先进电子制造生产线机器视觉检测方法与技术研究
|
摘要
先进电子制造是一项基础性和战略性产业,对于电子信息产业的发展至关重要。作为工业自动化与智能化的核心技术领域,先进电子制造生产线的自动视觉检测与识别是一个不断发展的新的研究领域和研究热点,具有较好的理论意义和较大的应用前景。作为一个新兴的技术领域,机器视觉检测与识别在国际上处于初级发展阶段,在我国也处于起步阶段。研究先进电子制造生产线的机器视觉检测理论方法和先进技术,对于提高我国的电子制造业自动化、智能化水平至关重要。
论文首先介绍了电子制造中的主要相关工艺和电子产品,结合国内外的机器视觉检测研究现状,从中提炼出拟待解决的关键性科学技术问题。随后,介绍了先进电子制造机器视觉检测中的关键硬件系统和软件系统。
针对先进电子制造中的机器视觉检测难题,本论文结合国家自然科学基金重点项目——“高速精密制造生产线的视觉检测与智能优化控制技术研究”,重点开展了先进电子制造业中的视觉检测理论方法和关键技术的研究,主要包括以下几个方面的工作。
1、研究了一种用于电子元件视觉检测的改进小波变换图像去噪方法,在简要介绍常规图像去噪方法的基础上,本文提出了一种基于四树复小波包变换的混合统计模型图像噪声抑制新方法,把含噪图像分解成低频逼近子图和若干高频方向子图,只需对高频方向子图进行噪声抑制。利用复系数层间相关性的强弱把高频方向子图分为主要类和次要类,分别采用相应的统计模型处理方法,取得了更佳的效果。
2、研究了基于机器视觉的PCB质量智能检测算法,并提出了一种基于神经网络的PCB质量智能检测算法和一种基于并行混沌优化算法的PCB元件检测算法。在PCB质量检测中,根据焊点正常和故障的不同的图像特征,选取合适的特征参数作为神经网络的输入量,以此建立神经网络分类器,可以检测PCB焊点的质量。PCB元件检测算法中,首先选取合适的模板,以此模板与搜索子图的匹配度来识别元件类型,采用并行混沌优化算法来搜索最佳的匹配度,以此识别元件。
3、提出了一种先进电子制造生产线贴片机的智能视觉定位方法,首先采用小波变换来提取电子元件图像特征,再采用RBF神经网络模式匹配算法,以此实现贴片机的视觉准确定位。
4、提出了一种基于多结构元多尺度形态学的电子元件边缘检测方法。首先,采用一种形态边缘检测算子,利用该检测算子进行电子元件图像边缘提取,然后再利用多结构元多尺度的形态结构元素进行边缘信息的调整。此外,还研究了芯片外观参数的检测方法,利用哈夫变换方法从提取出的边缘像素信息计算得到芯片的具体外观参数,并以实验来说明了该算法的效果。
5、针对电子材料(磁环)的质量检测,提出了一种基于灰度直方图和支持向量机的磁环质量智能检测方法,用低维的灰度信息来描述磁环的特征,将图像从背景分离之后,进行灰度直方图处理来提取灰度特征。接着采用主分量分析法,将灰度统计信息由高维向量降低到低维向量。随后,以低维向量为输入,用支持向量机进行分类,以此实现磁环质量的智能检测。
论文最后总结了全文的主要创新性研究成果,对下一步研究工作进行了展望。
Advanced electronic manufacturing is a fundamental and strategic industry for national economy development. As a core technology in industrial automation and intelligent, advanced electronics manufacturing vision automatic detection and recognition is a sunrise industry with broad prospects, and it carries a huge market potential filled with unlimited business opportunities. As an emerging area of technology, machine vision detection and recognition in the international arena is still in the initial stages of development, and it is still in the land reclamation seeding in our country. Study of advanced machine vision detection theory and methods and advanced technology is very essential for improving China's electronics manufacturing automation and intelligence level.
This dissertation first introduces the related processes and products in electronics manufacturing, considers domestic and international Machine Vision Detection of the status quo to extract the scientific and technical issues to be solved. Subsequently, this dissertation introduces the key hardware and software systems of advanced electronics manufacturing.
Focused on the visual inspection problems in advanced electronic manufacturing and combined with the National Natural Science Foundation of China-"High-speed precision manufacturing production line optimization of visual inspection and intelligent control technology research", this dissertation has studied advanced electronic manufacturing method of visual detection theory and key technology research, and it mainly including the following aspects of the work.
(1) A image denoising method is proposed for visual inspection of the advanced electronics manufacturing. After a brief introduction of conventional image denoising method, this part presents a four tree complex wavelet packet transform approach based on a mixed statistical model of image noise suppression new method. In the proposed method, the noisy image is decomposed into a low-frequency approximation sub-images and a number of high-frequency direction of sub-graph, only the direction of sub-maps of high-frequency noise suppression. The use of complex correlation coefficient between layers the direction of the strength of the high-frequency sub-images are divided into major categories and secondary categories, respectively, and the corresponding statistical model approach has achieved better results.
(2) In order to study the quality PCB machine vision-based intelligent detection algorithms, this part presents a neural network based intelligence PCB quality detection algorithm, and a parallel chaos optimization algorithm based PCB component detection algorithm. In the PCB quality control, this method selects the appropriate characteristic parameters as neural network input according to the normal and failure of solder joints of different image features, thus the establishment of neural network classifiers can detect PCB solder joint quality. In the PCB component detection algorithm, it first selects the appropriate template, as templates and search sub-graph matching to identify the component type of parallel chaotic optimization algorithm to search the best matching so as to identify the component.
(3) An intelligent mounter visual positioning method is presented for the advanced electronic manufacturing line. In this method, first wavelet transform is used to extract the image characteristics of electronic components, and then RBF neural network pattern matching algorithm is used to achieve accurate positioning placement machine vision.
(4) A chip-pin edge detection method is proposed based on multi-structuring elements and multi-scale morphological. First, the part presents a morphological edge detection operator, using the detection operator for image edge extraction, and then re-uses of multi-structuring elements morphological structure of multi-scale edge information elements adjustments. In addition, study of a chip based on Hough transform to extract the appearance parameter method, using Hough transform has been extracted from the edge pixel information to seek the specific appearance of the chip parameters.
(5) For electronic materials (magnetic ring) quality testing, a magnetic ring quality intelligent detection method is proposed based on gray-scale histogram and support vector machine. This part uses low-dimensional gray scale information to describe the characteristics of magnetic ring, and the image after separation from the background to carry out processing to extract the gray histogram feature. Then it uses principal component analysis, statistical information will be gray by the high-dimensional vector reduced to low-dimensional vector. Subsequently, a low-dimensional vector as input, using support vector machines for classification in order to achieve the quality of intelligence testing magnetic ring.
At last, a major innovative research result of this research work is concluded and it carried out on the next prospect.
论文首先介绍了电子制造中的主要相关工艺和电子产品,结合国内外的机器视觉检测研究现状,从中提炼出拟待解决的关键性科学技术问题。随后,介绍了先进电子制造机器视觉检测中的关键硬件系统和软件系统。
针对先进电子制造中的机器视觉检测难题,本论文结合国家自然科学基金重点项目——“高速精密制造生产线的视觉检测与智能优化控制技术研究”,重点开展了先进电子制造业中的视觉检测理论方法和关键技术的研究,主要包括以下几个方面的工作。
1、研究了一种用于电子元件视觉检测的改进小波变换图像去噪方法,在简要介绍常规图像去噪方法的基础上,本文提出了一种基于四树复小波包变换的混合统计模型图像噪声抑制新方法,把含噪图像分解成低频逼近子图和若干高频方向子图,只需对高频方向子图进行噪声抑制。利用复系数层间相关性的强弱把高频方向子图分为主要类和次要类,分别采用相应的统计模型处理方法,取得了更佳的效果。
2、研究了基于机器视觉的PCB质量智能检测算法,并提出了一种基于神经网络的PCB质量智能检测算法和一种基于并行混沌优化算法的PCB元件检测算法。在PCB质量检测中,根据焊点正常和故障的不同的图像特征,选取合适的特征参数作为神经网络的输入量,以此建立神经网络分类器,可以检测PCB焊点的质量。PCB元件检测算法中,首先选取合适的模板,以此模板与搜索子图的匹配度来识别元件类型,采用并行混沌优化算法来搜索最佳的匹配度,以此识别元件。
3、提出了一种先进电子制造生产线贴片机的智能视觉定位方法,首先采用小波变换来提取电子元件图像特征,再采用RBF神经网络模式匹配算法,以此实现贴片机的视觉准确定位。
4、提出了一种基于多结构元多尺度形态学的电子元件边缘检测方法。首先,采用一种形态边缘检测算子,利用该检测算子进行电子元件图像边缘提取,然后再利用多结构元多尺度的形态结构元素进行边缘信息的调整。此外,还研究了芯片外观参数的检测方法,利用哈夫变换方法从提取出的边缘像素信息计算得到芯片的具体外观参数,并以实验来说明了该算法的效果。
5、针对电子材料(磁环)的质量检测,提出了一种基于灰度直方图和支持向量机的磁环质量智能检测方法,用低维的灰度信息来描述磁环的特征,将图像从背景分离之后,进行灰度直方图处理来提取灰度特征。接着采用主分量分析法,将灰度统计信息由高维向量降低到低维向量。随后,以低维向量为输入,用支持向量机进行分类,以此实现磁环质量的智能检测。
论文最后总结了全文的主要创新性研究成果,对下一步研究工作进行了展望。
Advanced electronic manufacturing is a fundamental and strategic industry for national economy development. As a core technology in industrial automation and intelligent, advanced electronics manufacturing vision automatic detection and recognition is a sunrise industry with broad prospects, and it carries a huge market potential filled with unlimited business opportunities. As an emerging area of technology, machine vision detection and recognition in the international arena is still in the initial stages of development, and it is still in the land reclamation seeding in our country. Study of advanced machine vision detection theory and methods and advanced technology is very essential for improving China's electronics manufacturing automation and intelligence level.
This dissertation first introduces the related processes and products in electronics manufacturing, considers domestic and international Machine Vision Detection of the status quo to extract the scientific and technical issues to be solved. Subsequently, this dissertation introduces the key hardware and software systems of advanced electronics manufacturing.
Focused on the visual inspection problems in advanced electronic manufacturing and combined with the National Natural Science Foundation of China-"High-speed precision manufacturing production line optimization of visual inspection and intelligent control technology research", this dissertation has studied advanced electronic manufacturing method of visual detection theory and key technology research, and it mainly including the following aspects of the work.
(1) A image denoising method is proposed for visual inspection of the advanced electronics manufacturing. After a brief introduction of conventional image denoising method, this part presents a four tree complex wavelet packet transform approach based on a mixed statistical model of image noise suppression new method. In the proposed method, the noisy image is decomposed into a low-frequency approximation sub-images and a number of high-frequency direction of sub-graph, only the direction of sub-maps of high-frequency noise suppression. The use of complex correlation coefficient between layers the direction of the strength of the high-frequency sub-images are divided into major categories and secondary categories, respectively, and the corresponding statistical model approach has achieved better results.
(2) In order to study the quality PCB machine vision-based intelligent detection algorithms, this part presents a neural network based intelligence PCB quality detection algorithm, and a parallel chaos optimization algorithm based PCB component detection algorithm. In the PCB quality control, this method selects the appropriate characteristic parameters as neural network input according to the normal and failure of solder joints of different image features, thus the establishment of neural network classifiers can detect PCB solder joint quality. In the PCB component detection algorithm, it first selects the appropriate template, as templates and search sub-graph matching to identify the component type of parallel chaotic optimization algorithm to search the best matching so as to identify the component.
(3) An intelligent mounter visual positioning method is presented for the advanced electronic manufacturing line. In this method, first wavelet transform is used to extract the image characteristics of electronic components, and then RBF neural network pattern matching algorithm is used to achieve accurate positioning placement machine vision.
(4) A chip-pin edge detection method is proposed based on multi-structuring elements and multi-scale morphological. First, the part presents a morphological edge detection operator, using the detection operator for image edge extraction, and then re-uses of multi-structuring elements morphological structure of multi-scale edge information elements adjustments. In addition, study of a chip based on Hough transform to extract the appearance parameter method, using Hough transform has been extracted from the edge pixel information to seek the specific appearance of the chip parameters.
(5) For electronic materials (magnetic ring) quality testing, a magnetic ring quality intelligent detection method is proposed based on gray-scale histogram and support vector machine. This part uses low-dimensional gray scale information to describe the characteristics of magnetic ring, and the image after separation from the background to carry out processing to extract the gray histogram feature. Then it uses principal component analysis, statistical information will be gray by the high-dimensional vector reduced to low-dimensional vector. Subsequently, a low-dimensional vector as input, using support vector machines for classification in order to achieve the quality of intelligence testing magnetic ring.
At last, a major innovative research result of this research work is concluded and it carried out on the next prospect.
引文
[1]Tu P Y L, Yam R, Tse P, Sun A O. An integrated maintenance management system for an advanced manufacturing company. International Journal of Advanced Manufacturing Technology,2001,17(9):692-703
[2]Mikkelson James M, Tomasetta Louis R. High-density GaAs integrated circuit manufacturing. IEEE Transactions on Semiconductor Manufacturing,2003,16(3): 384-389
[3]穆建军.试论半导体领域专利权利要求中的功能性描述.半导体行业,2008,6(3)：21-22
[4]Rogers P, Warrington R. Production planning for surface mount technology lines. International Journal of Production Research,2004,42(13):2693-2718
[5]刘良江,王耀南.灰度直方图和支持向量机在磁环外观检测中的应用.仪器仪表学报,2006,27(8)：840-844
[6]卢盛林,张宪民,邝泳聪.基于神经网络的PCB焊点检测方法.华南理工大学学报(自然科学版),2008,36(5)：135-139
[7]周贤善,罗兵.基于ANN集成的贴片安装机器视觉检测.武汉理工大学学报,2008,30(1)：113-116
[8]段峰,王耀南,刘焕军.基于机器视觉的智能空瓶检测机器人研究.仪器仪表学报,2004,25(5)：624-627
[9]Ley Dietmar. Machine vision in Germany and Europe. Photonics Spectra,2007, 41(1):68-69
[10]Golnabi H, Asadpour A. Design and application of industrial machine vision systems. Robotics and Computer-Integrated Manufacturing,2007,23(6): 630-637
[11]Les Caren B. Machine vision market:Weathering the storm. Photonics Spectra, 2009,43(4):31-32
[12]Wang Jin-Jiang, Wang Zheng, Mo Xu-Tao et al. Machine vision inspection system for film. Sensor Review,2007,27(3):212-216
[13]罗伯特森.PCB设计基础.北京：机械工业出版社,2007,1-5
[14]肖国玲.微电子制造工艺技术.西安：西安电子科技大学出版社,2008
[15]Lee Dah-Jye, Archibald James, Xu Xiaoqian, Zhan Pengcheng. Using distance transform to solve real-time machine vision inspection problems. Machine Vision and Applications,2007,18(2):85-93
[16]Velasquez Juan D, Nof Shimon Y. Integration of machine-vision inspection information for best-matching of distributed components and suppliers. Computers in Industry,2008,59(1):69-81
[17]Cheng Fang, Ying Yi-Bin. Machine vision inspection of rice seed based on Hough transform. Journal of Zhejiang University:Science,2004,5(6):663-667
[18]Peng Xiangqian, Chen Youping, Yu Wenyong, Zhou Zude, Sun Guodong. An online defects inspection method for float glass fabrication based on machine vision. International Journal of Advanced Manufacturing Technology,2008, 39(11-12):1180-1189
[19]Qixin Cao, Zhuang Fu, Nianjiong Xia, Lewis F L. A binocular machine vision system for ball grid array package inspection. Assembly Automation,2005,25(3): 217-222
[20]Lubofsky Evan. Machine vision-Moving beyond the end of the line. Sensors, 2001,18(8):40-43
[21]O Silven, T Westman, etc. A defect analysis method for visual inspection. In Processing of the 8th International Conference on Pattern Recognition, Paris, 1986:868-870
[22]A M Darwish, A K Jain. A rule based approach for visual pattern inspection. IEEE Trans. Pattern Anal. Mach. Intell.,1988,10(1):56-68
[23]R T Chin. Automated visual inspection algorithms. Computer Vision:Theory and Industrial Applications, Spinger-Verlag, New York,1992:377-404
[24]S L Bartlett, P J Besl, etc. Automatic solder joint inspection. IEEE Trans. Pattern Anal. Match. Intell.,1988,10(1):31-34
[25]B H Khalaj, H K Aghajan. Automated direct patterned wafer inspection. In Processing of the IEEE Workshop on Applications of Computer Vision, Palm Springs, CA, November 1992:266-273.
[26]W Daley, T Rao. Color vision for industrial inspection. In Proceedings of SME Vision Conference'90, Detroit,1990:1211-1224.
[27]D W Capson, S K Eng. A tiered-color illumination approach for machine inspection of solder joints. IEEE Trans. Pattern Anal. Mach. Intell.,1988,10(3): 387-392.
[28]X L Xie, G Beni. A validity measure for fuzzy clustering. IEEE Trans. Pattern Anal. Mach. Intell.,1991,13:841-847
[29]T Pryor. Optical inspection and machine vision. In:Proceedings of the IEEE Workshop on Industrial Applications of Machine Vision, Research Triangle Park, NC,1982:3-20
[30]V V Badami, N R Corby, N H Irwin. Using range data for inspection printed wiring boards. In:Proceedings of the IEEE International Conference on Robotics and Automation, Philadelphia,1988:1466-1468
[31]G Kaplan. Industrial electronics. IEEE Spectrum,1994,31(1):74-76
[32]D B Shu, C C Li, J F Mancuso. A line extraction method for automated SEM inspection of VLSI resist. IEEE Trans. Pattern Anal. Mach. Intell.,1998,10(1): 117-120
[33]H Onda, Y Nishinaga, K Ono. Welding defect identification by artificial neural networks. In:Proceedings of the Japan/USA Symposium on Flexible Automation, San Francisco,1992:313-316
[34]Mumford David. Mathematic theories of shape:Do they model perception?. In: Proc. Geometric Methods in Computer Vision, SPIE,1991,1570:2-10
[35]Lisa Gottesfeld Brown. A Survey of Image Registration Techniques. ACM Computing Surveys,1992,24(4):231-254
[36]J B A, Maintz M A Viergever. A survey of medical image registration. Medical Image Analysis,1998,2(1):1-36
[37]Robert Hanek, Thorsten Schmitt, etc. Fast Image-based Object Localization in Natural Scenes. Proceedings of the 2002 IEEE/RSJ Intl. Conference on Intelligent Robots and Systems EPFL, Lausanne, Switzerland,2002
[38]Zhiyong Xie. Image Registration Using Hierarchical B-Splines. Doctoral Dissertation of Arizona State University,2002
[39]丁险峰,吴洪,张洪江等.形状匹配综述.自动化学报,2001,27(5)：678-694.
[40]王俊平,郝跃.IC真实缺陷的边界提取和缺陷尺寸与形状的表征.计算机学报,2000,23(7)：673-678.
[41]Schulze Mark A, Hunt Martin A, Voelkl Edgar, etc. Semiconductor wafer defect detection using digital holography. Processing of SPIE-Process and Materials Characterization and Diagnostics in IC Manufacturing, Santa Clara, CA, United States,2003:183-193.
[42]Kubota Toshiro, Talekar Parag, Sudarshan, etc. IEEE SoutheastCon 2002, Columbia,2002:42-47
[43]Yeh Chi-Hao, Shen Ta-Cheng, Wu Ful-Chiang. A case study:Passive component inspection using a 1D wavelet transform. International Journal of Advanced Manufacturing Technology,2003, (11-12):899-910
[44]Price Jeffery R, Bingham Philip R, Tobin Jr, etc. Estimating cross-section semiconductor structure by comparing top-down SEM images. In:Proceedings of SPIE-Machine Vision Applications in Industrial Inspection XI, Santa Clara, CA, United States,2003,5011:161-170
[45]Hou Tung-Hsu Tony. Automated vision system for IC lead inspection. International Journal of Production Research,2001,39(15):3353-3366
[46]Ye Q Z, Ong S H, Han X. A stereo vision system for the inspection of IC bonding wires. International Journal of Imaging Systems and Technology,2000,11(4): 254-262.
[47]Koshimizu Hiroyasu, Takagi Kazunori, Watanabe Takashi. Visual inspection PC system for quality control of electronic devices. Proceedings of SPIE Optomechatronic Systems Ⅲ, Stuttgart,2002:165-173
[48]Kassim A A, Zhou H, Ranganath S. Automatic IC orientation checks. Machine Vision and Applications,2000,12(3):107-112
[49]Hildebrand Lars, Fathi Madjid. Vision systems for the inspection of resistance welding joints. Proceedings of SPIE-Human Vision and Electronic Imaging, San Jose, CA, USA,2000:314-322
[50]Perng Der-Baau, Chen Yen-Chung. Advanced automated optical inspection system for fishtail collapse of microrouter. Nondestructive Testing and Evaluation,2008,23(4):257-272
[51]Wang C C, Jiang B C. PCB solder joint defects detection and classification using machine vision. International Journal of Industrial Engineering:Theory Applications and Practice,2001,8(4):359-369.
[52]王洪.机器视觉元件辅助贴放技术研究.上海交通大学博士学位论文,2002
[53]Merchant John. The human visual system provides clues to better object recognition. Photonics Spectra,2004,38(6):102-106
[54]Zhongxiang Hu, Lei Zhu, Jiaxu Teng, et al. Evaluation of three-dimensional surface roughness parameters based on digital image processing. International Journal of Advanced Manufacturing Technology,2009,40(3-4):342-348
[55]Shang-Hong Lai. Robust Image Matching under Partial Occlusion and Spatially Varying Illumination Change. Computer Vision and Image Understanding,2000, 78(1):84-98
[56]Shang-Hong Lai, Ming Fang. A Hybrid Image Alignment System for Fast and Precise Pattern Localization. Real-Time Imaging,2002,8(1):23-33
[57]丁汉,朱利民,林忠钦.面向芯片封装的高加速度运动系统的精确定位和操作.自然科学进展,2003,13(6)：568-574
[58]Shih-Hsu Chang, Fang-Hsuan Cheng, Wen-Hsing Hsu, et al. Fast Algorithm for Point Pattern Matching:Invariant to Translations, Rotations and Scale Changes. Pattern Recognition,1997,30(2):311-320
[59]张立华,徐文立.点模式匹配.计算机学报,1999,22(7)：740-745
[60]Zhang Qirong, Li Yuhua, Steele Ben, et al. Comparison of a CMOS-based and a CCD-based digital x-ray imaging system:Observer studies. Journal of Electronic Imaging,2005,14(2):1-6
[61]Luger Siegfried. Technology & Market trends in LED lighting:Overview about recent market and technology developments. VDI Berichte,2008,2059(1):3-11
[62]Dawson Ben. Applying automated optical inspection. Evaluation Engineering, 2005,44(7):62-67
[63]Park Tae-Hyoung, Kim Hwa-Jung, Kim Nam. Path planning of automated optical inspection machines for PCB assembly systems. International Journal of Control, Automation and Systems,2006,4(1):96-104
[64]由春秋.实时采集多幅图像叠加平均去噪的研究与实现.哈尔滨师范大学自然科学学报,2007,23(3)：25-28
[65]Zhang X M, Yin Z P, Xiong Y L. Adaptive switching mean filter using conditional morphological noise detector. Electronics Letters,2008,44(6): 406-408
[66]Ye Wanzhou, Wang Liang, Xu Liguo. Properties of locally convergent sequences with respect to median filter. Discrete Mathematics,2009,309(9):2775-2781
[67]Li Hui-Guang, Li Yan, Sun Chang-Pin, Li Xiao-Li. A new discrete wavelet transform fast algorithm with rational dilation factor. International Journal of Wavelets, Multiresolution and Information Processing,2008,6(4):609-616
[68]Liu Leibo, Zhang Milin, Meng Hongying, Zhang Li, Wang Zhihua. VLSI implementation of lifting-based forward and inverse wavelet transform. Chinese Journal of Electronics,2007,16(3):423-428
[69]Wang Y S. Sound quality estimation for nonstationary vehicle noises based on discrete wavelet transform. Journal of Sound and Vibration, 2009,324(3-5):1124-1140
[70]Nicolier Frederic, Laligant Olivier, Truchetet Frederic. Discrete wavelet transform implementation in fourier domain for multidimensional signal. Journal of Electronic Imaging,2002,11(3):338-346
[71]Jingyu Yang, Yao Wang, Wenli Xu, Qionghai Dai. Image Coding Using Dual-Tree Discrete Wavelet Transform. IEEE Transactions on Image Processing, 2008,17(9):1555-1569
[72]Kingsbury N G. The dual-tree complex wavelet transform:a new technique for shift invariance and directional filters. In:Proceedings of the 8th IEEE Digital Signal Processing Workshop, Bryce Canyon UT, USA,1998,86-89
[73]Do M N, Vetterli M. The finite ridgelet transform for image representation.IEEE Trans.on Image Processing.,2003,12(1):16-28
[74]J L Stack, E J Candes, D L Donoho. The Curvelet transform for image denoising. IEEE Trans.on Image Processing,2002,11(6):670-684
[75]M N Do and M Vetterli. Contourlets:A Directional multiresolution image representation. Proc. of IEEE International Conference on Image Processing (ICIP), Rochester,2002,357-360
[76]E J Candes, L Demanet, D L Donoho, et al. Fast discrete Curvelet transforms. Applied and Computational Mathematics. California Institute of Technology, 2005:1-43
[77]Truong T. Nguyen and Soontorn Oraintara. On the aliasing effect of the Contourlet filter banks. IEEE International Symposium on Circuits and Systems (ISCAS'06), Greece,2006.
[78]JalobeanuA, Blanc-Feraud L, Zerubia J. Satellite Image deblurring using complex wavelet packets. International Journal of Computer Vision,2003,51(3): 205-217
[79]CHANG S G. Adaptive wavelet thresholding for image denoising and compression. IEEE Trans on Image Processing,2000,9(9):1532-1546
[80]DENG Cheng-zhi, CAO Hart-qiang, WANG Sheng-qian. Contourlet based image denoising using non-Gaussian bivariate model. Infrared and Laser Engineering, 2006,35(2):234-237
[81]汪雪林,韩华,彭思龙.基于小波域局部高斯模型的图像复原.软件学报,2004,15(3)：443-449
[82]M Mihcak, I Kozintsev, K Ramchandran. Spalially adaptive statistical modeling of wavelet image coefficients and its application to denoising. IEEE International Conference on Acoustics,Speech and Signal Processing, Phoenix, USA,1999:3253-3256
[83]赵立兴,唐英干,刘冬,关新平.基于直方图指数平滑的模糊散度图像分割.系统工程与电子技术,2005,27(7)：1182-1185
[84]Yi Honglei, Ding Xin. On the optimization of training parameters of BP network for modeling 3D woven composites. Journal of Advanced Materials,2002, 34(3):3-8
[85]飞思科技产品研发中心.MATLAB 6.5辅助神经网络分析与设计.电子工业出版社,2003
[86]Tavazoei M S, Haeri M. An optimization algorithm based on chaotic behavior and fractal nature. Journal of Computational and Applied Mathematics,2007, 206(2):1070-1081
[87]单梁,强浩,李军等.基于Tent映射的混沌优化算法.控制与决策,2005,20(2)：179-182
[88]袁小芳.复杂发电机系统的智能控制理论方法研究.博士学位论文,湖南大学,2008：131-133
[89]Nelder J A, Mead R. A simplex method for function minimization. The Computer Journal,1965,7(4):308-313
[90]李强,张钹.一种基于图像灰度的快速匹配算法.软件学报,2006,17(2)：216-222
[91]王红梅,张科,李言俊.图像匹配研究进展.计算机工程与应用,2004,40(19)：42-46
[92]张志佳,黄莎白,史泽林.新的基于边缘特征的图像相关匹配方法.红外与激光工程,2003,32(6)：635-638
[93]周骥,石教英.图像特征点匹配的强壮算法.计算机辅助设计与图形学学报,2002,14(8)：754-757
[94]Kokshenev Illya, Braga Antonio Padua. A multi-objective approach to RBF network learning. Neurocomputing,2008,71(7-9):1203-1209
[95]Li W, Hori Y. An algorithm for extracting fuzzy rules based on RBF neural network. IEEE Transactions on Industrial Electronics,2006,53(4):1269-1276
[96]张小军,冯宏伟.基于径向基函数神经网络的车型识别技术.西北大学学报(自然科学网络版),2006,4(2)：1-7
[97]He Qinghang, Zhang Zhenxi. A new edge detection algorithm for image corrupted by White-Gaussian noise. International Journal of Electronics and Communications,2007,61(8):546-550
[98]Abbasi T A, Abbasi M U. A novel FPGA-based architecture for Sobel edge detection operator. International Journal of Electronics,2007,94(9):889-896
[99]Hou Z J, Wei G W. A new approach to edge detection. Pattern Recognition, 2002,35(7):1559-1570
[100]Ganguli Ranjan, Dan Budhadipta. Trend shift detection in jet engine gas path measurements using cascaded recursive median filter with gradient and laplacian edge detector. Journal of Engineering for Gas Turbines and Power,2004,126(1): 55-61
[101]Pei Soo-Chang, Horng Ji-Hwei. Design of FIR bilevel Laplacian-of-Gaussian filter. Signal Processing,2002,82(4):677-691
[102]Bao Paul, Zhang Lei, Wu Xiaolin. Canny edge detection enhancement by scale multiplication. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005,27(9):1485-1490
[103]Shih Frank Y. General Sweep Mathematical Morphology. Advances in Imaging and Electron Physics,2006,140:265-306,
[104]Sun Ji-Ping, Wu Bing. Image mathematical morphology and image restoration application in detecting underground bin level. Journal of Coal Science and Engineering,2004,10(2):105-110
[105]Bouaynaya Nidhal, Schonfeld Dan. Theoretical foundations of spatially-variant mathematical morphology part II:Gray-Level images. IEEE Transactions on Pattern Analysis and Machine Intelligence,2008,30(5):837-850
[106]段毅华,王雨点.基于分形和数学形态学的图像边缘检测方法.计算机工程与应用,2004,40(14)：63-64
[107]付永庆,王咏胜.一种基于数学形态学的灰度图像边缘检测算法.哈尔滨工程大学学报,2005,26(5)：685-687
[108]T Chen, Q H Wu, R Rahmani-Torkaman, J Hughes. A pseudo top-hat mathematical morphological approach to edge detection in dark regions. Pattern Recognition,2002,35:199-210
[109]杨述斌,彭复员.噪声污染图象中的广义形态边缘检测器.计算机工程与应用,2002,38(17)：91-92
[110]范立南,韩晓微,王忠石,徐心和.基于多结构元的噪声污染灰度图像边缘检测研究.武汉大学学报(工学版),2003,36(3)：86-90
[111]Shimizu M F, Nakamura G. A thinning algorithm for digital figures of characters. In:Proceedings.4th IEEE Southwest Symposium on Image Analysis and Interpretation,2000:83-87
[112]Song Z, Chen Y Q, Moore K L, Ma L. Applications of the sparse Hough transform for laser data line fitting and segmentation. International Journal of Robotics and Automation,2006,21(3):157-164
[113]Walsh Daniel, Raftery Adrian E. Accurate and efficient curve detection in images:The importance sampling Hough transform. Pattern Recognition,2002, 35(7):1421-1431
[114]姚骥.磁环缺陷图像检测系统的关键技术研究.硕士学位论文,上海大学,2006
[115]Lin Yi, Lee Yoonkyung, Wahba Grace. Support vector machines for classification in nonstandard situations. Machine Learning,2002,46(1-3): 191-202
[116]Horng Shi-Jinn, Fan Pingzhi, Su Ming-Yang, et al. Anomaly detection for web server based on smooth support vector machine. Computer Systems Science and Engineering,2008,23(3):209-218
[117]Chi Hoi-Ming, Ersoy Okan K. Support vector machine decision trees with rare event detection. International Journal of Smart Engineering System Design, 2002,4(4):225-242
[118]Bisgaard Sren, Huang Xuan. Visualizing principal components analysis for multivariate process data. Journal of Quality Technology,2008,40(3):299-309
[119]Voegtlin Thomas. Recursive principal components analysis. Neural Networks, 2005,18(8):1051-1063
[120]Shilton Alistair, Palaniswami M, Ralph Daniel. Incremental training of support vector machines. IEEE Transactions on Neural Networks,2005,16(1):114-131
[121]W J Hu, Q Song. An Accelerated Decomposition Algorithm for Robust Support Vector Machines. IEEE Transactions on Circuits and Systems,2004,51(5): 234-240
[122]V David Sanchez. Advanced support vector machines and kernel methods. Neurocomputing,2003,55 (1):5-20
[123]许建华,张学工,李衍达.支持向量机的新发展.控制与决策,2004,19(5)：481.484
[124]Li Zhang, Weida Zhou, Licheng Jiao, Wavelet Support Vector Machine. IEEE Transactions On Systems, Man, And Cybernetics,2004,34(1):34-39
[125]J A K Suykens, J Vandewalle. Recurrent Least Squares Support Vector Machines. IEEE Transactions on Circuits and Systems,2000,47(7):1109-1114
[126]Chun Fu Lin, Sheng De Wang. Fuzzy Support Vector Machines. IEEE Transactions on Neural Networks,2002,13(2):464-471
[127]Junshui Ma, James Theiler, Simon Perkins. Accurate on-line support vector regression. Neural Computation,2003,15(11):2683-2703
[2]Mikkelson James M, Tomasetta Louis R. High-density GaAs integrated circuit manufacturing. IEEE Transactions on Semiconductor Manufacturing,2003,16(3): 384-389
[3]穆建军.试论半导体领域专利权利要求中的功能性描述.半导体行业,2008,6(3)：21-22
[4]Rogers P, Warrington R. Production planning for surface mount technology lines. International Journal of Production Research,2004,42(13):2693-2718
[5]刘良江,王耀南.灰度直方图和支持向量机在磁环外观检测中的应用.仪器仪表学报,2006,27(8)：840-844
[6]卢盛林,张宪民,邝泳聪.基于神经网络的PCB焊点检测方法.华南理工大学学报(自然科学版),2008,36(5)：135-139
[7]周贤善,罗兵.基于ANN集成的贴片安装机器视觉检测.武汉理工大学学报,2008,30(1)：113-116
[8]段峰,王耀南,刘焕军.基于机器视觉的智能空瓶检测机器人研究.仪器仪表学报,2004,25(5)：624-627
[9]Ley Dietmar. Machine vision in Germany and Europe. Photonics Spectra,2007, 41(1):68-69
[10]Golnabi H, Asadpour A. Design and application of industrial machine vision systems. Robotics and Computer-Integrated Manufacturing,2007,23(6): 630-637
[11]Les Caren B. Machine vision market:Weathering the storm. Photonics Spectra, 2009,43(4):31-32
[12]Wang Jin-Jiang, Wang Zheng, Mo Xu-Tao et al. Machine vision inspection system for film. Sensor Review,2007,27(3):212-216
[13]罗伯特森.PCB设计基础.北京：机械工业出版社,2007,1-5
[14]肖国玲.微电子制造工艺技术.西安：西安电子科技大学出版社,2008
[15]Lee Dah-Jye, Archibald James, Xu Xiaoqian, Zhan Pengcheng. Using distance transform to solve real-time machine vision inspection problems. Machine Vision and Applications,2007,18(2):85-93
[16]Velasquez Juan D, Nof Shimon Y. Integration of machine-vision inspection information for best-matching of distributed components and suppliers. Computers in Industry,2008,59(1):69-81
[17]Cheng Fang, Ying Yi-Bin. Machine vision inspection of rice seed based on Hough transform. Journal of Zhejiang University:Science,2004,5(6):663-667
[18]Peng Xiangqian, Chen Youping, Yu Wenyong, Zhou Zude, Sun Guodong. An online defects inspection method for float glass fabrication based on machine vision. International Journal of Advanced Manufacturing Technology,2008, 39(11-12):1180-1189
[19]Qixin Cao, Zhuang Fu, Nianjiong Xia, Lewis F L. A binocular machine vision system for ball grid array package inspection. Assembly Automation,2005,25(3): 217-222
[20]Lubofsky Evan. Machine vision-Moving beyond the end of the line. Sensors, 2001,18(8):40-43
[21]O Silven, T Westman, etc. A defect analysis method for visual inspection. In Processing of the 8th International Conference on Pattern Recognition, Paris, 1986:868-870
[22]A M Darwish, A K Jain. A rule based approach for visual pattern inspection. IEEE Trans. Pattern Anal. Mach. Intell.,1988,10(1):56-68
[23]R T Chin. Automated visual inspection algorithms. Computer Vision:Theory and Industrial Applications, Spinger-Verlag, New York,1992:377-404
[24]S L Bartlett, P J Besl, etc. Automatic solder joint inspection. IEEE Trans. Pattern Anal. Match. Intell.,1988,10(1):31-34
[25]B H Khalaj, H K Aghajan. Automated direct patterned wafer inspection. In Processing of the IEEE Workshop on Applications of Computer Vision, Palm Springs, CA, November 1992:266-273.
[26]W Daley, T Rao. Color vision for industrial inspection. In Proceedings of SME Vision Conference'90, Detroit,1990:1211-1224.
[27]D W Capson, S K Eng. A tiered-color illumination approach for machine inspection of solder joints. IEEE Trans. Pattern Anal. Mach. Intell.,1988,10(3): 387-392.
[28]X L Xie, G Beni. A validity measure for fuzzy clustering. IEEE Trans. Pattern Anal. Mach. Intell.,1991,13:841-847
[29]T Pryor. Optical inspection and machine vision. In:Proceedings of the IEEE Workshop on Industrial Applications of Machine Vision, Research Triangle Park, NC,1982:3-20
[30]V V Badami, N R Corby, N H Irwin. Using range data for inspection printed wiring boards. In:Proceedings of the IEEE International Conference on Robotics and Automation, Philadelphia,1988:1466-1468
[31]G Kaplan. Industrial electronics. IEEE Spectrum,1994,31(1):74-76
[32]D B Shu, C C Li, J F Mancuso. A line extraction method for automated SEM inspection of VLSI resist. IEEE Trans. Pattern Anal. Mach. Intell.,1998,10(1): 117-120
[33]H Onda, Y Nishinaga, K Ono. Welding defect identification by artificial neural networks. In:Proceedings of the Japan/USA Symposium on Flexible Automation, San Francisco,1992:313-316
[34]Mumford David. Mathematic theories of shape:Do they model perception?. In: Proc. Geometric Methods in Computer Vision, SPIE,1991,1570:2-10
[35]Lisa Gottesfeld Brown. A Survey of Image Registration Techniques. ACM Computing Surveys,1992,24(4):231-254
[36]J B A, Maintz M A Viergever. A survey of medical image registration. Medical Image Analysis,1998,2(1):1-36
[37]Robert Hanek, Thorsten Schmitt, etc. Fast Image-based Object Localization in Natural Scenes. Proceedings of the 2002 IEEE/RSJ Intl. Conference on Intelligent Robots and Systems EPFL, Lausanne, Switzerland,2002
[38]Zhiyong Xie. Image Registration Using Hierarchical B-Splines. Doctoral Dissertation of Arizona State University,2002
[39]丁险峰,吴洪,张洪江等.形状匹配综述.自动化学报,2001,27(5)：678-694.
[40]王俊平,郝跃.IC真实缺陷的边界提取和缺陷尺寸与形状的表征.计算机学报,2000,23(7)：673-678.
[41]Schulze Mark A, Hunt Martin A, Voelkl Edgar, etc. Semiconductor wafer defect detection using digital holography. Processing of SPIE-Process and Materials Characterization and Diagnostics in IC Manufacturing, Santa Clara, CA, United States,2003:183-193.
[42]Kubota Toshiro, Talekar Parag, Sudarshan, etc. IEEE SoutheastCon 2002, Columbia,2002:42-47
[43]Yeh Chi-Hao, Shen Ta-Cheng, Wu Ful-Chiang. A case study:Passive component inspection using a 1D wavelet transform. International Journal of Advanced Manufacturing Technology,2003, (11-12):899-910
[44]Price Jeffery R, Bingham Philip R, Tobin Jr, etc. Estimating cross-section semiconductor structure by comparing top-down SEM images. In:Proceedings of SPIE-Machine Vision Applications in Industrial Inspection XI, Santa Clara, CA, United States,2003,5011:161-170
[45]Hou Tung-Hsu Tony. Automated vision system for IC lead inspection. International Journal of Production Research,2001,39(15):3353-3366
[46]Ye Q Z, Ong S H, Han X. A stereo vision system for the inspection of IC bonding wires. International Journal of Imaging Systems and Technology,2000,11(4): 254-262.
[47]Koshimizu Hiroyasu, Takagi Kazunori, Watanabe Takashi. Visual inspection PC system for quality control of electronic devices. Proceedings of SPIE Optomechatronic Systems Ⅲ, Stuttgart,2002:165-173
[48]Kassim A A, Zhou H, Ranganath S. Automatic IC orientation checks. Machine Vision and Applications,2000,12(3):107-112
[49]Hildebrand Lars, Fathi Madjid. Vision systems for the inspection of resistance welding joints. Proceedings of SPIE-Human Vision and Electronic Imaging, San Jose, CA, USA,2000:314-322
[50]Perng Der-Baau, Chen Yen-Chung. Advanced automated optical inspection system for fishtail collapse of microrouter. Nondestructive Testing and Evaluation,2008,23(4):257-272
[51]Wang C C, Jiang B C. PCB solder joint defects detection and classification using machine vision. International Journal of Industrial Engineering:Theory Applications and Practice,2001,8(4):359-369.
[52]王洪.机器视觉元件辅助贴放技术研究.上海交通大学博士学位论文,2002
[53]Merchant John. The human visual system provides clues to better object recognition. Photonics Spectra,2004,38(6):102-106
[54]Zhongxiang Hu, Lei Zhu, Jiaxu Teng, et al. Evaluation of three-dimensional surface roughness parameters based on digital image processing. International Journal of Advanced Manufacturing Technology,2009,40(3-4):342-348
[55]Shang-Hong Lai. Robust Image Matching under Partial Occlusion and Spatially Varying Illumination Change. Computer Vision and Image Understanding,2000, 78(1):84-98
[56]Shang-Hong Lai, Ming Fang. A Hybrid Image Alignment System for Fast and Precise Pattern Localization. Real-Time Imaging,2002,8(1):23-33
[57]丁汉,朱利民,林忠钦.面向芯片封装的高加速度运动系统的精确定位和操作.自然科学进展,2003,13(6)：568-574
[58]Shih-Hsu Chang, Fang-Hsuan Cheng, Wen-Hsing Hsu, et al. Fast Algorithm for Point Pattern Matching:Invariant to Translations, Rotations and Scale Changes. Pattern Recognition,1997,30(2):311-320
[59]张立华,徐文立.点模式匹配.计算机学报,1999,22(7)：740-745
[60]Zhang Qirong, Li Yuhua, Steele Ben, et al. Comparison of a CMOS-based and a CCD-based digital x-ray imaging system:Observer studies. Journal of Electronic Imaging,2005,14(2):1-6
[61]Luger Siegfried. Technology & Market trends in LED lighting:Overview about recent market and technology developments. VDI Berichte,2008,2059(1):3-11
[62]Dawson Ben. Applying automated optical inspection. Evaluation Engineering, 2005,44(7):62-67
[63]Park Tae-Hyoung, Kim Hwa-Jung, Kim Nam. Path planning of automated optical inspection machines for PCB assembly systems. International Journal of Control, Automation and Systems,2006,4(1):96-104
[64]由春秋.实时采集多幅图像叠加平均去噪的研究与实现.哈尔滨师范大学自然科学学报,2007,23(3)：25-28
[65]Zhang X M, Yin Z P, Xiong Y L. Adaptive switching mean filter using conditional morphological noise detector. Electronics Letters,2008,44(6): 406-408
[66]Ye Wanzhou, Wang Liang, Xu Liguo. Properties of locally convergent sequences with respect to median filter. Discrete Mathematics,2009,309(9):2775-2781
[67]Li Hui-Guang, Li Yan, Sun Chang-Pin, Li Xiao-Li. A new discrete wavelet transform fast algorithm with rational dilation factor. International Journal of Wavelets, Multiresolution and Information Processing,2008,6(4):609-616
[68]Liu Leibo, Zhang Milin, Meng Hongying, Zhang Li, Wang Zhihua. VLSI implementation of lifting-based forward and inverse wavelet transform. Chinese Journal of Electronics,2007,16(3):423-428
[69]Wang Y S. Sound quality estimation for nonstationary vehicle noises based on discrete wavelet transform. Journal of Sound and Vibration, 2009,324(3-5):1124-1140
[70]Nicolier Frederic, Laligant Olivier, Truchetet Frederic. Discrete wavelet transform implementation in fourier domain for multidimensional signal. Journal of Electronic Imaging,2002,11(3):338-346
[71]Jingyu Yang, Yao Wang, Wenli Xu, Qionghai Dai. Image Coding Using Dual-Tree Discrete Wavelet Transform. IEEE Transactions on Image Processing, 2008,17(9):1555-1569
[72]Kingsbury N G. The dual-tree complex wavelet transform:a new technique for shift invariance and directional filters. In:Proceedings of the 8th IEEE Digital Signal Processing Workshop, Bryce Canyon UT, USA,1998,86-89
[73]Do M N, Vetterli M. The finite ridgelet transform for image representation.IEEE Trans.on Image Processing.,2003,12(1):16-28
[74]J L Stack, E J Candes, D L Donoho. The Curvelet transform for image denoising. IEEE Trans.on Image Processing,2002,11(6):670-684
[75]M N Do and M Vetterli. Contourlets:A Directional multiresolution image representation. Proc. of IEEE International Conference on Image Processing (ICIP), Rochester,2002,357-360
[76]E J Candes, L Demanet, D L Donoho, et al. Fast discrete Curvelet transforms. Applied and Computational Mathematics. California Institute of Technology, 2005:1-43
[77]Truong T. Nguyen and Soontorn Oraintara. On the aliasing effect of the Contourlet filter banks. IEEE International Symposium on Circuits and Systems (ISCAS'06), Greece,2006.
[78]JalobeanuA, Blanc-Feraud L, Zerubia J. Satellite Image deblurring using complex wavelet packets. International Journal of Computer Vision,2003,51(3): 205-217
[79]CHANG S G. Adaptive wavelet thresholding for image denoising and compression. IEEE Trans on Image Processing,2000,9(9):1532-1546
[80]DENG Cheng-zhi, CAO Hart-qiang, WANG Sheng-qian. Contourlet based image denoising using non-Gaussian bivariate model. Infrared and Laser Engineering, 2006,35(2):234-237
[81]汪雪林,韩华,彭思龙.基于小波域局部高斯模型的图像复原.软件学报,2004,15(3)：443-449
[82]M Mihcak, I Kozintsev, K Ramchandran. Spalially adaptive statistical modeling of wavelet image coefficients and its application to denoising. IEEE International Conference on Acoustics,Speech and Signal Processing, Phoenix, USA,1999:3253-3256
[83]赵立兴,唐英干,刘冬,关新平.基于直方图指数平滑的模糊散度图像分割.系统工程与电子技术,2005,27(7)：1182-1185
[84]Yi Honglei, Ding Xin. On the optimization of training parameters of BP network for modeling 3D woven composites. Journal of Advanced Materials,2002, 34(3):3-8
[85]飞思科技产品研发中心.MATLAB 6.5辅助神经网络分析与设计.电子工业出版社,2003
[86]Tavazoei M S, Haeri M. An optimization algorithm based on chaotic behavior and fractal nature. Journal of Computational and Applied Mathematics,2007, 206(2):1070-1081
[87]单梁,强浩,李军等.基于Tent映射的混沌优化算法.控制与决策,2005,20(2)：179-182
[88]袁小芳.复杂发电机系统的智能控制理论方法研究.博士学位论文,湖南大学,2008：131-133
[89]Nelder J A, Mead R. A simplex method for function minimization. The Computer Journal,1965,7(4):308-313
[90]李强,张钹.一种基于图像灰度的快速匹配算法.软件学报,2006,17(2)：216-222
[91]王红梅,张科,李言俊.图像匹配研究进展.计算机工程与应用,2004,40(19)：42-46
[92]张志佳,黄莎白,史泽林.新的基于边缘特征的图像相关匹配方法.红外与激光工程,2003,32(6)：635-638
[93]周骥,石教英.图像特征点匹配的强壮算法.计算机辅助设计与图形学学报,2002,14(8)：754-757
[94]Kokshenev Illya, Braga Antonio Padua. A multi-objective approach to RBF network learning. Neurocomputing,2008,71(7-9):1203-1209
[95]Li W, Hori Y. An algorithm for extracting fuzzy rules based on RBF neural network. IEEE Transactions on Industrial Electronics,2006,53(4):1269-1276
[96]张小军,冯宏伟.基于径向基函数神经网络的车型识别技术.西北大学学报(自然科学网络版),2006,4(2)：1-7
[97]He Qinghang, Zhang Zhenxi. A new edge detection algorithm for image corrupted by White-Gaussian noise. International Journal of Electronics and Communications,2007,61(8):546-550
[98]Abbasi T A, Abbasi M U. A novel FPGA-based architecture for Sobel edge detection operator. International Journal of Electronics,2007,94(9):889-896
[99]Hou Z J, Wei G W. A new approach to edge detection. Pattern Recognition, 2002,35(7):1559-1570
[100]Ganguli Ranjan, Dan Budhadipta. Trend shift detection in jet engine gas path measurements using cascaded recursive median filter with gradient and laplacian edge detector. Journal of Engineering for Gas Turbines and Power,2004,126(1): 55-61
[101]Pei Soo-Chang, Horng Ji-Hwei. Design of FIR bilevel Laplacian-of-Gaussian filter. Signal Processing,2002,82(4):677-691
[102]Bao Paul, Zhang Lei, Wu Xiaolin. Canny edge detection enhancement by scale multiplication. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005,27(9):1485-1490
[103]Shih Frank Y. General Sweep Mathematical Morphology. Advances in Imaging and Electron Physics,2006,140:265-306,
[104]Sun Ji-Ping, Wu Bing. Image mathematical morphology and image restoration application in detecting underground bin level. Journal of Coal Science and Engineering,2004,10(2):105-110
[105]Bouaynaya Nidhal, Schonfeld Dan. Theoretical foundations of spatially-variant mathematical morphology part II:Gray-Level images. IEEE Transactions on Pattern Analysis and Machine Intelligence,2008,30(5):837-850
[106]段毅华,王雨点.基于分形和数学形态学的图像边缘检测方法.计算机工程与应用,2004,40(14)：63-64
[107]付永庆,王咏胜.一种基于数学形态学的灰度图像边缘检测算法.哈尔滨工程大学学报,2005,26(5)：685-687
[108]T Chen, Q H Wu, R Rahmani-Torkaman, J Hughes. A pseudo top-hat mathematical morphological approach to edge detection in dark regions. Pattern Recognition,2002,35:199-210
[109]杨述斌,彭复员.噪声污染图象中的广义形态边缘检测器.计算机工程与应用,2002,38(17)：91-92
[110]范立南,韩晓微,王忠石,徐心和.基于多结构元的噪声污染灰度图像边缘检测研究.武汉大学学报(工学版),2003,36(3)：86-90
[111]Shimizu M F, Nakamura G. A thinning algorithm for digital figures of characters. In:Proceedings.4th IEEE Southwest Symposium on Image Analysis and Interpretation,2000:83-87
[112]Song Z, Chen Y Q, Moore K L, Ma L. Applications of the sparse Hough transform for laser data line fitting and segmentation. International Journal of Robotics and Automation,2006,21(3):157-164
[113]Walsh Daniel, Raftery Adrian E. Accurate and efficient curve detection in images:The importance sampling Hough transform. Pattern Recognition,2002, 35(7):1421-1431
[114]姚骥.磁环缺陷图像检测系统的关键技术研究.硕士学位论文,上海大学,2006
[115]Lin Yi, Lee Yoonkyung, Wahba Grace. Support vector machines for classification in nonstandard situations. Machine Learning,2002,46(1-3): 191-202
[116]Horng Shi-Jinn, Fan Pingzhi, Su Ming-Yang, et al. Anomaly detection for web server based on smooth support vector machine. Computer Systems Science and Engineering,2008,23(3):209-218
[117]Chi Hoi-Ming, Ersoy Okan K. Support vector machine decision trees with rare event detection. International Journal of Smart Engineering System Design, 2002,4(4):225-242
[118]Bisgaard Sren, Huang Xuan. Visualizing principal components analysis for multivariate process data. Journal of Quality Technology,2008,40(3):299-309
[119]Voegtlin Thomas. Recursive principal components analysis. Neural Networks, 2005,18(8):1051-1063
[120]Shilton Alistair, Palaniswami M, Ralph Daniel. Incremental training of support vector machines. IEEE Transactions on Neural Networks,2005,16(1):114-131
[121]W J Hu, Q Song. An Accelerated Decomposition Algorithm for Robust Support Vector Machines. IEEE Transactions on Circuits and Systems,2004,51(5): 234-240
[122]V David Sanchez. Advanced support vector machines and kernel methods. Neurocomputing,2003,55 (1):5-20
[123]许建华,张学工,李衍达.支持向量机的新发展.控制与决策,2004,19(5)：481.484
[124]Li Zhang, Weida Zhou, Licheng Jiao, Wavelet Support Vector Machine. IEEE Transactions On Systems, Man, And Cybernetics,2004,34(1):34-39
[125]J A K Suykens, J Vandewalle. Recurrent Least Squares Support Vector Machines. IEEE Transactions on Circuits and Systems,2000,47(7):1109-1114
[126]Chun Fu Lin, Sheng De Wang. Fuzzy Support Vector Machines. IEEE Transactions on Neural Networks,2002,13(2):464-471
[127]Junshui Ma, James Theiler, Simon Perkins. Accurate on-line support vector regression. Neural Computation,2003,15(11):2683-2703