基于计算机视觉的自动光学检测关键技术与应用研究
|
摘要
随着近年来制造工业在我国的迅速发展,对于产品质量的要求也在不断提高,相应的对于产品质量检测技术的速度和检测准确率也提出了更高的要求,当前的人工检测方法已经不能满足工业生产需要。而计算机视觉技术的发展使得自动光学检测技术在工业质量检测的各个领域都得到了广泛的应用,包括半导体工业、电子电机工业、机械零件加工业、食品加工与包装业、汽车工业、金属加工业、印刷业、纺织皮革工业、农林产业等。但是国内科研机构在这方面的研究才刚刚起步,目前的技术在解决高速、高准确度以及缺陷分类的应用方面仍然存在不足。本文从自动光学检测相关的几个关键技术入手,在分析研究现有技术优缺点的基础上,针对工业生产的实际需求,对多种关键技术中的核心算法进行了研究与重新设计。并以晶圆缺陷检测为例,对自动光学检测设备的硬件结构和软件算法进行了研究,设计了一种具有快速、准确、具备缺陷分类能力等特点的晶圆缺陷检测设备。
论文首先对自动光学检测中的图像质量进行了分析,对于成像质量最为相关的图像采集系统的各个组件性能进行了研究。在分析和总结了常用光源对于被测物体作用原理的基础上,提出了根据被测物体自身光学特性进行照明方案设计的原则。以大功率LED晶圆缺陷检测为例,分析了物体表面反光特性存在巨大反差下的成像特点,提出了一种基于明暗场同时照明的光源设计方案,解决了摄像机动态范围不足的问题。
论文对图像分割算法进行了深入的研究,针对当前阈值分割、模板匹配等算法中存在的运行效率较低,容易受到外界环境变化干扰等问题,提出了一种新的基于压缩感知的自学习目标区域分割算法,解决了传统算法存在的问题。并根据晶圆缺陷检测的实际应用需求,设计了结合压缩感知分割算法和模板匹配算法的芯片分割方法,克服了基于压缩感知的目标区域分割算法只能对单一目标进行识别的缺点,在保证检测效率的前提下兼顾了定位的准确性。
特征提取与缺陷分类是自动光学检测技术中重要的一环。对于晶圆缺陷特征的提取,传统的基于灰度值统计特性的特征提取算法能够提供的特征量较少、分类能力较低、且容易受到光照、噪声等因素的干扰。本文设计了一种改进型的基于相位一致性的线检测算法,提高了该算法的运行效率和检测精度。并在此基础上将该检测方法扩展到二维空间中实现了基于相位一致性的角点检测。此外本文针对传统的基于角点检测的特征提取算法只能实现定位功能,而无法直接对角点的形貌特性进行描述的缺点,将尺度空间原理与Harris角点检测算法相结合,提出了一种新的利用尺度空间信息对缺陷形状特征进行描述的方法。在使用BP神经网络分类器进行的分类实验中,以上算法体现了良好的分类准确度和抗干扰性。
最后将本文的理论研究成果应用于实际的工业化设备开发中,研制了一种基于计算机视觉的晶圆缺陷自动检测系统并完成样机制作。使用该样机对文中提出的理论算法进行了实验验证和对比分析,结果显示这些算法都具有较好的实用价值。使用工业生产中的晶圆样片对样机进行测试,结果显示该系统完成单片晶圆检测时间小于12分钟,漏检率为O%,误检率小于10%,完全可以达到工业应用的要求。
Automated optical inspection (AOI) system has become more and more important in the field of wafer production. In order to be competitive in the semiconductor manufacturing industry, defect inspection becomes a critical issue. The goal of wafer defect inspection is to detect defective dies and discard them. However, these existing methods have limitations when dealing with the high speed and high accuracy applications. In this paper, the previous research works and literatures of the key algorithms in automated optical inspection. Some novel algorithms with high speed, high accuracy are proposed and the system of defect inspection is designed aims the existing method's shortages.
This dissertation firstly analyzed the image used in an automated optical inspection system. The image acquirement system which is the most relevant factors to the image quality is analyzed. A novel bright-dark-field light source design is proposed based on the research of the previous light source design to solve the insufficient of camera dynamic range.
The algorithms for image segmentation have been studied in details. The existing thresholding, geometric primitives and template matching methods have been summarized and compared. A novel image segmentation algorithm based on compressed sensing principle is proposed. This algorithm is efficient and robust. Then this algorithm is used in a wafer defect inspection system with template matching algorithm to get an efficient and accurate inspection result.
The feature extraction and defect classification are the key technique of the automated optical inspection. For feature extraction of wafer defect, the traditional methods are low efficiency, poor classification, and sensitive to lighting and noise. In this paper, the line detection algorithm based on phase congruency is improved to get high efficiency and accuracy. This novel algorithm can be also used in corner detection. Then a new feature extraction algorithm based on scale-space principle and Harris corner detector is proposed. This algorithm has accuracy classification results in back-propagation neural network test.
Finally, an automated wafer defect inspection system based on computer vision was presented. The algorithms and methods in this paper were verified and compared using this system. The experiment results have shown that the proposed algorithms and methods can inspect the wafer defect with high speed and accuracy. The inspection time of one wafer is12minutes, the missing rate is0%and the false rate is less than10%.
论文首先对自动光学检测中的图像质量进行了分析,对于成像质量最为相关的图像采集系统的各个组件性能进行了研究。在分析和总结了常用光源对于被测物体作用原理的基础上,提出了根据被测物体自身光学特性进行照明方案设计的原则。以大功率LED晶圆缺陷检测为例,分析了物体表面反光特性存在巨大反差下的成像特点,提出了一种基于明暗场同时照明的光源设计方案,解决了摄像机动态范围不足的问题。
论文对图像分割算法进行了深入的研究,针对当前阈值分割、模板匹配等算法中存在的运行效率较低,容易受到外界环境变化干扰等问题,提出了一种新的基于压缩感知的自学习目标区域分割算法,解决了传统算法存在的问题。并根据晶圆缺陷检测的实际应用需求,设计了结合压缩感知分割算法和模板匹配算法的芯片分割方法,克服了基于压缩感知的目标区域分割算法只能对单一目标进行识别的缺点,在保证检测效率的前提下兼顾了定位的准确性。
特征提取与缺陷分类是自动光学检测技术中重要的一环。对于晶圆缺陷特征的提取,传统的基于灰度值统计特性的特征提取算法能够提供的特征量较少、分类能力较低、且容易受到光照、噪声等因素的干扰。本文设计了一种改进型的基于相位一致性的线检测算法,提高了该算法的运行效率和检测精度。并在此基础上将该检测方法扩展到二维空间中实现了基于相位一致性的角点检测。此外本文针对传统的基于角点检测的特征提取算法只能实现定位功能,而无法直接对角点的形貌特性进行描述的缺点,将尺度空间原理与Harris角点检测算法相结合,提出了一种新的利用尺度空间信息对缺陷形状特征进行描述的方法。在使用BP神经网络分类器进行的分类实验中,以上算法体现了良好的分类准确度和抗干扰性。
最后将本文的理论研究成果应用于实际的工业化设备开发中,研制了一种基于计算机视觉的晶圆缺陷自动检测系统并完成样机制作。使用该样机对文中提出的理论算法进行了实验验证和对比分析,结果显示这些算法都具有较好的实用价值。使用工业生产中的晶圆样片对样机进行测试,结果显示该系统完成单片晶圆检测时间小于12分钟,漏检率为O%,误检率小于10%,完全可以达到工业应用的要求。
Automated optical inspection (AOI) system has become more and more important in the field of wafer production. In order to be competitive in the semiconductor manufacturing industry, defect inspection becomes a critical issue. The goal of wafer defect inspection is to detect defective dies and discard them. However, these existing methods have limitations when dealing with the high speed and high accuracy applications. In this paper, the previous research works and literatures of the key algorithms in automated optical inspection. Some novel algorithms with high speed, high accuracy are proposed and the system of defect inspection is designed aims the existing method's shortages.
This dissertation firstly analyzed the image used in an automated optical inspection system. The image acquirement system which is the most relevant factors to the image quality is analyzed. A novel bright-dark-field light source design is proposed based on the research of the previous light source design to solve the insufficient of camera dynamic range.
The algorithms for image segmentation have been studied in details. The existing thresholding, geometric primitives and template matching methods have been summarized and compared. A novel image segmentation algorithm based on compressed sensing principle is proposed. This algorithm is efficient and robust. Then this algorithm is used in a wafer defect inspection system with template matching algorithm to get an efficient and accurate inspection result.
The feature extraction and defect classification are the key technique of the automated optical inspection. For feature extraction of wafer defect, the traditional methods are low efficiency, poor classification, and sensitive to lighting and noise. In this paper, the line detection algorithm based on phase congruency is improved to get high efficiency and accuracy. This novel algorithm can be also used in corner detection. Then a new feature extraction algorithm based on scale-space principle and Harris corner detector is proposed. This algorithm has accuracy classification results in back-propagation neural network test.
Finally, an automated wafer defect inspection system based on computer vision was presented. The algorithms and methods in this paper were verified and compared using this system. The experiment results have shown that the proposed algorithms and methods can inspect the wafer defect with high speed and accuracy. The inspection time of one wafer is12minutes, the missing rate is0%and the false rate is less than10%.
引文
[1]T. Yang, M. Rajasekharan, and B. Peters, Semiconductor fabrication facilities design using a hybrid search methodology[J], Computers & Industrial Engineering,36,1999: 565-583
[2]Chao-Ton Su, Taho Yang, Chir-Mour Ke, Neural-network approach for semiconductor wafer post-sawing inspection[J], IEEE Transactions on Semiconductor Manufacturing, 2,2002:260-266
[3]C. Y. Chang, J.W. Chang, An Unsupervised Self-Organizing Neural Network for Automatic Semiconductor Wafer Defect Inspection[C], Robotics and Automation, Proceedings of the 2005 IEEE International Conference on Robotics and Automation, 2005:3000-3005,
[4]朱妙贤,IC晶片的机器视觉自动检测技术的研究[D],广东工业大学,2005
[5]刘润予,深亚微米级IC晶片缺陷检测的机器视觉系统[D],广东工业大学,2007
[6]伍冯洁,深亚微米级IC的图像检测及自动分析技术研究[D],广东工业大学,2006
[7]韩芳芳,表面缺陷视觉在线检测关键技术研究[D],天津大学,2012
[8]Roberts. L. G, Machine perception of three-dimensional solids, In Optical and Electro-optical Information Processing [M]. Cambridge:MIT Press,1965:7-24
[9]Guzman. A, Decomposition of a visual scene into three-dimensional bodies[D], In automatic Interpretation and Classification of Images. New York:Academic Press, 1969:15-58
[10]贾云得,机器视觉[M],北京：科学出版社,2000
[11]Mouaddib. E, Batle. J, Recent progress in structured light in order to solve the correspondence problem in stereo vision[C], Proceedings of the 1997 IEEE International Conference on Robotics and Automation,2007:130-136
[12]Tech. E. K, Mital. D. P, A transputer-based automated visual inspection system for electronic devices and PCBS[J], Optics and Lasers in Engineering,22,1995:161-180
[13]Xu. G, Alp. Z, Epipolar geometry in stereo motion and object recognition[M], The Netherlands:Kluwer Academic Publishers,1996:79-204
[14]卢盛林,张宪民,邝泳聪,基于神经网络的PCB焊点检测方法[J],华南理工大学学报(自然科学版),2008,36(5)：135-139
[15]吴晖辉,张宪民,洪始良,综合边缘和颜色特征的IC类贴装器件的检测[J],光学精密工程,2009,17(9)：2283-2291
[16]张进,微型零件高精度影像测量系统中关键技术研究[D],天津大学,2011.
[17]赵彦玲,基于图像技术的钢球表面缺陷分析与识别[D],哈尔滨理工大学,2009.
[18]孙大文,吴迪,何鸿举,冯耀泽,现代光学成像技术在食品品质快速检测中的应用[J],华南理工大学学报(自然科学版),2012,40(10)：59-68
[19]陈卓,基于小波的铝箔包装食品异物图像处理与识别[D],南京林业大学,2008.
[20]丁一宁,汽车组合仪表PCB自动焊接生产线关键技术研究[D],上海交通大学,2011
[21]厉晓飞,基于机器视觉的汽车零件缺陷检测技术研究[D],武汉理工大学,2012.
[22]何永辉,王康健,石桂芬,基于机器视觉的高速带钢孔洞检测系统,应用光学,2007,28(3)：345-349
[23]吴雪飞,数字图像处理技术在印刷中的应用[D],江南大学,2011.
[24]狄炜,徐伯俊,数字图像处理在纺织中的应用及发展[J],上海纺织科技,2002,30(3)：61-62
[25]毕明德,基于机器视觉的布匹疵点检测系统研究[D],华中科技大学,2012
[26]王玉亮,基于机器视觉的玉米种子品种识别与检测研究[D],山东农业大学,2009
[27]谢飞,张旭苹,张益听,王顺,徐静珠,基于机器视觉的货运列车超限检测中基准面获取方法[J],铁道学报,2012,34(10)：72-78
[28]Y. X. Zhang, S. Wang, X. P. Zhang, Y. M. Guo, F. Xie, Freight Train Gauge-Exceeding Detection Based on 3D Stereo Vision Measurement[J], Machine Vision,2012(7)
[29]Gonzalez. R. C著,阮秋琦等译,数字图像处理[M],北京：电子工业出版社,2007
[30]H.Tang, MRI Brain Image Segmentation by Multi-resolution Edge Detection and Region Seletion[J], Computerized Medical Imaging and Graphics,2000,24(10): 349-357
[31]张云飞,张晔,利用二维熵自动确定图像分割的阈值[J],哈尔滨工程大学学报, 2006,27(3)：353-356
[32]J.Orlando, S.Rui, Image Segmentation by Histogram Thresholding Using Fuzzy Sets[J], IEEE Transactions on Image Processing,2002,11(12):1457-1465
[33]W. G. Li, Multi-threshold Color Image Segmentation Based on Region Growing[C], IEEE International Conference on Intelligent Computing and Intelligent Systems, 2010:239-243
[34]Qin. A. K, Clausi. D. A, Multivariate Image Segmentation Using Semantic Region Growing With Adaptive Edge Penalty[J], IEEE Transactions on Image Processing, 2010,19(8):2157-2170
[35]F. Y. Shih, S. X. Cheng, Automatic Seeded Region Growing for Color Image Segmentation[J], Image and Vision Computing,2005,23:877-886
[36]R. M. Chantal, P. Francoise, Automated 3d Region Growing Algorithm Based on an Assessment Function[J], Pattern Recognition Letters,2002,23:137-150
[37]C. Steger, M. Ulrich, Machine Vision Algorithms and Applications[M], Wiley-VCH, 2008
[38]袁春兰,熊宗龙,周雪花,彭小辉,基于Sobel算子的图像边缘检测研究[J],激光与红外,2009,39(1)：85-87
[39]M Heath, S Sarkar, T Sanocki, Comparison of Edge Detectors:A Methodology and Initial Study[J], Computer Vision and Image Understanding,1998,69(1):38-54
[40]苗启广,王宝树,基于改进的拉普拉斯金字塔变换的图像融合方法[J],光学学报,2007,27(9)：1605-1610
[41]严国萍,戴若愚,潘晴,刘袁缘,基于LOG算子的自适应图像边缘检测方法[J],华中科技大学学报(自然科学版),2008,36(3)：85-102
[42]Wan. T., Canagarajah. N., Achim. A., Segmentation of Noisy Colour Images Using Cauchy Distribution in the Complex Wavelet Domain[J], IEEE Transactions on Image Processing,2011,5(2):159-170
[43]D. Grandi, J. S. Lee, Schuler. D. L., Target Detection and Texture Segmentation in Polarimetric SAR Images Using a Wavelet Frame:Theoretical Aspects[J], IEEE Transactions on Geoscience and Remote Sensing,2007,45(11):3437-3453
[44]D. Qi, Y. X. Li, L. Yu, The Application of Mathematical Morphological Optimization Algorithm in Edge Detection of Defected Wood Image[C], IEEE International Conference on Automation and Logistics,2008:2271-2276
[45]Kabir H, Y. Wang, M. Yu, etc. High-Dimensional Neural-Network Technique and Applications to Microwave Filter Modeling[J], IEEE Transactions on Microwave Theory and Techniques,2010,58(1):145-156
[46]M. N. Kurnaz, Z. Dokur, T. Olmez, Segmentation of Remote-sensing Images by Incremental Neural Network[J], Pattern Recognition Letters,2005,26:1096-1104
[47]Waqas. K., Baig. R, Ali. S, Feature Subset Selection Using Multi-objective Genetic Algorithms[C], IEEE International Multitopic Conference,2009:1-6
[48]赵雪春,戚飞虎,用可变形模板进行基于内容的图像分割算法[J],电子学报,2000,28(4)：69-72
[49]何阳清,基于几何特征的手势识别算法研究[D],上海海事大学,2004.
[50]G. C. H. Chuang, C. C. J. Kuo, Wavelet Descriptor of Planar Curves:Theory and Applications[J], IEEE Trans Image Process,1996,5(1):56-70
[51]赵锋,赵荣椿,纹理分割及特征提取方法综述[J],中国体视学与图像分析,1998,3(4)：238-245
[52]王晓峰,黄德双,杜吉祥,张国军,叶片图像特征提取与识别技术的研究[J],计算机工程与应用,2006,(3)：190-193
[53]R. A. Johnson, D. W. Wichern著,陆璇等译,实用多元统计分析[M],北京：清华大学出版社,2005：347-585
[54]T. Sergois, K. Konstantios,李晶皎,王爱侠,张广渊等译,模式识别[M],北京：电子工业出版社,2006：10-23
[55]F. Ham, I. Kostanic,叶世伟,王海娟译,神经计算原理[M],北京：机械工业出版社,2007
[56]Goshtasby. A, Template Matching in Rotated Images[J], IEEE Transactions on Pattern Analysis and Machine Intelligence,7(3):338-344
[57]Belongie. S, Shape Matching And Object Recognition Using Shape Contexts[J], IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(4):509-500
[58]Chaudhuri. S, Detection of Blood-Vessels in Retinal Images Using Two-Dimensional Matched-Filters[J], IEEE Transactions on Medical Imaging,1989,8(3):263-269
[59]Pereira. S, Robust Template Matching for Affine Resistant Image Watermarks[J], IEEE Transactions on Image Processing,2000,9(6):1123-1129
[60]Jain. A, Object Matching Using Deformable Templates[J], IEEE Transactions on Pattern Analysis And Machine Intelligence,1996,18(3):267-278
[61]Mital, D.P, Teoh. E.K. Computer based wafer inspection system[J], Proceedings IECON'91,3(28):2497-2503
[62]Zhang. J. M, Lin. R. M, Wang. M. J, The development of an automatic post-sawing inspection system using computer vision techniques[J], Computers in Industry,1999, 30:51-60
[63]Su. C. T, Yang. T, Ke. C. M. A neural-network approach for semiconductor wafer post-sawing inspection[J]. IEEE Transactions on Semiconductor Manufacturing,2002, 15(2):260-266
[64]T. Lindeberg, Edge Detection And Ridge Detection With Automatic Scale Selection[J], International Journal Of Computer Vision,1998,30(2):117-154
[65]T. Lindeberg, Feature Detection With Automatic Scale Selection[J], International Journal of Computer Vision,1998,30(2):79-116
[66]L. Bretzner, Feature Tracking with Automatic Selection of Spatial Scales[J], Computer Vision and Image Understanding,1988,71(3),385-392
[67]Lindeberg, T, Principles for Automatic Scale Selection[M], Handbook on Computational Vision and Active Perception Laboratory,1999:1-45
[68]Trajkovic. M, Fast Corner Detection[J], Image And Vision Computing,1998,16(2): 75-87
[69]Zheng. Z. Q, Wang. H, Teoh. E. K, Analysis of Gray Level Corner Detection[J], Pattern Recognition Letters,1999,20(2):149-162
[70]Harris. C, Stephens. M, A combined corner and edge detector[C], Alvey vision conference,1988:147-152
[71]Canny. J, A Computational Approach to Edge-Detection[J], IEEE Transactions on Pattern Analysis and Machine Intelligence,1986,8(6):679-698
[72]Pal. N, Pal. S, A Review on Image Segmentation Techniques[J], Pattern Recognition, 1993,26(9):1277-1294
[73]Comaniciu. D, Meer. P, Mean Shift:A Robust Approach Toward Feature Space Analysis[J], IEEE Transactions on Pattern Analysis and Machine Intelligence,2002, 24(5):603-619
[74]M. Born, E. Wolf, Principle Of Optics[M], Cambridge University Press, Cambridge, 1999
[75]K. Lenhardt, Optical Systems In Machine Vision[M], Handbook Of Machine Vision, Wiley-Vch, Weinheim,2006
[76]光学显微镜,http://baike.baidu.com/view/25112.htm
[77]邓仕超,光学显微镜图像处理技术及应用研究[D],天津大学,2010.
[78]Holst. G. C, CCD arrays, cameras, and displays [M]. SPIE Press, Bellingham, WA, 2nd,1998
[79]Gamal. A. E, Eltoukhy. H, CMOS image sensor. IEEE Circuits and Devices Magazine[J],2005,21(3):6-20
[80]Miida. T, A 1.5Mpixel imager with localized hole-modulation method[C], International Solid-State Circuits Conference,2002:42-43
[81]陈榕庭,彭美桂,CCD/CMOS图像传感器基础与应用[M],北京：科学出版社,2006
[82]O. Yadid, CMOS Imagers:From Photo-Transduction to Image Processing[D], Kluwer Academic, Dordrecht,2004
[83]Alleysson. D, Susstrunk. S, Herault. J, Linear demosaicing inspired by the human visual system [J]. IEEE Transactions on Image Processing,2005,14(4):439-449
[84]汪家其,用于LED晶元检测中的高动态范围图像合成技术[D],南京大学,2012
[85]Hirakawa. K, Parks. T. W, Adaptive homogeneity-directed demosaicing algorithm [J], IEEE Transactions on Image Processing,2005,14(3):360-369
[86]冈萨雷斯,数字图像处理[M],北京：电子工业出版社,2006
[87]R. Deriche, Using Canny'S Criteria to Derive A Recursively Implemented Opti-Mal Edge Detector[J], International Journal of Computer Vision,1987,1:167-187
[88]R. M. Haralick, L. G. Shapiro, Computer and Robot Vision[M], Addison-Wesley,1990
[89]S. H. Joseph, Unbiased Least Squares Fitting of Circular Arcs[J], Computer Vision, Graphics, and Image Processing:Graphical Models and Image Processing,1994,56(5): 424-432
[90]S. J. Ahn, W. Rauh, H. J. Warnecke, Least-Squares Orthogonal Distances Fitting of Circle, Sphere, Ellipse, Hyperbola, And Parabola[J], Pattern Recognition,2001,34(12): 2283-2203
[91]B. Kashin, The Widths of Certain Finite Dimensional Sets and Classes of Smooth functions[J], Izv Akad Nauk Sssr,1977,41(2):334-351
[92]E. J. Candes, J. Romberg, T. Tao, Robust Uncertainty Principles:Exact Signal reconstruction From Highly Incomplete Frequency Information[J]. IEEE Transactions on Information Theory,2006,52(2):489-509.
[93]D. L. Donoho. Compressed Sensing[J]. IEEE Transactions on Information Theory, 2006,52(4):1289-1306.
[94]Achlioptas. D, Database-Friendly Random Projections:Johnson-Lindenstrauss With binary Coins[J]. Journal of Computer and System Sciences,2003,66:671-687
[95]Baraniuk. R, Davenport. M, Devore. R, Wakin. M, A Simple Proof of The Restricted Isometry Property for Random Matrices[J], Constructive Approximation,2008,28: 253-263
[96]Wright. J, Yang. A, Ganesh. A, Sastry. S, Ma. Y, Robust Face Recognition via Sparse Representation[J], Pattern Analysis and Machine Intelligence,2009,31:210-227
[97]Li. H, Shen. C, Shi. Q, Real-Time Visual Tracking Using Compressive Sensing[C]. IEEE Conference on Computer Vision and Pattern Recognition,2001:1305-1312
[98]Liu. L, Fieguth. P, Texture Classification from Random Features[J]. Pattern Analysis and Machine Intelligence,34:574-586
[99]Viola. P, Jones. M, Rapid Object Detection Using A Boosted Cascade of Simple Features[C]. IEEE Conference on Computer Vision and Pattern Recognition,2001: 511-518
[100]Li. S, Zhang. Z, Float boost learning and statistical face detection[J]. Pattern Analysis and Machine Intelligence,2004,26:1-12
[101]Babenko. B, Yang. M. H, Belongie. S, Robust Object Tracking with Online Multiple Instance Learning[J]. Pattern Analysis and Machine Intelligence,2011,33,1619-1632
[102]Diaconis. P, Freedman. D, Asymptotics of Graphical Projection Pursuit[J]. Annals of Statistics,1984,12,228-235
[103]Kwon. J, Lee. K, Visual tracking decomposition[C], Computer Vision and Pattern Recognition,2010:1269-1276
[104]Santner. J, Leistner. C, Saffari. A, PROST Parallel Robust Online Simple Tracking[C], IEEE Conference on Computer Vision and Pattern Recognition,2010:723-730
[105]Babenko. B, Yang. M. H, Belongie. S, Robust object tracking with online multiple instance learning[M]. IEEE Conference on Pattern Analysis and Machine Intelligence, 2011:1619-1632
[106]Grabner. H, Grabner. M, Bischof. H, Real-time tracking via online boosting[C], British Machine Vision Conference,2006:47-56
[107]Mei. X., Ling. H, Robust visual tracking and vehicle classification via sparse representation[J]. IEEE Conference on Pattern Analysis and Machine Intelligence, 2011:2259-2272
[108]Kalal. Z, Matas. J, P-n learning:bootstrapping binary classifier by structural constraints[C], IEEE Conference on Computer Vision and Pattern Recognition,2010: 49-56
[109]C. Steger, On The Calculation of Arbitrary Moments of Polygons[M], Technical Report FGBV-96-05,1996
[110]边肇祺,张学工,模式识别[M],清华大学出版社,北京,2000
[111]Morrone. M.C, Ross. J. R, Burr. D. C, Owens. R. A, Mach Bands are Phase Dependent[J], Nature,1986,324:250-253
[112]Kovesi. P. D, Edges are Not Just Steps[C], Proceedings of The Fifth Asian Conference on Computer Vision,2002:822-827
[113]P. Kovesi, Phase Congruency Detects Corners and Edges[C], The Australian Pattern Recognition Society Conference,2003:309-318
[114]Kovesi. P. D, Invariant Measures of Image Features from Phase Information[D], PHD Thesis, The University of Western Australia,1996
[115]D. J. Field, Relations Between The Statistics of Natural Images and The Response Properties of Cortical Cells[J], Journal of The Optical Society of America,1987,4(12): 2379-2394.
[116]惠颖,罗海波,基于相位一致性检测的目标分割和跟踪方法[J],红外与激光工程,2007,36(6)：984-987
[117]卢振泰,冯衍秋,冯前进,基于主相位一致性的医学图像配准[J],电子学报,2008,36(10)：1974-1978
[118]Hui. W. L, C. Xu, Zhong. W. F, A Method for Extracting The Center of Ting-Structured-Light Stripe[C]. International Symposium on Intelligent Information Technology Application,2008,2:906-910
[119]Steger. C, An Unbiased Detector of Curvilinear Structures[J], IEEE Transactions on Pattern Analysis and Machine Intelligence,1998,20:113-125
[120]Wang. S, Xu. J. Z, Zhang. Y. X, Zhang. X. P, Xie. F, Reliability Evaluation Method and Application for Light-Stripe-Center Extraction[J],Acta Optica Sinica,2011,11: 203-209
[121]Lu. C. S. Chung. P. C, Chen. C. F, Unsupervised Texture Segmentation Using Gabor Filters[J], Pttern Recognition,1997,30(5):729-742
[122]Basher. M. K, Matsumoto. T, Ohnishi. N, Wavelet Transform-Based Locally Orderless Images for Texture Segmentation[J], Pattern Recognition Letters,2003,24(15): 2633-2650
[123]P. J. Werbos, Beyond Regression:New Tools For Prediction And Analysis in The Behavioral Sciences[D], PH.D. Thesis, Cambridge, Harvard University,1974
[124]Y. Lecun, Une Procedure D'Apprentissage Pour Reseau A Seuil Asymmetrique[C], CESTA,1985:599-604
[125]D. E. Rumelhart, G. E. Hinton, R. J. Williams, Learing Internal Representations by Error Propagation[M], M.I.T Press,1986
[126]D. E. Rumelhart, G. E. Hinton, R. J. Williams, Learning Representations by Back-Propagating Errors[J], Nature,1986,323:533
[127]D. Nguyen, B. Widrow, Improving The Learning Speed of The 2-Layer Neural Networks by Choosing Initial Values of Adaptive Weights[C], Proceedings of The International Joint Conference on Neural Networks,1990,3:21-26
[2]Chao-Ton Su, Taho Yang, Chir-Mour Ke, Neural-network approach for semiconductor wafer post-sawing inspection[J], IEEE Transactions on Semiconductor Manufacturing, 2,2002:260-266
[3]C. Y. Chang, J.W. Chang, An Unsupervised Self-Organizing Neural Network for Automatic Semiconductor Wafer Defect Inspection[C], Robotics and Automation, Proceedings of the 2005 IEEE International Conference on Robotics and Automation, 2005:3000-3005,
[4]朱妙贤,IC晶片的机器视觉自动检测技术的研究[D],广东工业大学,2005
[5]刘润予,深亚微米级IC晶片缺陷检测的机器视觉系统[D],广东工业大学,2007
[6]伍冯洁,深亚微米级IC的图像检测及自动分析技术研究[D],广东工业大学,2006
[7]韩芳芳,表面缺陷视觉在线检测关键技术研究[D],天津大学,2012
[8]Roberts. L. G, Machine perception of three-dimensional solids, In Optical and Electro-optical Information Processing [M]. Cambridge:MIT Press,1965:7-24
[9]Guzman. A, Decomposition of a visual scene into three-dimensional bodies[D], In automatic Interpretation and Classification of Images. New York:Academic Press, 1969:15-58
[10]贾云得,机器视觉[M],北京：科学出版社,2000
[11]Mouaddib. E, Batle. J, Recent progress in structured light in order to solve the correspondence problem in stereo vision[C], Proceedings of the 1997 IEEE International Conference on Robotics and Automation,2007:130-136
[12]Tech. E. K, Mital. D. P, A transputer-based automated visual inspection system for electronic devices and PCBS[J], Optics and Lasers in Engineering,22,1995:161-180
[13]Xu. G, Alp. Z, Epipolar geometry in stereo motion and object recognition[M], The Netherlands:Kluwer Academic Publishers,1996:79-204
[14]卢盛林,张宪民,邝泳聪,基于神经网络的PCB焊点检测方法[J],华南理工大学学报(自然科学版),2008,36(5)：135-139
[15]吴晖辉,张宪民,洪始良,综合边缘和颜色特征的IC类贴装器件的检测[J],光学精密工程,2009,17(9)：2283-2291
[16]张进,微型零件高精度影像测量系统中关键技术研究[D],天津大学,2011.
[17]赵彦玲,基于图像技术的钢球表面缺陷分析与识别[D],哈尔滨理工大学,2009.
[18]孙大文,吴迪,何鸿举,冯耀泽,现代光学成像技术在食品品质快速检测中的应用[J],华南理工大学学报(自然科学版),2012,40(10)：59-68
[19]陈卓,基于小波的铝箔包装食品异物图像处理与识别[D],南京林业大学,2008.
[20]丁一宁,汽车组合仪表PCB自动焊接生产线关键技术研究[D],上海交通大学,2011
[21]厉晓飞,基于机器视觉的汽车零件缺陷检测技术研究[D],武汉理工大学,2012.
[22]何永辉,王康健,石桂芬,基于机器视觉的高速带钢孔洞检测系统,应用光学,2007,28(3)：345-349
[23]吴雪飞,数字图像处理技术在印刷中的应用[D],江南大学,2011.
[24]狄炜,徐伯俊,数字图像处理在纺织中的应用及发展[J],上海纺织科技,2002,30(3)：61-62
[25]毕明德,基于机器视觉的布匹疵点检测系统研究[D],华中科技大学,2012
[26]王玉亮,基于机器视觉的玉米种子品种识别与检测研究[D],山东农业大学,2009
[27]谢飞,张旭苹,张益听,王顺,徐静珠,基于机器视觉的货运列车超限检测中基准面获取方法[J],铁道学报,2012,34(10)：72-78
[28]Y. X. Zhang, S. Wang, X. P. Zhang, Y. M. Guo, F. Xie, Freight Train Gauge-Exceeding Detection Based on 3D Stereo Vision Measurement[J], Machine Vision,2012(7)
[29]Gonzalez. R. C著,阮秋琦等译,数字图像处理[M],北京：电子工业出版社,2007
[30]H.Tang, MRI Brain Image Segmentation by Multi-resolution Edge Detection and Region Seletion[J], Computerized Medical Imaging and Graphics,2000,24(10): 349-357
[31]张云飞,张晔,利用二维熵自动确定图像分割的阈值[J],哈尔滨工程大学学报, 2006,27(3)：353-356
[32]J.Orlando, S.Rui, Image Segmentation by Histogram Thresholding Using Fuzzy Sets[J], IEEE Transactions on Image Processing,2002,11(12):1457-1465
[33]W. G. Li, Multi-threshold Color Image Segmentation Based on Region Growing[C], IEEE International Conference on Intelligent Computing and Intelligent Systems, 2010:239-243
[34]Qin. A. K, Clausi. D. A, Multivariate Image Segmentation Using Semantic Region Growing With Adaptive Edge Penalty[J], IEEE Transactions on Image Processing, 2010,19(8):2157-2170
[35]F. Y. Shih, S. X. Cheng, Automatic Seeded Region Growing for Color Image Segmentation[J], Image and Vision Computing,2005,23:877-886
[36]R. M. Chantal, P. Francoise, Automated 3d Region Growing Algorithm Based on an Assessment Function[J], Pattern Recognition Letters,2002,23:137-150
[37]C. Steger, M. Ulrich, Machine Vision Algorithms and Applications[M], Wiley-VCH, 2008
[38]袁春兰,熊宗龙,周雪花,彭小辉,基于Sobel算子的图像边缘检测研究[J],激光与红外,2009,39(1)：85-87
[39]M Heath, S Sarkar, T Sanocki, Comparison of Edge Detectors:A Methodology and Initial Study[J], Computer Vision and Image Understanding,1998,69(1):38-54
[40]苗启广,王宝树,基于改进的拉普拉斯金字塔变换的图像融合方法[J],光学学报,2007,27(9)：1605-1610
[41]严国萍,戴若愚,潘晴,刘袁缘,基于LOG算子的自适应图像边缘检测方法[J],华中科技大学学报(自然科学版),2008,36(3)：85-102
[42]Wan. T., Canagarajah. N., Achim. A., Segmentation of Noisy Colour Images Using Cauchy Distribution in the Complex Wavelet Domain[J], IEEE Transactions on Image Processing,2011,5(2):159-170
[43]D. Grandi, J. S. Lee, Schuler. D. L., Target Detection and Texture Segmentation in Polarimetric SAR Images Using a Wavelet Frame:Theoretical Aspects[J], IEEE Transactions on Geoscience and Remote Sensing,2007,45(11):3437-3453
[44]D. Qi, Y. X. Li, L. Yu, The Application of Mathematical Morphological Optimization Algorithm in Edge Detection of Defected Wood Image[C], IEEE International Conference on Automation and Logistics,2008:2271-2276
[45]Kabir H, Y. Wang, M. Yu, etc. High-Dimensional Neural-Network Technique and Applications to Microwave Filter Modeling[J], IEEE Transactions on Microwave Theory and Techniques,2010,58(1):145-156
[46]M. N. Kurnaz, Z. Dokur, T. Olmez, Segmentation of Remote-sensing Images by Incremental Neural Network[J], Pattern Recognition Letters,2005,26:1096-1104
[47]Waqas. K., Baig. R, Ali. S, Feature Subset Selection Using Multi-objective Genetic Algorithms[C], IEEE International Multitopic Conference,2009:1-6
[48]赵雪春,戚飞虎,用可变形模板进行基于内容的图像分割算法[J],电子学报,2000,28(4)：69-72
[49]何阳清,基于几何特征的手势识别算法研究[D],上海海事大学,2004.
[50]G. C. H. Chuang, C. C. J. Kuo, Wavelet Descriptor of Planar Curves:Theory and Applications[J], IEEE Trans Image Process,1996,5(1):56-70
[51]赵锋,赵荣椿,纹理分割及特征提取方法综述[J],中国体视学与图像分析,1998,3(4)：238-245
[52]王晓峰,黄德双,杜吉祥,张国军,叶片图像特征提取与识别技术的研究[J],计算机工程与应用,2006,(3)：190-193
[53]R. A. Johnson, D. W. Wichern著,陆璇等译,实用多元统计分析[M],北京：清华大学出版社,2005：347-585
[54]T. Sergois, K. Konstantios,李晶皎,王爱侠,张广渊等译,模式识别[M],北京：电子工业出版社,2006：10-23
[55]F. Ham, I. Kostanic,叶世伟,王海娟译,神经计算原理[M],北京：机械工业出版社,2007
[56]Goshtasby. A, Template Matching in Rotated Images[J], IEEE Transactions on Pattern Analysis and Machine Intelligence,7(3):338-344
[57]Belongie. S, Shape Matching And Object Recognition Using Shape Contexts[J], IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(4):509-500
[58]Chaudhuri. S, Detection of Blood-Vessels in Retinal Images Using Two-Dimensional Matched-Filters[J], IEEE Transactions on Medical Imaging,1989,8(3):263-269
[59]Pereira. S, Robust Template Matching for Affine Resistant Image Watermarks[J], IEEE Transactions on Image Processing,2000,9(6):1123-1129
[60]Jain. A, Object Matching Using Deformable Templates[J], IEEE Transactions on Pattern Analysis And Machine Intelligence,1996,18(3):267-278
[61]Mital, D.P, Teoh. E.K. Computer based wafer inspection system[J], Proceedings IECON'91,3(28):2497-2503
[62]Zhang. J. M, Lin. R. M, Wang. M. J, The development of an automatic post-sawing inspection system using computer vision techniques[J], Computers in Industry,1999, 30:51-60
[63]Su. C. T, Yang. T, Ke. C. M. A neural-network approach for semiconductor wafer post-sawing inspection[J]. IEEE Transactions on Semiconductor Manufacturing,2002, 15(2):260-266
[64]T. Lindeberg, Edge Detection And Ridge Detection With Automatic Scale Selection[J], International Journal Of Computer Vision,1998,30(2):117-154
[65]T. Lindeberg, Feature Detection With Automatic Scale Selection[J], International Journal of Computer Vision,1998,30(2):79-116
[66]L. Bretzner, Feature Tracking with Automatic Selection of Spatial Scales[J], Computer Vision and Image Understanding,1988,71(3),385-392
[67]Lindeberg, T, Principles for Automatic Scale Selection[M], Handbook on Computational Vision and Active Perception Laboratory,1999:1-45
[68]Trajkovic. M, Fast Corner Detection[J], Image And Vision Computing,1998,16(2): 75-87
[69]Zheng. Z. Q, Wang. H, Teoh. E. K, Analysis of Gray Level Corner Detection[J], Pattern Recognition Letters,1999,20(2):149-162
[70]Harris. C, Stephens. M, A combined corner and edge detector[C], Alvey vision conference,1988:147-152
[71]Canny. J, A Computational Approach to Edge-Detection[J], IEEE Transactions on Pattern Analysis and Machine Intelligence,1986,8(6):679-698
[72]Pal. N, Pal. S, A Review on Image Segmentation Techniques[J], Pattern Recognition, 1993,26(9):1277-1294
[73]Comaniciu. D, Meer. P, Mean Shift:A Robust Approach Toward Feature Space Analysis[J], IEEE Transactions on Pattern Analysis and Machine Intelligence,2002, 24(5):603-619
[74]M. Born, E. Wolf, Principle Of Optics[M], Cambridge University Press, Cambridge, 1999
[75]K. Lenhardt, Optical Systems In Machine Vision[M], Handbook Of Machine Vision, Wiley-Vch, Weinheim,2006
[76]光学显微镜,http://baike.baidu.com/view/25112.htm
[77]邓仕超,光学显微镜图像处理技术及应用研究[D],天津大学,2010.
[78]Holst. G. C, CCD arrays, cameras, and displays [M]. SPIE Press, Bellingham, WA, 2nd,1998
[79]Gamal. A. E, Eltoukhy. H, CMOS image sensor. IEEE Circuits and Devices Magazine[J],2005,21(3):6-20
[80]Miida. T, A 1.5Mpixel imager with localized hole-modulation method[C], International Solid-State Circuits Conference,2002:42-43
[81]陈榕庭,彭美桂,CCD/CMOS图像传感器基础与应用[M],北京：科学出版社,2006
[82]O. Yadid, CMOS Imagers:From Photo-Transduction to Image Processing[D], Kluwer Academic, Dordrecht,2004
[83]Alleysson. D, Susstrunk. S, Herault. J, Linear demosaicing inspired by the human visual system [J]. IEEE Transactions on Image Processing,2005,14(4):439-449
[84]汪家其,用于LED晶元检测中的高动态范围图像合成技术[D],南京大学,2012
[85]Hirakawa. K, Parks. T. W, Adaptive homogeneity-directed demosaicing algorithm [J], IEEE Transactions on Image Processing,2005,14(3):360-369
[86]冈萨雷斯,数字图像处理[M],北京：电子工业出版社,2006
[87]R. Deriche, Using Canny'S Criteria to Derive A Recursively Implemented Opti-Mal Edge Detector[J], International Journal of Computer Vision,1987,1:167-187
[88]R. M. Haralick, L. G. Shapiro, Computer and Robot Vision[M], Addison-Wesley,1990
[89]S. H. Joseph, Unbiased Least Squares Fitting of Circular Arcs[J], Computer Vision, Graphics, and Image Processing:Graphical Models and Image Processing,1994,56(5): 424-432
[90]S. J. Ahn, W. Rauh, H. J. Warnecke, Least-Squares Orthogonal Distances Fitting of Circle, Sphere, Ellipse, Hyperbola, And Parabola[J], Pattern Recognition,2001,34(12): 2283-2203
[91]B. Kashin, The Widths of Certain Finite Dimensional Sets and Classes of Smooth functions[J], Izv Akad Nauk Sssr,1977,41(2):334-351
[92]E. J. Candes, J. Romberg, T. Tao, Robust Uncertainty Principles:Exact Signal reconstruction From Highly Incomplete Frequency Information[J]. IEEE Transactions on Information Theory,2006,52(2):489-509.
[93]D. L. Donoho. Compressed Sensing[J]. IEEE Transactions on Information Theory, 2006,52(4):1289-1306.
[94]Achlioptas. D, Database-Friendly Random Projections:Johnson-Lindenstrauss With binary Coins[J]. Journal of Computer and System Sciences,2003,66:671-687
[95]Baraniuk. R, Davenport. M, Devore. R, Wakin. M, A Simple Proof of The Restricted Isometry Property for Random Matrices[J], Constructive Approximation,2008,28: 253-263
[96]Wright. J, Yang. A, Ganesh. A, Sastry. S, Ma. Y, Robust Face Recognition via Sparse Representation[J], Pattern Analysis and Machine Intelligence,2009,31:210-227
[97]Li. H, Shen. C, Shi. Q, Real-Time Visual Tracking Using Compressive Sensing[C]. IEEE Conference on Computer Vision and Pattern Recognition,2001:1305-1312
[98]Liu. L, Fieguth. P, Texture Classification from Random Features[J]. Pattern Analysis and Machine Intelligence,34:574-586
[99]Viola. P, Jones. M, Rapid Object Detection Using A Boosted Cascade of Simple Features[C]. IEEE Conference on Computer Vision and Pattern Recognition,2001: 511-518
[100]Li. S, Zhang. Z, Float boost learning and statistical face detection[J]. Pattern Analysis and Machine Intelligence,2004,26:1-12
[101]Babenko. B, Yang. M. H, Belongie. S, Robust Object Tracking with Online Multiple Instance Learning[J]. Pattern Analysis and Machine Intelligence,2011,33,1619-1632
[102]Diaconis. P, Freedman. D, Asymptotics of Graphical Projection Pursuit[J]. Annals of Statistics,1984,12,228-235
[103]Kwon. J, Lee. K, Visual tracking decomposition[C], Computer Vision and Pattern Recognition,2010:1269-1276
[104]Santner. J, Leistner. C, Saffari. A, PROST Parallel Robust Online Simple Tracking[C], IEEE Conference on Computer Vision and Pattern Recognition,2010:723-730
[105]Babenko. B, Yang. M. H, Belongie. S, Robust object tracking with online multiple instance learning[M]. IEEE Conference on Pattern Analysis and Machine Intelligence, 2011:1619-1632
[106]Grabner. H, Grabner. M, Bischof. H, Real-time tracking via online boosting[C], British Machine Vision Conference,2006:47-56
[107]Mei. X., Ling. H, Robust visual tracking and vehicle classification via sparse representation[J]. IEEE Conference on Pattern Analysis and Machine Intelligence, 2011:2259-2272
[108]Kalal. Z, Matas. J, P-n learning:bootstrapping binary classifier by structural constraints[C], IEEE Conference on Computer Vision and Pattern Recognition,2010: 49-56
[109]C. Steger, On The Calculation of Arbitrary Moments of Polygons[M], Technical Report FGBV-96-05,1996
[110]边肇祺,张学工,模式识别[M],清华大学出版社,北京,2000
[111]Morrone. M.C, Ross. J. R, Burr. D. C, Owens. R. A, Mach Bands are Phase Dependent[J], Nature,1986,324:250-253
[112]Kovesi. P. D, Edges are Not Just Steps[C], Proceedings of The Fifth Asian Conference on Computer Vision,2002:822-827
[113]P. Kovesi, Phase Congruency Detects Corners and Edges[C], The Australian Pattern Recognition Society Conference,2003:309-318
[114]Kovesi. P. D, Invariant Measures of Image Features from Phase Information[D], PHD Thesis, The University of Western Australia,1996
[115]D. J. Field, Relations Between The Statistics of Natural Images and The Response Properties of Cortical Cells[J], Journal of The Optical Society of America,1987,4(12): 2379-2394.
[116]惠颖,罗海波,基于相位一致性检测的目标分割和跟踪方法[J],红外与激光工程,2007,36(6)：984-987
[117]卢振泰,冯衍秋,冯前进,基于主相位一致性的医学图像配准[J],电子学报,2008,36(10)：1974-1978
[118]Hui. W. L, C. Xu, Zhong. W. F, A Method for Extracting The Center of Ting-Structured-Light Stripe[C]. International Symposium on Intelligent Information Technology Application,2008,2:906-910
[119]Steger. C, An Unbiased Detector of Curvilinear Structures[J], IEEE Transactions on Pattern Analysis and Machine Intelligence,1998,20:113-125
[120]Wang. S, Xu. J. Z, Zhang. Y. X, Zhang. X. P, Xie. F, Reliability Evaluation Method and Application for Light-Stripe-Center Extraction[J],Acta Optica Sinica,2011,11: 203-209
[121]Lu. C. S. Chung. P. C, Chen. C. F, Unsupervised Texture Segmentation Using Gabor Filters[J], Pttern Recognition,1997,30(5):729-742
[122]Basher. M. K, Matsumoto. T, Ohnishi. N, Wavelet Transform-Based Locally Orderless Images for Texture Segmentation[J], Pattern Recognition Letters,2003,24(15): 2633-2650
[123]P. J. Werbos, Beyond Regression:New Tools For Prediction And Analysis in The Behavioral Sciences[D], PH.D. Thesis, Cambridge, Harvard University,1974
[124]Y. Lecun, Une Procedure D'Apprentissage Pour Reseau A Seuil Asymmetrique[C], CESTA,1985:599-604
[125]D. E. Rumelhart, G. E. Hinton, R. J. Williams, Learing Internal Representations by Error Propagation[M], M.I.T Press,1986
[126]D. E. Rumelhart, G. E. Hinton, R. J. Williams, Learning Representations by Back-Propagating Errors[J], Nature,1986,323:533
[127]D. Nguyen, B. Widrow, Improving The Learning Speed of The 2-Layer Neural Networks by Choosing Initial Values of Adaptive Weights[C], Proceedings of The International Joint Conference on Neural Networks,1990,3:21-26