251-1263.
[17]Newman T S, Flynn P J, Jain A K. Model-based classification of quadric surfaces[J]. CVGIP:Image Understanding,2008,58(2):235-249.
[18]Lu S.Improving edge measurement on noisy image by hierarchial NN[J].Pattern Recognition Letters.2005:12-155.
[19]刘巍,丁未思.基于计算机视觉的高精度瓷砖质检系统[J].微电子学与计算机,2007,24(1)：55-58.
[20]沈晶,杨志学.一种新的基于纹理分水岭的纺织品缺陷检测方法[J].中国图象图形学报,2009,10：1997-2003.
[21]龙永红,吴敏.基于灰度共生矩阵和颜色特征的泡罩包装缺陷检测[J].湖南大学学报(自然科学版)2005,32(6)65-68.
[22]苏彩红,朱学峰,刘笛.基于灰度共生矩阵和颜色特征的墙地砖缺陷自动检测的研究[J].计算机工程与应用,2004,11：229-231.
[23]李益红,卢朝阳,李静,等.一种提取局部区域共同向量的瑕疵分类算法[J].西安电子科技大学学报,2011,38(5)：59-64.
[24]郭航,霍宏涛.灰度共生矩阵在皮肤纹理检测中的应用研究[J].中国图象图形学报,2010,15(007)：1074-1078.
[25]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.
[26]潘如如,高卫东,张星烨.基于动态聚类的织物疵点识别算法[J].纺织学报,2008,29(10)：122-126.
[27]刘帅奇,胡绍海,肖扬,等.基于局部混合滤波的SAR图像边缘检测[J].电子与信息学报,2013,35(5)：1120-11 27.
[28]Liu Y H, Lin S H, Hsueh Y L, et al. Automatic target defect identification for TFT-LCD array process inspection using kernel FCM-based fuzzy SVDD ensemble[J]. Expert Systems with Applications,2009,36(2):1978-1998.
[29]Kim D G, Burks T F, Qin J, et al. Classification of grapefruit peel diseases using color texture feature analysis[J]. International Journal of Agricultural and Biological Engineering, 2009,2(3):41-50.
[30]TeohOng A, Ibrahim Z B, Ramli S. Computer machine vision inspection on printed circuit boards flux defects [J]. American Journal of Engineering and Applied Sciences,2013, 6(3):263.
[31]Xiong G, Lu J, Wu S. New Automatic Optical inspection system for PCB[C]//Proceed-ings of the 2012 International Conference on Electronics, Communications and Control. IEEE Computer Society,2012:1270-1273.
[32]Anu A, Anouncia M, Rajaji L. Automatic visual inspection of PCB using CAD information[C]//Radar,Communication and Computing(ICRCC),2012 International Confe-rence on. IEEE,2012:217-220.
[33]Tien F C, Yeh C H, Hsieh K H. Automated visual inspection for microdrills in printed circuit board production[J]. International Journal of Production Research,2004,42(12): 2477-2495.
[34]Leta F R, Feliciano F F, Martins F P R. Computer vision system for printed circuit board inspection[C]//ABCM Symp. Series in Mechatronics.2008,3:623-632.
[35]Kim N H, Pyun J Y, Choi K S, et al. Real-time inspection system for printed circuit boards[C]//IEEE International Symposium on. IEEE,2001,1:166-170.
[36]Su J C, Huang C K, Tarng Y S. An automated flank wear measurement of microdrills using machine vision[J]. Journal of materials processing technology,2006,180(1):328-335.
[37]陈晓辉,刘晓军.印刷电路板瑕疵检测与分类算法[J].华中科技大学学报：自然科学版,2010,38(7)：70-73.
[38]肇祺,张学工.模式识别[M].清华大学出版社,2000.
[39]Zahn C T, Roskies R Z. Fourier descriptors for plane closed curves[J]. Computers, IEEE Transactions on,1972,100(3):269-281.
[40]Persoon E, Fu K S. Shape discrimination using Fourier descriptors[J]. Systems, Man and Cybernetics, IEEE Transactions on,1977,7(3):170-179.
[41]Krzyzak A, Leung S Y, Suen C Y. Reconstruction of two-dimensional patterns from Fourier descriptors[J]. Machine vision and applications,1989,2(3):123-140.
[42]Reeves A P, Prokop R J, Andrews S E, et al. Three-dimensional shape analysis using moments and Fourier descriptors[J].Pattern Analysis and Machine Intelligence, IEEE Transactions on,1988,10(6):937-943.
[43]Kirby M, Sirovich L. Application of the Karhunen-Loeve procedure for the charac-terization of human faces[J].Pattern Analysis and Machine Intelligence, IEEE Transactions on,1990,12(1):103-108.
[44]Khotanzad A, Hong Y H. Invariant image recognition by Zernike moments[J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on,1990,12(5):489-497.
[45]Hu M K. Visual pattern recognition by moment invariants [J]. Information Theory, IRE Transactions on,1962,8(2):179-187.
[46]Wee C Y, Paramesran R. On the computational aspects of Zernike moments [J]. Image and Vision Computing,2007,25(6):967-980.
[47]Amayeh G, Erol A, Bebis G, et al. Accurate and efficient computation of high order zernike moments[M].Advances in visual computing. Springer Berlin Heidelberg,2005: 462-469.
[48]Kotoulas L, Andreadis I. Real-time computation of Zernike moments[J]. Circuits and Systems for Video Technology, IEEE Transactions on,2005,15(6):801-809.
[49]罗四维,赵连伟.基于谱图理论的流形学习算法[J].计算机研究与发展,2006,43(7)：1173-1179.
[50]Cheon Y, Kim D. Natural facial expression recognition using differential-AAM and manifold learning[J]. Pattern Recognition,2009,42(7):1340-1350.
[51]Schwarz L A, Mateus D, Castaneda V, et al. Manifold Learning for ToF-based Human Body Tracking and Activity Recognition[C]//BMVC.2010:1-11.
[52]Liu Z, Yin Y, Wang H, et al. Finger vein recognition with manifold learning[J]. Journal of Network and Computer Applications,2010,33(3):275-282.
[53]陈小娜,章程辉.绿橙表面缺陷的计算机视觉分级技术[J].农机化研究,2009, 31(10)：126-129.
[54]刘静,黄勇平,章程辉.视觉系统开发模块在芒果果面缺陷检测中的应用[J].食品与机械,2009,25(2)：210-213.
[55]陈文飞,廖斌,许雪峰等.基于Piecewise直方图均衡化的图像增强方法[J].通信学报,2011,32(9)：153-160.
[56]李博,刘胜兰,张丽艳.基于流形调和变换的浅浮雕生成算法[J].计算机辅助设计与图形学学报,2012,24(2)：252-261.
[57]左凯,孙同景,李振华等.二维分数阶卡尔曼滤波及其在图像处理中的应用[J].电子与信息学报,2010,32(12)：3027-3031.
[58]刘洪江.基于机器视觉的毛杆缺陷检测技术的研究[D].广州：广东工业大学,2011.
[59]刘振华,毛士艺,袁运能.SAR图像组合分割算法[J].电子学报,2003,6(6)：833-836.
[60]税午阳,周明全,耿国华.磁共振颅脑图像的脑组织自动获取方法[J].软件学报,2009,20(5)：1139-1145.
[61]范九伦,赵凤.灰度图像的二维Otsu曲线阈值分割法[J].电子学报,2007,35(4)：751-755.
[62]叶传奇,苗启广,王宝树.基于区域分割和Counterlet变换的图像融合算法[J].光学学报,2008,28(3)：447-453.
[63]马腾飞,郑永果,赵卫东.基于边缘检测与Hough变换的车牌字符分割算法[J].系统仿真学报,2006,18(1)：391-392.
[64]冯衍秋,陈武凡,梁斌等.基于Gibbs随机场与模糊C均值聚类的图像分割新算法[J].电子学报,2004,32(4)：645-647.
[65]徐建华.图像处理与分析[M].科学出版社,1992.
[66]Marr D, Hildreth E. Theory of edge detection[J]. Proceedings of the Royal Society of London. Series B. Biological Sciences,1980,207(1167):187-217.
[67]陶新民,徐晶,杨立标等.一种改进的粒子群和K均值混合聚类算法[J].电子与信息学报,2010,32(1)：92-97.
[68]曹易,张宁.一种改进的模糊C-均值聚类算法[J].上海理工大学学报,2012,34(4)：351-354.
[69]Zhou H, Yuan Y, Shi C. Object tracking using SIFT features and mean shift[J]. Computer Vision and Image Understanding,2009,113(3):345-352.
[70]Shi Jianbo,Malik J. Normalized cuts and image segmentation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22 (8):888-905.
[71]李小斌,田铮,刘密歌,等.基于加权割的图像分割[J].电子学报,2008,36(1)：76-80.
[72]Ng A Y, Jordan M I, Weiss Y. On spectral clustering:Analysis and an algorithm[J]. Advances in Neural Information Processing Systems,2002,2:849-856.
[73]Shawe-Taylor J, Cristianini N, Kandola J S. On the concentration of spectral properties [C]//Advances in Neural Information Processing Systems.2001:511-517.
[74]Wang Song,Siskind J M.Image segmentation with ratio cut[J].IEEE Trans on Pattern Anal Mach Intell,2003,25(6):675-690.
[75]Blongie S, Malik J. Efficient spatiotemporal grouping using the nystrom method[J]. IEEE Computer Vision and Pattern Recognition.2001,1:231-238.
[76]David Toliver, Robert T. Multilevel spectral partitioning for efficient image segmen-tation and tracking[C]//Proceeding of the Seventh IEEE Workshop on Applications of Computer Vision. Breckenridge:CO,2005:414-420.
[77]Aleix M M,Pardit Mittrapiyanuruk,Avinash C K. On combining graph-partitioning with non-parametric clustering for image segmentation[J].Computer Vision and Image Understanding,2004,95(1):72-85.
[78]Wu Z, Leahy R. An optimal graph theoretic approach to data clustering:Theory and its application to image segmentation[J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on,1993,15(11):1101-1113.
[79]Sarkar S, Soundararajan P. Supervised learning of large perceptual organization:Graph spectral partitioning and learning automata[J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on,2000,22(5):504-525.
[80]Ding C H Q, He X, Zha H, et al. A min-max cut algorithm for graph partitioning and data clustering[C]//Data Mining,2001. ICDM 2001, Proceedings IEEE International Conference on. IEEE,2001:107-114.
[81]Y Boykov, Veksler O, Zabih R. Fast approximate energy minimization via graph cuts[J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on,2001,23(11):1222-1239.
[82]Y Boykov, M P Jolly.Interactive graph cuts for optimal boundary and region segmen-tation of objects in N-D images[J].Proceeding of IEEE International Conference on Computer Vision,2001(1):105-112.
[83]Li Yin,Sun Jian, Tang ChiKeung, etal. Lazy Snapping[C]//Proceedings of ACM SIGGRAPH 2004,23(3):303-308.
[84]C. Rother, A. Blake, V. Kolmogorov. Grabcut-interactive foreground extraction using iterated graph cuts[C]//Proceedings Process of the 2004 SIGGRAPH Conference,Los Angeles, California, USA:2004:309-314.
[85]Xu Ning, Ahuja N, Bansal R. Object segmentation using graph cuts based active contours[J]. Computer Vision and Image Understanding,2007,107(3):210-224.
[86]Cheng Chen, Houqiang Li, Xiaobo Zhou, Stephen Wong.Constraint factor graph cut based active contour method for automated cellular image segmentation in RNAi screening[J]. Journal of Microscopy,2008,230(2):177-191.
[87]陶文兵,金海,田金文等.一种新的基于图谱理论的图像阈值分割方法[J].计算机学报,2007,30(1)：110-119.
[88]Jianzhuang L, Wenqing L, Yupeng T. Automatic thresholding of gray-level pictures using two-dimension Otsu method[C]//International Conference on IEEE,1991:325-327.
[89]Feng D, Wenkang S, Liangzhou C, et al. Infrared image segmentation with 2-D maximum entropy method based on particle swarm optimization[J].Pattern Recognition Letters,2005,26(5):597-603.
[90]Blongie S, Malik J. Efficient spatiotemporal grouping using the nystrom method[J]. IEEE Computer Vision and Pattern Recognition.2001,1:231-238.
[91]Mokhtarian F, Mackworth A K. A theory of multiscale, curvature-based shape representation for planar curves[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1992,14(8):789-805.
[92]Ohm J R, Bunjamin F, Liebsch W, et al. A set of visual feature descriptors and their combination in a low-level description scheme[J].Signal Processing:Image Communi-cation.2000,16(1):157-179.
[93]Chen G, Bui T D.Invariant Fourier-wavelet descriptor for pattern recognition[J].Pattern recognition,1999,32(7):1083-1088.
[94]Zhang D,Lu G.Shape-based image retrieval using generic Fourier descriptor[J].Signal Processing:Image Communication,2002,17(10):825-848.
[95]Li J,Pan Q,Zhang H,et al.Image recognition using Radon transform[C]//Intelligent Transportation Systems.2003.Proceedings.2003 IEEE.IEEE,2003,1:741-744.
[96]Deans S R. The Radon Transform and Some of Its Applications[M].New York, Wiley:1983.
[97]吕玉增.形状仿射不变特征提取与识别研究[D].长沙：国防科学技术大学,2008.
[98]刘雄飞,郭爽,李长庚.非均匀噪声分布心电信号的奇异值小波消噪法[J].中南大学学报：自然科学版,2009,40(5)：1374-1380.
[99]Hein M,Maier M.2007. Manifold denoising[C]//In Adv. in Neural Inf. Proc. Syst.19 (NIPS).2007.
[100]Cheeger J, Yau S T. A lower bound for the heat kernel[J]. Communications on Pure and Applied Mathematics,1981,34(4):465-480.
[101]Cheng S Y, Li P, Yau S T. On the upper estimate of the heat kernel of a complete Riemannian manifold[J]. American Journal of Mathematics,1981,103(5):1021-1063.
[102]Cheng S Y,Yau S T. Differential equations on Riemannian manifolds and their geometric applications[J]. Communications on Pure and Applied Mathematics,1975,28(3): 333-354.
[103]Grigor'yan A. Heat kernel and analysis on manifolds[M]. AMS Bookstore,2012.
[104]Hein M,Maier M. Manifold Denoising[C]//Advances in Neural Information Processing Systems 20. Cambridge, MA:MIT Press,2007,8:486-500.
[105]Hein,M,Audibert,etc..From graphs to manifolds-weak and strong pointwise consis-tency of graph Laplacians[C]//In Proceeding of the 18th Conf. on Learning Theory (COLT), 2005,486-500.
[106]GRAHAM F C.Spectral graph theory[M].Fresno:American Mathematical Society, 1997.
[107]Fanghan Chen,Wensheng Wang.Target recognition in clutter scene based on wavelet transform[J].Optics Communication,2009,282(4):523-526.
[108]V R S Mani,S Arivazhagan,J Jason Braino.Multimodal image fusion using multire-solution techniques[J].International Journal of Biomedical Engineering and Technology 2013,11(1):3160-3163.
[109]Ma Jianwei,Plonka G.The curvelet transform[J]. IEEE Signal Processing Magazine, 2010,27(2):118-133.
[110]T K Kim,R Cipolla.Canonical correlation analysis of video volume tensors for action categorization and detection[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2009,31(8):1415-1428.
[111]Tuzel O, Porikli F, Meer P.Human detection via classification on Riemannian manifolds[C]//Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition (CVPR'07). Washington,DC,USA:IEEE Computer Society,2007:1-8.
[112]Yui Man Lui.Advances in matrix manifolds for computer vision[J].Image and Vision Computing,2012,30(7):380-388.
[113]Roweis S T.Saul L K.Nonlinear dimensionality reduction by locally linear embed-ding[J].Science,2000,290(5500):2323-2326.
[114]Belkin M, Niyogi P.Laplacian eigenmaps for dimensionality reduction and data representation[J].Neural computation.2003.15(6):1373-1396.
[115]Tenenbaum J B.De Silva V,Langford J C.A global geometric framework for nonlinear dimensionality reduction[J].Science.2000,290(5500):2319-2323.
[116]He X, Yan S, Hu Y, et al.Face recognition using laplacianfaces[J].Pattern Analysis and Machine Intelligence, IEEE Transactions on,2005,27(3):328-340.
[117]Zhang Z, Chow W S.Tensor locally linear discriminative analysis[J].IEEE Signal Processing Letters,2011,18(11):643-646.
[118]Si S,Tao D,Geng B.Bregman divergence-based regularization for transfer subspace learningfJ].Knowledge and Data Engineering. IEEE Transactions on.2010,22(7):929-942.
[119]Weiwei Y.Two-dimensional discriminant locality preserving projections for face recognition[J].Pattern Recognition Letters.2009.30(15):1378-1383.
[120]Pennec X, Fillard P,Ayache N.A Riemannian framework for tensor computing[J]. International Journal of Computer Vision,2006,66(1):41-66.
[121]Sommer S,Lauze F,Nielsen M.Optimization over geodesics for exact principal geo-desic analysis[J].Advances in Computational Mathematics,2013,38(2):281-320.
[122]Harandi M T,Sanderson C,Shirazi S,et al.Graph embedding discriminant analysis on Grassmannian manifolds for improved image set matching[C]//Computer Vision and Pattern Recognition (CVPR),2011 IEEE Conference on.IEEE,2011:2705-2712.
[123]Lui Y M.Advances in matrix manifolds for computer vision[J].Image and Vision Computing,2012,30(6):380-388.
[124]Pennec X.Intrinsic statistics on Riemannian manifolds:basic tools for geometric measurements[J].Journal of Mathematical Imaging and Vision,2006,25(1):127-154.
[125]Tuzel O,Porikli F,Meer P.Pedestrian detection via classification on riemannian manifolds[J].Pattern Analysis and Machine Intelligence, IEEE Transactions on,2008, 30(10):1713-1727.
[126]Turaga P, Veeraraghavan A, Srivastava A, et al.Statistical computations on grassmann and stiefel manifolds for image and video-based recognition[J].Pattern Analysis and Machine Intelligence, IEEE Transactions on,2011,33(11):2273-2286.
[127]陈省身,陈维桓.微分几何讲义[M].北京大学出版社,1983.
[128]Absil P A,Mahony R,Sepulchre R.Optimization algorithms on matrix manifolds[M]. Princeton University Press,2009.
[129]Eisenhart L P.Riemannian geometry[M].Princeton university press,1997.
[130]Tuzel O,Porikli F,Meer P.Region covariance:A fast descriptor for detection and classification[C]//Computer Vision-ECCV 2006.Springer Berlin Heidelberg,2006:589-600.
[131]Fletcher P TJoshi S.Riemannian geometry for the statistical analysis of diffusion tensor data[J].Signal Processing,2007,87(2):250-262.
[132]Tuzel O, Porikli F, Meer P. Human detection via classification on riemannian manifolds[C]//Computer Vision and Pattern Recognition,2007. CVPR'07. IEEE Conference on. IEEE,2007:1-8.
[133]Porikli F, Tuzel O, Meer P. Covariance tracking using model update based on Lie algebra[C]//Proceedings of the 2006 IEEE Conference on Computer Vision and Pattern Recognition(CVPR'06).Washington,DC,USA:IEEE Computer Society,2006:728-735.
[134]Arsigny V,Fillard P,Pennec X,et al.Geometric means in a novel vector space structure on symmetric positive-definite matrices[J].SIAM Journal on Matrix Analysis and Applications,2007,29(1):328-347.
[135]Hall S B,LIE G.Lie algebras.and representations:an elementary introduction[M]. New York:Springer,2003.
[136]Scholkopf B,Smola A,Muller K-R.Nonlinear component analysis as a kernel eigen-value problem[J].Neural Computation,1998,10(5):1299-1319.
[137]Cheng Jian, Liu Qingshan,Lu Hanqing,et al.Texture classification using kernel inde-pendent component analysis[C]//Proceedings of the 17th International Conference on Pattern Recognition.Washington,DC,USA:IEEE ComputerComputer Society,2004:620-623.
[138]Mika S,Ratsch G,Weston J,et al.Fisher discriminant analysis with kernels[C]//Pro-ceed ings of the 1999 IEEE Signal Processing Society Workshop on Neural Networks for Signal Processing IX,1999:41-48.
[139]Wright J. Yang A Y,Ganesh A.et al.Robust face recognition via sparse representation [J].Pattern Analysis and Machine Intelligence,IEEE Transactions on,2009,31(2):210-227.
[140]Qiao L,Chen S,Tan X.Sparsity preserving projections with applications to face reco-gnition[J].Pattern Recognition,2010,43(1):331-341.
[141]殷俊,杨万扣.核稀疏保持投影及生物特征识别应用[J].电子学报,2013,41(4)：639-645.
[142]王科俊,阎涛,吕卓纹等.核稀疏保留投影及在步态识别中的应用[J].中国图象图形学报,2013,18(003)：257-263.
[143]赵佳佳,唐峥远,杨杰等.基于图像稀疏表示的红外小目标检测算法[J].红外与毫米波学报.2011.30(2)：156-161.
[144]王燕霞,张弓.基于特征参数稀疏表示的SAR图像目标识别[J].重庆邮电大学学报(自然科学版),2012,24(3)：308-313.
[145]李树涛,魏丹.压缩传感综述[J].自动化学报,2009,35(11)：1369-1377.
[146]戴琼海,付长军,季向阳.压缩感知研究[J].计算机学报,2011,34(3)：425-434.
[147]Tropp J A,Gilbert A C.Signal recovery from random measurements via orthogonal matching pursuit [J]. IEEE Transac-tions on Information Theory,2007,53(12):4655-4665.
[148]Karabulut G Z,Anario D,.Flexible tree search based ortho- gonal matching pursuit algorithm[C]//IEEE International Conference on Acoustics, Speech,and Signal Processing, 2005:673-676.
[149]Deana N,Roman V. Signal recovery from incomplete and inaccurate measure- ments via regularized orthogonal match-ing pursuit[J].IEEE journal of selected topics in signal processing,2010,4(2):310-316.
[150]Dot T,L Gan,Guyen N,Tran T D. Sparsity adaptive matching pursuit algorithm for practical compressed sensing[C]//Asilomar Conference on Signals,Systems and Computers, 2008:581-587.
[151]Ando T.Concavity of certain maps on positive definite matrices and applications to Hadamard products[J].Linear Algebra and its Applications,1979,26:203-241.
[152]Sra S. Positive definite matrices and the symmetric Stein divergence[J]. arXiv preprint arXiv:1110.1773,2011.
[153]Berg C, Christensen J P R, Ressel P.Harmonic analysis on semigroups[M].Springer, 1984.
[2]敖波,邬冠华,邓翠贞,等.航空精密铸件射线CR成像检测研究[J].铸造,2011,60(9)：884-887.
[3]郑雪莲,杨北革,逯海军,等.基于改进多尺度CB形态学的输电线路覆冰厚度检测[J].电测与仪表,2013(4)：45-49.
[4]Schmidberger E J, Ahlers R J. Quality control with a robot-guided electro-optical sensor[C]//Proc. of Ⅳ Conf. on Robot Vision and Sensory Control. London.2006:27-36.
[5]Lu R S, Li Y F, Yu Q. On-line measurement of the straightness of seamless steel pipes using machine vision technique[J].Sensors and Actuators A:Physical,2001,94(1):95-101.
[6]周烜.微机在工件不圆度自动测量中的应用[J].基础自动化,2010,8(6)：50-52.
[7]廖强,米林,周忆,等.微尺寸视觉精密检测系统设计[J].重庆大学学报(自然科学版),2007,12(6)：21-23.
[8]熊海林,陈香萍.圆度误差的一种微机图像测量法[J].航空计测技术,1998,18(4)：25-28.
[9]郭海涛,孙大军.数字图象目标圆的计算方法[J].黑龙江大学自然科学学报,2011,18(2)：53-55.
[10]Athanas P M, Abbott A L. Real-time image processing on a custom computing platform [J]. Computer,1995,28(2):16-25.
[11]Kleynen O, Leemans V, Destain M F. Development of a multi-spectral vision system for the detection of defects on apples[J]. Journal of Food Engineering,2005,69(1):41-49.
[12]Brosnan T, Sun D W. Inspection and grading of agricultural and food products by computer vision systems—a review[J]. Computers and electronics in agriculture,2002,36(2): 193-213.
[13]应义斌,付峰.水果品质机器视觉检测中的图像颜色变换模型[J].农业机械学报,2004,35(1)：85-89.
[14]邱道尹,张红涛,刘新宇,等.基于机器视觉的大田害虫检测系统[J].农业机械学报, 2007,38(1)：120-122.
[15]左奇,史忠科.基于机器视觉的胶囊完整性检测系统研究[J].西安交通大学学报,2002,36(12)：1262-1265.
[16]Lee B Y, Tarng Y S. Surface roughness inspection by computer vision in turning operations[J].International Journal of Machine Tools and Manufacture,2001,41(9): 1251-1263.
[17]Newman T S, Flynn P J, Jain A K. Model-based classification of quadric surfaces[J]. CVGIP:Image Understanding,2008,58(2):235-249.
[18]Lu S.Improving edge measurement on noisy image by hierarchial NN[J].Pattern Recognition Letters.2005:12-155.
[19]刘巍,丁未思.基于计算机视觉的高精度瓷砖质检系统[J].微电子学与计算机,2007,24(1)：55-58.
[20]沈晶,杨志学.一种新的基于纹理分水岭的纺织品缺陷检测方法[J].中国图象图形学报,2009,10：1997-2003.
[21]龙永红,吴敏.基于灰度共生矩阵和颜色特征的泡罩包装缺陷检测[J].湖南大学学报(自然科学版)2005,32(6)65-68.
[22]苏彩红,朱学峰,刘笛.基于灰度共生矩阵和颜色特征的墙地砖缺陷自动检测的研究[J].计算机工程与应用,2004,11：229-231.
[23]李益红,卢朝阳,李静,等.一种提取局部区域共同向量的瑕疵分类算法[J].西安电子科技大学学报,2011,38(5)：59-64.
[24]郭航,霍宏涛.灰度共生矩阵在皮肤纹理检测中的应用研究[J].中国图象图形学报,2010,15(007)：1074-1078.
[25]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.
[26]潘如如,高卫东,张星烨.基于动态聚类的织物疵点识别算法[J].纺织学报,2008,29(10)：122-126.
[27]刘帅奇,胡绍海,肖扬,等.基于局部混合滤波的SAR图像边缘检测[J].电子与信息学报,2013,35(5)：1120-11 27.
[28]Liu Y H, Lin S H, Hsueh Y L, et al. Automatic target defect identification for TFT-LCD array process inspection using kernel FCM-based fuzzy SVDD ensemble[J]. Expert Systems with Applications,2009,36(2):1978-1998.
[29]Kim D G, Burks T F, Qin J, et al. Classification of grapefruit peel diseases using color texture feature analysis[J]. International Journal of Agricultural and Biological Engineering, 2009,2(3):41-50.
[30]TeohOng A, Ibrahim Z B, Ramli S. Computer machine vision inspection on printed circuit boards flux defects [J]. American Journal of Engineering and Applied Sciences,2013, 6(3):263.
[31]Xiong G, Lu J, Wu S. New Automatic Optical inspection system for PCB[C]//Proceed-ings of the 2012 International Conference on Electronics, Communications and Control. IEEE Computer Society,2012:1270-1273.
[32]Anu A, Anouncia M, Rajaji L. Automatic visual inspection of PCB using CAD information[C]//Radar,Communication and Computing(ICRCC),2012 International Confe-rence on. IEEE,2012:217-220.
[33]Tien F C, Yeh C H, Hsieh K H. Automated visual inspection for microdrills in printed circuit board production[J]. International Journal of Production Research,2004,42(12): 2477-2495.
[34]Leta F R, Feliciano F F, Martins F P R. Computer vision system for printed circuit board inspection[C]//ABCM Symp. Series in Mechatronics.2008,3:623-632.
[35]Kim N H, Pyun J Y, Choi K S, et al. Real-time inspection system for printed circuit boards[C]//IEEE International Symposium on. IEEE,2001,1:166-170.
[36]Su J C, Huang C K, Tarng Y S. An automated flank wear measurement of microdrills using machine vision[J]. Journal of materials processing technology,2006,180(1):328-335.
[37]陈晓辉,刘晓军.印刷电路板瑕疵检测与分类算法[J].华中科技大学学报：自然科学版,2010,38(7)：70-73.
[38]肇祺,张学工.模式识别[M].清华大学出版社,2000.
[39]Zahn C T, Roskies R Z. Fourier descriptors for plane closed curves[J]. Computers, IEEE Transactions on,1972,100(3):269-281.
[40]Persoon E, Fu K S. Shape discrimination using Fourier descriptors[J]. Systems, Man and Cybernetics, IEEE Transactions on,1977,7(3):170-179.
[41]Krzyzak A, Leung S Y, Suen C Y. Reconstruction of two-dimensional patterns from Fourier descriptors[J]. Machine vision and applications,1989,2(3):123-140.
[42]Reeves A P, Prokop R J, Andrews S E, et al. Three-dimensional shape analysis using moments and Fourier descriptors[J].Pattern Analysis and Machine Intelligence, IEEE Transactions on,1988,10(6):937-943.
[43]Kirby M, Sirovich L. Application of the Karhunen-Loeve procedure for the charac-terization of human faces[J].Pattern Analysis and Machine Intelligence, IEEE Transactions on,1990,12(1):103-108.
[44]Khotanzad A, Hong Y H. Invariant image recognition by Zernike moments[J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on,1990,12(5):489-497.
[45]Hu M K. Visual pattern recognition by moment invariants [J]. Information Theory, IRE Transactions on,1962,8(2):179-187.
[46]Wee C Y, Paramesran R. On the computational aspects of Zernike moments [J]. Image and Vision Computing,2007,25(6):967-980.
[47]Amayeh G, Erol A, Bebis G, et al. Accurate and efficient computation of high order zernike moments[M].Advances in visual computing. Springer Berlin Heidelberg,2005: 462-469.
[48]Kotoulas L, Andreadis I. Real-time computation of Zernike moments[J]. Circuits and Systems for Video Technology, IEEE Transactions on,2005,15(6):801-809.
[49]罗四维,赵连伟.基于谱图理论的流形学习算法[J].计算机研究与发展,2006,43(7)：1173-1179.
[50]Cheon Y, Kim D. Natural facial expression recognition using differential-AAM and manifold learning[J]. Pattern Recognition,2009,42(7):1340-1350.
[51]Schwarz L A, Mateus D, Castaneda V, et al. Manifold Learning for ToF-based Human Body Tracking and Activity Recognition[C]//BMVC.2010:1-11.
[52]Liu Z, Yin Y, Wang H, et al. Finger vein recognition with manifold learning[J]. Journal of Network and Computer Applications,2010,33(3):275-282.
[53]陈小娜,章程辉.绿橙表面缺陷的计算机视觉分级技术[J].农机化研究,2009, 31(10)：126-129.
[54]刘静,黄勇平,章程辉.视觉系统开发模块在芒果果面缺陷检测中的应用[J].食品与机械,2009,25(2)：210-213.
[55]陈文飞,廖斌,许雪峰等.基于Piecewise直方图均衡化的图像增强方法[J].通信学报,2011,32(9)：153-160.
[56]李博,刘胜兰,张丽艳.基于流形调和变换的浅浮雕生成算法[J].计算机辅助设计与图形学学报,2012,24(2)：252-261.
[57]左凯,孙同景,李振华等.二维分数阶卡尔曼滤波及其在图像处理中的应用[J].电子与信息学报,2010,32(12)：3027-3031.
[58]刘洪江.基于机器视觉的毛杆缺陷检测技术的研究[D].广州：广东工业大学,2011.
[59]刘振华,毛士艺,袁运能.SAR图像组合分割算法[J].电子学报,2003,6(6)：833-836.
[60]税午阳,周明全,耿国华.磁共振颅脑图像的脑组织自动获取方法[J].软件学报,2009,20(5)：1139-1145.
[61]范九伦,赵凤.灰度图像的二维Otsu曲线阈值分割法[J].电子学报,2007,35(4)：751-755.
[62]叶传奇,苗启广,王宝树.基于区域分割和Counterlet变换的图像融合算法[J].光学学报,2008,28(3)：447-453.
[63]马腾飞,郑永果,赵卫东.基于边缘检测与Hough变换的车牌字符分割算法[J].系统仿真学报,2006,18(1)：391-392.
[64]冯衍秋,陈武凡,梁斌等.基于Gibbs随机场与模糊C均值聚类的图像分割新算法[J].电子学报,2004,32(4)：645-647.
[65]徐建华.图像处理与分析[M].科学出版社,1992.
[66]Marr D, Hildreth E. Theory of edge detection[J]. Proceedings of the Royal Society of London. Series B. Biological Sciences,1980,207(1167):187-217.
[67]陶新民,徐晶,杨立标等.一种改进的粒子群和K均值混合聚类算法[J].电子与信息学报,2010,32(1)：92-97.
[68]曹易,张宁.一种改进的模糊C-均值聚类算法[J].上海理工大学学报,2012,34(4)：351-354.
[69]Zhou H, Yuan Y, Shi C. Object tracking using SIFT features and mean shift[J]. Computer Vision and Image Understanding,2009,113(3):345-352.
[70]Shi Jianbo,Malik J. Normalized cuts and image segmentation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22 (8):888-905.
[71]李小斌,田铮,刘密歌,等.基于加权割的图像分割[J].电子学报,2008,36(1)：76-80.
[72]Ng A Y, Jordan M I, Weiss Y. On spectral clustering:Analysis and an algorithm[J]. Advances in Neural Information Processing Systems,2002,2:849-856.
[73]Shawe-Taylor J, Cristianini N, Kandola J S. On the concentration of spectral properties [C]//Advances in Neural Information Processing Systems.2001:511-517.
[74]Wang Song,Siskind J M.Image segmentation with ratio cut[J].IEEE Trans on Pattern Anal Mach Intell,2003,25(6):675-690.
[75]Blongie S, Malik J. Efficient spatiotemporal grouping using the nystrom method[J]. IEEE Computer Vision and Pattern Recognition.2001,1:231-238.
[76]David Toliver, Robert T. Multilevel spectral partitioning for efficient image segmen-tation and tracking[C]//Proceeding of the Seventh IEEE Workshop on Applications of Computer Vision. Breckenridge:CO,2005:414-420.
[77]Aleix M M,Pardit Mittrapiyanuruk,Avinash C K. On combining graph-partitioning with non-parametric clustering for image segmentation[J].Computer Vision and Image Understanding,2004,95(1):72-85.
[78]Wu Z, Leahy R. An optimal graph theoretic approach to data clustering:Theory and its application to image segmentation[J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on,1993,15(11):1101-1113.
[79]Sarkar S, Soundararajan P. Supervised learning of large perceptual organization:Graph spectral partitioning and learning automata[J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on,2000,22(5):504-525.
[80]Ding C H Q, He X, Zha H, et al. A min-max cut algorithm for graph partitioning and data clustering[C]//Data Mining,2001. ICDM 2001, Proceedings IEEE International Conference on. IEEE,2001:107-114.
[81]Y Boykov, Veksler O, Zabih R. Fast approximate energy minimization via graph cuts[J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on,2001,23(11):1222-1239.
[82]Y Boykov, M P Jolly.Interactive graph cuts for optimal boundary and region segmen-tation of objects in N-D images[J].Proceeding of IEEE International Conference on Computer Vision,2001(1):105-112.
[83]Li Yin,Sun Jian, Tang ChiKeung, etal. Lazy Snapping[C]//Proceedings of ACM SIGGRAPH 2004,23(3):303-308.
[84]C. Rother, A. Blake, V. Kolmogorov. Grabcut-interactive foreground extraction using iterated graph cuts[C]//Proceedings Process of the 2004 SIGGRAPH Conference,Los Angeles, California, USA:2004:309-314.
[85]Xu Ning, Ahuja N, Bansal R. Object segmentation using graph cuts based active contours[J]. Computer Vision and Image Understanding,2007,107(3):210-224.
[86]Cheng Chen, Houqiang Li, Xiaobo Zhou, Stephen Wong.Constraint factor graph cut based active contour method for automated cellular image segmentation in RNAi screening[J]. Journal of Microscopy,2008,230(2):177-191.
[87]陶文兵,金海,田金文等.一种新的基于图谱理论的图像阈值分割方法[J].计算机学报,2007,30(1)：110-119.
[88]Jianzhuang L, Wenqing L, Yupeng T. Automatic thresholding of gray-level pictures using two-dimension Otsu method[C]//International Conference on IEEE,1991:325-327.
[89]Feng D, Wenkang S, Liangzhou C, et al. Infrared image segmentation with 2-D maximum entropy method based on particle swarm optimization[J].Pattern Recognition Letters,2005,26(5):597-603.
[90]Blongie S, Malik J. Efficient spatiotemporal grouping using the nystrom method[J]. IEEE Computer Vision and Pattern Recognition.2001,1:231-238.
[91]Mokhtarian F, Mackworth A K. A theory of multiscale, curvature-based shape representation for planar curves[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1992,14(8):789-805.
[92]Ohm J R, Bunjamin F, Liebsch W, et al. A set of visual feature descriptors and their combination in a low-level description scheme[J].Signal Processing:Image Communi-cation.2000,16(1):157-179.
[93]Chen G, Bui T D.Invariant Fourier-wavelet descriptor for pattern recognition[J].Pattern recognition,1999,32(7):1083-1088.
[94]Zhang D,Lu G.Shape-based image retrieval using generic Fourier descriptor[J].Signal Processing:Image Communication,2002,17(10):825-848.
[95]Li J,Pan Q,Zhang H,et al.Image recognition using Radon transform[C]//Intelligent Transportation Systems.2003.Proceedings.2003 IEEE.IEEE,2003,1:741-744.
[96]Deans S R. The Radon Transform and Some of Its Applications[M].New York, Wiley:1983.
[97]吕玉增.形状仿射不变特征提取与识别研究[D].长沙：国防科学技术大学,2008.
[98]刘雄飞,郭爽,李长庚.非均匀噪声分布心电信号的奇异值小波消噪法[J].中南大学学报：自然科学版,2009,40(5)：1374-1380.
[99]Hein M,Maier M.2007. Manifold denoising[C]//In Adv. in Neural Inf. Proc. Syst.19 (NIPS).2007.
[100]Cheeger J, Yau S T. A lower bound for the heat kernel[J]. Communications on Pure and Applied Mathematics,1981,34(4):465-480.
[101]Cheng S Y, Li P, Yau S T. On the upper estimate of the heat kernel of a complete Riemannian manifold[J]. American Journal of Mathematics,1981,103(5):1021-1063.
[102]Cheng S Y,Yau S T. Differential equations on Riemannian manifolds and their geometric applications[J]. Communications on Pure and Applied Mathematics,1975,28(3): 333-354.
[103]Grigor'yan A. Heat kernel and analysis on manifolds[M]. AMS Bookstore,2012.
[104]Hein M,Maier M. Manifold Denoising[C]//Advances in Neural Information Processing Systems 20. Cambridge, MA:MIT Press,2007,8:486-500.
[105]Hein,M,Audibert,etc..From graphs to manifolds-weak and strong pointwise consis-tency of graph Laplacians[C]//In Proceeding of the 18th Conf. on Learning Theory (COLT), 2005,486-500.
[106]GRAHAM F C.Spectral graph theory[M].Fresno:American Mathematical Society, 1997.
[107]Fanghan Chen,Wensheng Wang.Target recognition in clutter scene based on wavelet transform[J].Optics Communication,2009,282(4):523-526.
[108]V R S Mani,S Arivazhagan,J Jason Braino.Multimodal image fusion using multire-solution techniques[J].International Journal of Biomedical Engineering and Technology 2013,11(1):3160-3163.
[109]Ma Jianwei,Plonka G.The curvelet transform[J]. IEEE Signal Processing Magazine, 2010,27(2):118-133.
[110]T K Kim,R Cipolla.Canonical correlation analysis of video volume tensors for action categorization and detection[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2009,31(8):1415-1428.
[111]Tuzel O, Porikli F, Meer P.Human detection via classification on Riemannian manifolds[C]//Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition (CVPR'07). Washington,DC,USA:IEEE Computer Society,2007:1-8.
[112]Yui Man Lui.Advances in matrix manifolds for computer vision[J].Image and Vision Computing,2012,30(7):380-388.
[113]Roweis S T.Saul L K.Nonlinear dimensionality reduction by locally linear embed-ding[J].Science,2000,290(5500):2323-2326.
[114]Belkin M, Niyogi P.Laplacian eigenmaps for dimensionality reduction and data representation[J].Neural computation.2003.15(6):1373-1396.
[115]Tenenbaum J B.De Silva V,Langford J C.A global geometric framework for nonlinear dimensionality reduction[J].Science.2000,290(5500):2319-2323.
[116]He X, Yan S, Hu Y, et al.Face recognition using laplacianfaces[J].Pattern Analysis and Machine Intelligence, IEEE Transactions on,2005,27(3):328-340.
[117]Zhang Z, Chow W S.Tensor locally linear discriminative analysis[J].IEEE Signal Processing Letters,2011,18(11):643-646.
[118]Si S,Tao D,Geng B.Bregman divergence-based regularization for transfer subspace learningfJ].Knowledge and Data Engineering. IEEE Transactions on.2010,22(7):929-942.
[119]Weiwei Y.Two-dimensional discriminant locality preserving projections for face recognition[J].Pattern Recognition Letters.2009.30(15):1378-1383.
[120]Pennec X, Fillard P,Ayache N.A Riemannian framework for tensor computing[J]. International Journal of Computer Vision,2006,66(1):41-66.
[121]Sommer S,Lauze F,Nielsen M.Optimization over geodesics for exact principal geo-desic analysis[J].Advances in Computational Mathematics,2013,38(2):281-320.
[122]Harandi M T,Sanderson C,Shirazi S,et al.Graph embedding discriminant analysis on Grassmannian manifolds for improved image set matching[C]//Computer Vision and Pattern Recognition (CVPR),2011 IEEE Conference on.IEEE,2011:2705-2712.
[123]Lui Y M.Advances in matrix manifolds for computer vision[J].Image and Vision Computing,2012,30(6):380-388.
[124]Pennec X.Intrinsic statistics on Riemannian manifolds:basic tools for geometric measurements[J].Journal of Mathematical Imaging and Vision,2006,25(1):127-154.
[125]Tuzel O,Porikli F,Meer P.Pedestrian detection via classification on riemannian manifolds[J].Pattern Analysis and Machine Intelligence, IEEE Transactions on,2008, 30(10):1713-1727.
[126]Turaga P, Veeraraghavan A, Srivastava A, et al.Statistical computations on grassmann and stiefel manifolds for image and video-based recognition[J].Pattern Analysis and Machine Intelligence, IEEE Transactions on,2011,33(11):2273-2286.
[127]陈省身,陈维桓.微分几何讲义[M].北京大学出版社,1983.
[128]Absil P A,Mahony R,Sepulchre R.Optimization algorithms on matrix manifolds[M]. Princeton University Press,2009.
[129]Eisenhart L P.Riemannian geometry[M].Princeton university press,1997.
[130]Tuzel O,Porikli F,Meer P.Region covariance:A fast descriptor for detection and classification[C]//Computer Vision-ECCV 2006.Springer Berlin Heidelberg,2006:589-600.
[131]Fletcher P TJoshi S.Riemannian geometry for the statistical analysis of diffusion tensor data[J].Signal Processing,2007,87(2):250-262.
[132]Tuzel O, Porikli F, Meer P. Human detection via classification on riemannian manifolds[C]//Computer Vision and Pattern Recognition,2007. CVPR'07. IEEE Conference on. IEEE,2007:1-8.
[133]Porikli F, Tuzel O, Meer P. Covariance tracking using model update based on Lie algebra[C]//Proceedings of the 2006 IEEE Conference on Computer Vision and Pattern Recognition(CVPR'06).Washington,DC,USA:IEEE Computer Society,2006:728-735.
[134]Arsigny V,Fillard P,Pennec X,et al.Geometric means in a novel vector space structure on symmetric positive-definite matrices[J].SIAM Journal on Matrix Analysis and Applications,2007,29(1):328-347.
[135]Hall S B,LIE G.Lie algebras.and representations:an elementary introduction[M]. New York:Springer,2003.
[136]Scholkopf B,Smola A,Muller K-R.Nonlinear component analysis as a kernel eigen-value problem[J].Neural Computation,1998,10(5):1299-1319.
[137]Cheng Jian, Liu Qingshan,Lu Hanqing,et al.Texture classification using kernel inde-pendent component analysis[C]//Proceedings of the 17th International Conference on Pattern Recognition.Washington,DC,USA:IEEE ComputerComputer Society,2004:620-623.
[138]Mika S,Ratsch G,Weston J,et al.Fisher discriminant analysis with kernels[C]//Pro-ceed ings of the 1999 IEEE Signal Processing Society Workshop on Neural Networks for Signal Processing IX,1999:41-48.
[139]Wright J. Yang A Y,Ganesh A.et al.Robust face recognition via sparse representation [J].Pattern Analysis and Machine Intelligence,IEEE Transactions on,2009,31(2):210-227.
[140]Qiao L,Chen S,Tan X.Sparsity preserving projections with applications to face reco-gnition[J].Pattern Recognition,2010,43(1):331-341.
[141]殷俊,杨万扣.核稀疏保持投影及生物特征识别应用[J].电子学报,2013,41(4)：639-645.
[142]王科俊,阎涛,吕卓纹等.核稀疏保留投影及在步态识别中的应用[J].中国图象图形学报,2013,18(003)：257-263.
[143]赵佳佳,唐峥远,杨杰等.基于图像稀疏表示的红外小目标检测算法[J].红外与毫米波学报.2011.30(2)：156-161.
[144]王燕霞,张弓.基于特征参数稀疏表示的SAR图像目标识别[J].重庆邮电大学学报(自然科学版),2012,24(3)：308-313.
[145]李树涛,魏丹.压缩传感综述[J].自动化学报,2009,35(11)：1369-1377.
[146]戴琼海,付长军,季向阳.压缩感知研究[J].计算机学报,2011,34(3)：425-434.
[147]Tropp J A,Gilbert A C.Signal recovery from random measurements via orthogonal matching pursuit [J]. IEEE Transac-tions on Information Theory,2007,53(12):4655-4665.
[148]Karabulut G Z,Anario D,.Flexible tree search based ortho- gonal matching pursuit algorithm[C]//IEEE International Conference on Acoustics, Speech,and Signal Processing, 2005:673-676.
[149]Deana N,Roman V. Signal recovery from incomplete and inaccurate measure- ments via regularized orthogonal match-ing pursuit[J].IEEE journal of selected topics in signal processing,2010,4(2):310-316.
[150]Dot T,L Gan,Guyen N,Tran T D. Sparsity adaptive matching pursuit algorithm for practical compressed sensing[C]//Asilomar Conference on Signals,Systems and Computers, 2008:581-587.
[151]Ando T.Concavity of certain maps on positive definite matrices and applications to Hadamard products[J].Linear Algebra and its Applications,1979,26:203-241.
[152]Sra S. Positive definite matrices and the symmetric Stein divergence[J]. arXiv preprint arXiv:1110.1773,2011.
[153]Berg C, Christensen J P R, Ressel P.Harmonic analysis on semigroups[M].Springer, 1984.