弱透光环境下微构件机器视觉检测关键技术研究
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摘要
为实现微操纵装配过程中微构件的静动态状况的高精度实时监测,推动非接触式微机器视觉检测技术的发展,本文结合国家高科技研究发展计划资助项目《基于超声辐射力的微纳构件三维遥操纵关键技术研究》,以机器视觉、数字图像处理和小波变换等为理论和技术基础,针对微操纵过程中弱透光环境下微构件静动态相关参数的检测,在开展微视觉图像处理技术和微视觉检测系统标定技术研究的基础上,采用基于小波变换的亚像素边缘检测方法和最小外接矩形测量方法,实现了二维图像中微构件的形状尺寸、方位角度及质心位置的精确测量,并结合相位相关技术和光流估计技术,发展了一种多尺度微位移的显微视觉高精度检测技术。同时,还开发了一套双通道微视觉检测系统,以满足微操纵装配应用的需求。具体内容包括：
第一章,综合论述了开展微机器视觉检测技术研究的重要意义,对微机器视觉检测技术的研究现状及其发展趋势进行了系统总结,并在讨论分析现有微机器视觉检测关键技术优缺点的基础上,提出了本论文的研究内容及章节安排。
第二章,在分析弱透光环境下微视觉图像特性的基础上,提出了具有全局和局部特性的SCLAHE图像增强方法和基于多分辨率阈值的非均匀光照微视觉图像实时分割方法,并与传统图像处理方法进行了实验比较,得出了本章所提方法具有更高的精确性和鲁棒性。
第三章,对微机器视觉系统成像原理和畸变特性展开了研究,根据所建的微机器视觉标定模型,提出了基于多项式畸变补偿的DLT微机器视觉系统标定方法,并通过实验验证了本章标定技术的可行性和有效性。
第四章,发展了一种基于小波变换亚像素边缘检测的微构件二维尺寸测量方法和基于任意方位最小外接矩形的微构件角度及质心测量方法,并有机结合相位相关技术和光流估计技术,提出了一种多尺度微位移的显微视觉高精度检测技术。最后开展了计算机仿真和实验研究,证实了本章所提方法能有效协调测量范围和精度之间的矛盾。
第五章,根据前三章提出的微机器视觉检测理论和技术,构建了一套面向超声辐射力微粒子操纵应用的双通道微视觉检测系统,并对检测系统的总体方案及其系统软硬件关键技术进行了设计和开发,通过实验研究证实了所研发技术、系统的可行性和有效性。
最后为论文的结论和展望,对全文的研究内容作了概括总结,并对进一步的研究进行了展望。
In order to realize the high-precision and real-time inspection of static and dynamic status for micro-components during the process of micro-manipulation and promote the development of non-contacted micro machine vision inspection technology, combining with National High-Tech R&D Program "research on key technologies of three-dimension remote manipulation for micro-components based on ultrasonic radiation force", the measurement of the static and dynamic parameters of micro-components under the weak light environment during the process of micro-manipulation was studied in this dissertation by means of the theory and technology including machine vision, digital image processing and wavelet transform et al. On the basis of researching on micro-vision image processing and micro-vision inspection system calibrating techniques, the wavelet-based sub-pixel edge detection method and the minimum bounding rectangle measuring method were used to achieve the accurate measurement of the shape and size, orientation angle and centroid location of micro-components in 2D image. And combining with the phase correlation and optical flow estimation techniques, a new high-precision technique based on micro-vision was developed to detect multi-scale micro-displacement. Meanwhile, a set of dual-channel micro-vision inspection system was designed to meet the needs of micromanipulation and microassembly applications. The detail work was presented as follows:
Chapter 1, a survey of the importance about research on micro machine vision inspection technology was given. The review of current status and future development trend of micro machine vision inspection technology was summarized. Based on discussing and analyzing the advantages and disadvantages of the traditional micro machine vision techniques, the research content of this dissertation was presented and each chapter of this dissertation was arranged.
Chapter 2, according to the analysis of micro-vision images features under the weak light, the SCLAHE image enhancement method with the global and local characteristics and a kind of real-time segmentation method for micro-vision images with uneven illumination based on multi-resolution threshold were proposed. Then compared with the traditional image processing methods, the presented methods were proved to be more accurate and robust.
Chapter 3, the research on visual imaging theory and aberration of micro machine vision system was carried out. According to the established micro machine vision calibration model, a kind of calibration method for micro machine vision system was proposed, which was realized by means of polynomial distortion compensation and direct linear transform (DLT). Then the experiment was demonstrated to show the feasibility and validity of the presented calibration technique.
Chapter 4, a kind of 2D size measuring method for the micro-component using wavelet-based sub-pixel edge detection technique and a kind of angle and centroid measuring method for the micro-components based on minimum bounding rectangle were developed. Then combining with the phase correlation and optical flow estimation techniques, a new high-precision technique based on micro-vision was proposed to detect multi-scale micro-displacement. Finally, the computer simulation and experiments were studied to show that the proposed method was able to coordinate effectively the contradiction between the measuring range and accuracy.
Chapter 5, according to the micro machine vision inspection theory presented in above chapters, a set of dual-channel micro-vision inspection system for detecting the static and dynamic status of particles was developed. Then the overall program of this system was provided, and the hardware and software of this system were designed. After experiments, the feasibility and validity of the developed techniques and system were showed.
Chapter 6, the main work and conclusions in this dissertation were summarized and the prospect for further research objects was put forward.
第一章,综合论述了开展微机器视觉检测技术研究的重要意义,对微机器视觉检测技术的研究现状及其发展趋势进行了系统总结,并在讨论分析现有微机器视觉检测关键技术优缺点的基础上,提出了本论文的研究内容及章节安排。
第二章,在分析弱透光环境下微视觉图像特性的基础上,提出了具有全局和局部特性的SCLAHE图像增强方法和基于多分辨率阈值的非均匀光照微视觉图像实时分割方法,并与传统图像处理方法进行了实验比较,得出了本章所提方法具有更高的精确性和鲁棒性。
第三章,对微机器视觉系统成像原理和畸变特性展开了研究,根据所建的微机器视觉标定模型,提出了基于多项式畸变补偿的DLT微机器视觉系统标定方法,并通过实验验证了本章标定技术的可行性和有效性。
第四章,发展了一种基于小波变换亚像素边缘检测的微构件二维尺寸测量方法和基于任意方位最小外接矩形的微构件角度及质心测量方法,并有机结合相位相关技术和光流估计技术,提出了一种多尺度微位移的显微视觉高精度检测技术。最后开展了计算机仿真和实验研究,证实了本章所提方法能有效协调测量范围和精度之间的矛盾。
第五章,根据前三章提出的微机器视觉检测理论和技术,构建了一套面向超声辐射力微粒子操纵应用的双通道微视觉检测系统,并对检测系统的总体方案及其系统软硬件关键技术进行了设计和开发,通过实验研究证实了所研发技术、系统的可行性和有效性。
最后为论文的结论和展望,对全文的研究内容作了概括总结,并对进一步的研究进行了展望。
In order to realize the high-precision and real-time inspection of static and dynamic status for micro-components during the process of micro-manipulation and promote the development of non-contacted micro machine vision inspection technology, combining with National High-Tech R&D Program "research on key technologies of three-dimension remote manipulation for micro-components based on ultrasonic radiation force", the measurement of the static and dynamic parameters of micro-components under the weak light environment during the process of micro-manipulation was studied in this dissertation by means of the theory and technology including machine vision, digital image processing and wavelet transform et al. On the basis of researching on micro-vision image processing and micro-vision inspection system calibrating techniques, the wavelet-based sub-pixel edge detection method and the minimum bounding rectangle measuring method were used to achieve the accurate measurement of the shape and size, orientation angle and centroid location of micro-components in 2D image. And combining with the phase correlation and optical flow estimation techniques, a new high-precision technique based on micro-vision was developed to detect multi-scale micro-displacement. Meanwhile, a set of dual-channel micro-vision inspection system was designed to meet the needs of micromanipulation and microassembly applications. The detail work was presented as follows:
Chapter 1, a survey of the importance about research on micro machine vision inspection technology was given. The review of current status and future development trend of micro machine vision inspection technology was summarized. Based on discussing and analyzing the advantages and disadvantages of the traditional micro machine vision techniques, the research content of this dissertation was presented and each chapter of this dissertation was arranged.
Chapter 2, according to the analysis of micro-vision images features under the weak light, the SCLAHE image enhancement method with the global and local characteristics and a kind of real-time segmentation method for micro-vision images with uneven illumination based on multi-resolution threshold were proposed. Then compared with the traditional image processing methods, the presented methods were proved to be more accurate and robust.
Chapter 3, the research on visual imaging theory and aberration of micro machine vision system was carried out. According to the established micro machine vision calibration model, a kind of calibration method for micro machine vision system was proposed, which was realized by means of polynomial distortion compensation and direct linear transform (DLT). Then the experiment was demonstrated to show the feasibility and validity of the presented calibration technique.
Chapter 4, a kind of 2D size measuring method for the micro-component using wavelet-based sub-pixel edge detection technique and a kind of angle and centroid measuring method for the micro-components based on minimum bounding rectangle were developed. Then combining with the phase correlation and optical flow estimation techniques, a new high-precision technique based on micro-vision was proposed to detect multi-scale micro-displacement. Finally, the computer simulation and experiments were studied to show that the proposed method was able to coordinate effectively the contradiction between the measuring range and accuracy.
Chapter 5, according to the micro machine vision inspection theory presented in above chapters, a set of dual-channel micro-vision inspection system for detecting the static and dynamic status of particles was developed. Then the overall program of this system was provided, and the hardware and software of this system were designed. After experiments, the feasibility and validity of the developed techniques and system were showed.
Chapter 6, the main work and conclusions in this dissertation were summarized and the prospect for further research objects was put forward.
引文
[1]李庆祥,李玉和等编著.微装配与微操作技术[M].北京：清华大学出版社,2004：263.
[2]Shaochen C. Nanomanufacturing:Challenges and opportunities from design to fabrication[C].2009.
[3]张力.激光干涉法进行正弦力校准研究[J].计量学报.2005(4)：337-342.
[4]王晓嘉,高隽,王磊.激光三角法综述[J].仪器仪表学报.2004：601-604.
[5]梁宜勇,杨国光,孙戎.激光直写调焦系统特性及离焦应用研究[J].浙江大学学报(工学版).2005(2)：102-105.
[6]张广军著.视觉测量[M].北京：科学出版社,2008：335.
[7]Xudong L, Guanghua Z, Shusheng B. Development of global vision system for biological automatic micro-manipulation system[C].2001.
[8]Kim N H, Bovik A C, Aggarwal S J. Shape description of biological objects via stereo light microscopy [J]. Systems, Man and Cybernetics, IEEE Transactions on.1990,20(2):475-489.
[9]Liao W H, Aggarwal S J, Aggarwal J K. Reconstruction of dynamic 3D structure of biological objects using stereo microscope images[J]. Machine Vision and Applications.1997,9(4):166-178.
[10]Sano T, Nagahata H, Endo H, et al. A visual feedback system for micromanipulation with stereoscopic microscope[C].1998.
[11]Schreier H W, Garcia D, Sutton M A. Advances in light microscope stereo vision[J]. Experimental Mechanics.2004,44(3):278-288.
[12]Lasson L S J M T. Microscopic 3-D displacement field measurements using digital speckle photogray[J]. Optical and Lasers in Engineering.2004,5(41):767-777.
[13]Bown M R, Macinnes J M, Allen R W K, et al. Three-dimensional, three-component velocity measurements using stereoscopic micro-PIV and PTV[J]. Measurement Science and Technology.2006, 17(8):2175-2185.
[14]陈立国,孙立宁,荣伟彬.基于显微视觉与微力觉柔顺混合控制的微操作机器人[J].高技术通讯.2003(12)：53-56.
[15]孙立宁,陈立国,刘品宽,等.微操作机器人显微视觉系统若干问题[J].光学精密工程.2002(2)：171-175.
[16]李丽宏,孙立新,安庆宾,等.基于双目显微立体视觉系统的研究[J].激光与光电子学进展.2003,40(9)：30-35.
[17]王跃宗.SLM显微立体视觉量化和三维数据重构研究[D].大连理工大学,2003.
[18]Li X, Wang X, Zhou Z, et al. On the architecture of the micro machine vision system[Z]. Changchun, China:2006 AFOSR Asia Office; Chinese Academy of Sciences, CAS, China; Chinese Academy of Engineering, CAE, China; Chinese Association of Science and Technology, CAST, China; European Optical Society, EOS.
[19]Branchitta F, Diani M, Corsini G, et al. Dynamic range compression and contrast enhancement in IR imaging systems[Z]. Florence, Italy:2007SPIEEurope.
[20]Pizer S M, Amburn E P, Austin J D, et al. ADAPTIVE HISTOGRAM EQUALIZATION AND ITS VARIATIONS.[J]. Computer vision, graphics, and image processing.1987,39(3):355-368.
[21]Pizer S M, Johnston R E, Ericksen J P, et al. Contrast-limited adaptive histogram equalization:Speed and effectiveness[Z]. Atlanta, GA, USA:1990,337-345.
[22]Reza A M. Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement[J]. Journal of VLSI Signal Processing Systems for Signal, Image, and Video Technology.2004,38(1):35-44.
[23]Jianmao X, Junzhong S, Changjiang Z. Non-linear Algorithm for Contrast Enhancement for Image Using Wavelet Neural Network[C].2006.
[24]G. Apostolopoulos E D. Local Adaptive Contrast Enhancement in Digital Images[Z].2007.
[25]Jin Y, Fayad L, Laine A. Contrast enhancement by multi-scale adaptive histogram equalization[Z]. San Diego, CA, United states:2001,206-213.
[26]Otsu. A Threshold Selection Method from Gray-Level Histograms[J]. Systems, Man and Cybernetics, IEEE Transactions on.1979,9(1):62-66.
[27]Pun T. ENTROPIC THRESHOLDING, A NEW APPROACH.[J]. Computer graphics and image processing.1981,16(3):210-239.
[28]Cao L, Shi Z K, Cheng E K W. Fast automatic multilevel thresholding method[J]. Electronics Letters. 2002,38(16):868-870.
[29]Kim B G, Shim J I, Park D J. Fast image segmentation based on multi-resolution analysis and wavelets[J]. Pattern Recognition Letters.2003,24(16):2995-3006.
[30]Liu Jianzhuang, Li Wenqing. Automatic thresholding of gray-level pictures via two-dimensional OTSU method[J]. Zidonghua Xuebao/Acta Automatica Sinica.1993,19(1):101-105.
[31]Jun Z, Jinglu H. Image segmentation based on 2D Otsu method with histogram analysis[Z]. Wuhan, Hubei, China:2008,105-108.
[32]Zhu N, Wang G, Yang G, et al. A fast 2D otsu thresholding algorithm based on improved histogram[Z]. Nanjing, China:2009,319-323.
[33]景晓军,蔡安妮,等.一种基于二维最大类间方差的图像分割算法[J].通信学报.2001,22(4)：71-76.
[34]郎咸朋,朱枫,郝颖明,等.基于积分图像的快速二维Otsu算法[J].仪器仪表学报.2009(1)：39-43.
[35]Bi Ying Wei, Qiu Tian Shuang. Adaptive image segmentation method based on a simplified PCNN[J]. Tien Tzu Hsueh Pao/Acta Electronica Sinica.2005,33(4):647-650.
[36]Chan P K, Cheng S H, Poon T C. Automated segmentation in confocal images using a density clustering method[J]. Journal of Electronic Imaging.2007,16(4).
[37]田军委,黄永宣,于亚琳.基于熵约束的快速FCM聚类多阈值图像分割算法[J].模式识别与人工智能.2008,21(2)：221-226.
[38]彭静,刘小明.基于数学形态学的显微图像分割算法[J].中国科技博览.2009(32)：146-147.
[39]何斌等编著VisualC++数字图像处理[M].北京：人民邮电出版社,2002：674.
[40]朱红高.图像边缘检测技术研究现状[J].制造业自动化.2010(1)：45-47.
[41]刘冲,夏泽邑,裴伟,等.SLM彩色显微图像的亚像素边缘检测方法[J].机械工程学报.2005,41(1)：71-76.
[42]黄向东,谭久彬.激光共焦显微图像多尺度边缘提取算法研究[J].光电子.激光.2005,16(4)：458-461.
[43]张雷,王秋光,戚基萍.基于模糊边缘检测的胸水癌细胞显微图像处理技术[J].哈尔滨理工大学学报.2007,12(1)：90-92.
[44]白建明,王之琼.分形理论在X光片图像边缘增强中的应用[J].黑龙江医药科学.2006,29(1)：78-79.
[45]刘新春,陈仕东,邹谋炎,等.基于局部直方图相关的造影图象边缘检测方法[J].中国图象图形学报.2000,5(9)：750-754.
[46]马苗,樊养余,谢松云,等.基于灰色系统理论的图象边缘检测新算法[J].中国图象图形学报：A辑.2003,8(10)：1136-1139.
[47]肖锋.基于BP神经网络的数字图像边缘检测算法的研究[J].西安科技大学学报.2005,25(3)：372-375.
[48]Abdel-Aziz Y I K H M. Direct linear transformation into object space coordinates in close-range photogrammetry[J]. Close-Range Photogrammetry.1971:1-18.
[49]Tsai R. A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses[J]. Robotics and Automation, IEEE Journal of.1987,3(4):323-344.
[50]Bacakoglu H, Kamel M S. A three-step camera calibration method[J]. Instrumentation and Measurement, IEEE Transactions on.1997,46(5):1165-1172.
[51]Zhang Z. A flexible new technique for camera calibration[J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on.2000,22(11):1330-1334.
[52]Danuser G. Photogrammetric calibration of a stereo light microscope[J]. Journal of Microscopy.1999, 193(1):62-83.
[53]Zhou Y, Nelson B J. Calibration of a parametric model of an optical microscope[J]. Optical Engineering. 1999,38(12):1989-1995.
[54]J F. On chromatic and geometrical calibration[D]. Lyngby:Technical University of Denmark,1999.
[55]Zhang D, Luo M, Arola D D. Displacement/strain measurements using an optical microscope and digital image correlation[J]. Optical Engineering.2006,45(3).
[56]王跃宗,刘冲,王立鼎,等.微操作成像系统中体视显微镜的参数标定[J].机械工程学报.2003(9)： 81-86.
[57]Kiyono S, Gao W, Zhang S, et al. Self-calibration of a scanning white light interference microscope[J]. Optical Engineering.2000,39(10):2720-2725.
[58]王兵振,刘文耀.一种显微图像测量系统的标定方法[J].光电工程.2006,33(2)：119-122.
[59]郭宝龙,朱娟娟,孙伟.电子稳像的分层位平面全局运动估计算法[J].光子学报.2009(11)：2993-2998.
[60]郑志彬,叶中付.基于相位相关的图像配准算法[J].数据采集与处理.2006,21(4)：444-449.
[61]王晓燕,郑建宏.用于快速块匹配运动估计的自适应十字模式搜索[J].电子与信息学报.2005,27(1)：104-107.
[62]于辉,左洪福,黄传奇.基于立体视觉技术的磨粒显微测量方法[J].交通运输工程学报.2003(1)：88-92.
[63]陈治,胡晓东,傅星,等.基于块匹配的MEMS平面纳米精度运动测量[J].光学精密工程.2008(3)：505-510.
[64]高思田,陈治,胡晓东,等.纳米精度MEMS平面运动测量技术[J].天津大学学报.2008(4)：423-428.
[65]陈治,朱洪程,胡晓东,等.基于相位相关技术的MEMS旋转角度高分辨力测量[J].光学精密工程.2009(8)：1884-1889.
[66]Wei Sun C Q. Dynamic characterization of a microgyroscope by digital image spectrum correlation[J]. Optical engineering.2008,47(3).
[67]胡晓东,李晓俊,孙彬,等.MEMS微结构旋转角度的快速测量方法[J].纳米技术与精密工程.2009(4)：328-332.
[68]冷汹涛.基于光流技术和滤波器方法的多尺度微运动测量[J].西安科技大学学报.2009,29(3)：364-368.
[69]林晓春,李存志.采用AMMDFM方法对水下微光图像进行增强处理[J].西安电子科技大学学报(自然科学版).2006(4)：543-546.
[70]于起峰等著.基于图像的精密测量与运动测量[M].北京：科学出版社,2002：217.
[71]卢清华,张宪民,范彦斌.计算机微视觉微运动测量系统图像噪声分析[J].机械设计与制造.2008(4)：90-92.
[72]Rafaelc美.数字图像处理[M].北京：电子工业出版社,2007.
[73]Barnard K A F B. Investigations into multi-scaleretinex[J]. Colour Imaging Vision and Technology.1999: 17-36.
[74]Chunming L, Gatenby C, Li W, et al. A robust parametric method for bias field estimation and segmentation of MR images[C].2009.
[75]Zheng Y, Grossman M, Awate S P, et al. Automatic correction of intensity nonuniformity from sparseness of gradient distribution in medical images[Z]. London, United kingdom:2009,852-859.
[76]Olshausen B A, Field D J. Emergence of simple-cell receptive field properties by learning a sparse code for natural images[J]. Nature.1996,381(6583):607.
[77]Kim B G, Shim J I, Park D J. Fast image segmentation based on multi-resolution analysis and wavelets[J]. Pattern Recognition Letters.2003,24(16):2995-3006.
[78]Jianqing L, Yee-Hong Y. Multiresolution color image segmentation[J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on.1994,16(7):689-700.
[79]王跃宗,刘京会,李德胜.基于SLM显微立体视觉模型的3D微定位研究[J].计算机工程与应用.2005,41(20)：29-32.
[80]侯晓萍.显微立体视觉系统的标定与三维重建技术研究[D].河北工业大学,2004.
[81]Introduction to Stereomicroscopy[Z].
[82]张正友马颂德.计算机视觉：计算理论与算法基础[M].北京：科学出版社,2006.
[83]Dong-Min W, Dong-Chul P. An Efficient Method for Camera Calibration Using MultiLayer Perceptron Type Neural Network[C].2009.
[84]刘庆民.基于计算机视觉的小尺寸零件精密测量技术研究[D].吉林大学,2006.
[85]杨福生著.小波变换的工程分析与应用[M].北京：科学出版社,1999：271.
[86]丁兴号.基于小波变换的亚像素边缘检测[J].仪器仪表学报.2005,26(8)：801-804.
[87]Toussaint G T. SOLVING GEOMETRIC PROBLEMS WITH THE'ROTATING CALIPERS'.[Z]. Athens, Greece:1983n8.
[88]Carstensteger德,Markusulrich德,Christianwiedemann著德,等.机器视觉算法与应用[M].北京：清华大学出版社,2008：497.
[89]Goodson P Z K E. Subpixel displacement and deformation grandient measurement using digital image/speckle correlation(DISC)[J]. Optical Engineering.2001,8(40):1613-1620.
[90]Jean-Yves Bouguet. Pyramidal implementation of the Lucas Kanade feature tracker description of the algorithm [Z]. Intel Corporation-Microprocessor Research Labs,1999.
[2]Shaochen C. Nanomanufacturing:Challenges and opportunities from design to fabrication[C].2009.
[3]张力.激光干涉法进行正弦力校准研究[J].计量学报.2005(4)：337-342.
[4]王晓嘉,高隽,王磊.激光三角法综述[J].仪器仪表学报.2004：601-604.
[5]梁宜勇,杨国光,孙戎.激光直写调焦系统特性及离焦应用研究[J].浙江大学学报(工学版).2005(2)：102-105.
[6]张广军著.视觉测量[M].北京：科学出版社,2008：335.
[7]Xudong L, Guanghua Z, Shusheng B. Development of global vision system for biological automatic micro-manipulation system[C].2001.
[8]Kim N H, Bovik A C, Aggarwal S J. Shape description of biological objects via stereo light microscopy [J]. Systems, Man and Cybernetics, IEEE Transactions on.1990,20(2):475-489.
[9]Liao W H, Aggarwal S J, Aggarwal J K. Reconstruction of dynamic 3D structure of biological objects using stereo microscope images[J]. Machine Vision and Applications.1997,9(4):166-178.
[10]Sano T, Nagahata H, Endo H, et al. A visual feedback system for micromanipulation with stereoscopic microscope[C].1998.
[11]Schreier H W, Garcia D, Sutton M A. Advances in light microscope stereo vision[J]. Experimental Mechanics.2004,44(3):278-288.
[12]Lasson L S J M T. Microscopic 3-D displacement field measurements using digital speckle photogray[J]. Optical and Lasers in Engineering.2004,5(41):767-777.
[13]Bown M R, Macinnes J M, Allen R W K, et al. Three-dimensional, three-component velocity measurements using stereoscopic micro-PIV and PTV[J]. Measurement Science and Technology.2006, 17(8):2175-2185.
[14]陈立国,孙立宁,荣伟彬.基于显微视觉与微力觉柔顺混合控制的微操作机器人[J].高技术通讯.2003(12)：53-56.
[15]孙立宁,陈立国,刘品宽,等.微操作机器人显微视觉系统若干问题[J].光学精密工程.2002(2)：171-175.
[16]李丽宏,孙立新,安庆宾,等.基于双目显微立体视觉系统的研究[J].激光与光电子学进展.2003,40(9)：30-35.
[17]王跃宗.SLM显微立体视觉量化和三维数据重构研究[D].大连理工大学,2003.
[18]Li X, Wang X, Zhou Z, et al. On the architecture of the micro machine vision system[Z]. Changchun, China:2006 AFOSR Asia Office; Chinese Academy of Sciences, CAS, China; Chinese Academy of Engineering, CAE, China; Chinese Association of Science and Technology, CAST, China; European Optical Society, EOS.
[19]Branchitta F, Diani M, Corsini G, et al. Dynamic range compression and contrast enhancement in IR imaging systems[Z]. Florence, Italy:2007SPIEEurope.
[20]Pizer S M, Amburn E P, Austin J D, et al. ADAPTIVE HISTOGRAM EQUALIZATION AND ITS VARIATIONS.[J]. Computer vision, graphics, and image processing.1987,39(3):355-368.
[21]Pizer S M, Johnston R E, Ericksen J P, et al. Contrast-limited adaptive histogram equalization:Speed and effectiveness[Z]. Atlanta, GA, USA:1990,337-345.
[22]Reza A M. Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement[J]. Journal of VLSI Signal Processing Systems for Signal, Image, and Video Technology.2004,38(1):35-44.
[23]Jianmao X, Junzhong S, Changjiang Z. Non-linear Algorithm for Contrast Enhancement for Image Using Wavelet Neural Network[C].2006.
[24]G. Apostolopoulos E D. Local Adaptive Contrast Enhancement in Digital Images[Z].2007.
[25]Jin Y, Fayad L, Laine A. Contrast enhancement by multi-scale adaptive histogram equalization[Z]. San Diego, CA, United states:2001,206-213.
[26]Otsu. A Threshold Selection Method from Gray-Level Histograms[J]. Systems, Man and Cybernetics, IEEE Transactions on.1979,9(1):62-66.
[27]Pun T. ENTROPIC THRESHOLDING, A NEW APPROACH.[J]. Computer graphics and image processing.1981,16(3):210-239.
[28]Cao L, Shi Z K, Cheng E K W. Fast automatic multilevel thresholding method[J]. Electronics Letters. 2002,38(16):868-870.
[29]Kim B G, Shim J I, Park D J. Fast image segmentation based on multi-resolution analysis and wavelets[J]. Pattern Recognition Letters.2003,24(16):2995-3006.
[30]Liu Jianzhuang, Li Wenqing. Automatic thresholding of gray-level pictures via two-dimensional OTSU method[J]. Zidonghua Xuebao/Acta Automatica Sinica.1993,19(1):101-105.
[31]Jun Z, Jinglu H. Image segmentation based on 2D Otsu method with histogram analysis[Z]. Wuhan, Hubei, China:2008,105-108.
[32]Zhu N, Wang G, Yang G, et al. A fast 2D otsu thresholding algorithm based on improved histogram[Z]. Nanjing, China:2009,319-323.
[33]景晓军,蔡安妮,等.一种基于二维最大类间方差的图像分割算法[J].通信学报.2001,22(4)：71-76.
[34]郎咸朋,朱枫,郝颖明,等.基于积分图像的快速二维Otsu算法[J].仪器仪表学报.2009(1)：39-43.
[35]Bi Ying Wei, Qiu Tian Shuang. Adaptive image segmentation method based on a simplified PCNN[J]. Tien Tzu Hsueh Pao/Acta Electronica Sinica.2005,33(4):647-650.
[36]Chan P K, Cheng S H, Poon T C. Automated segmentation in confocal images using a density clustering method[J]. Journal of Electronic Imaging.2007,16(4).
[37]田军委,黄永宣,于亚琳.基于熵约束的快速FCM聚类多阈值图像分割算法[J].模式识别与人工智能.2008,21(2)：221-226.
[38]彭静,刘小明.基于数学形态学的显微图像分割算法[J].中国科技博览.2009(32)：146-147.
[39]何斌等编著VisualC++数字图像处理[M].北京：人民邮电出版社,2002：674.
[40]朱红高.图像边缘检测技术研究现状[J].制造业自动化.2010(1)：45-47.
[41]刘冲,夏泽邑,裴伟,等.SLM彩色显微图像的亚像素边缘检测方法[J].机械工程学报.2005,41(1)：71-76.
[42]黄向东,谭久彬.激光共焦显微图像多尺度边缘提取算法研究[J].光电子.激光.2005,16(4)：458-461.
[43]张雷,王秋光,戚基萍.基于模糊边缘检测的胸水癌细胞显微图像处理技术[J].哈尔滨理工大学学报.2007,12(1)：90-92.
[44]白建明,王之琼.分形理论在X光片图像边缘增强中的应用[J].黑龙江医药科学.2006,29(1)：78-79.
[45]刘新春,陈仕东,邹谋炎,等.基于局部直方图相关的造影图象边缘检测方法[J].中国图象图形学报.2000,5(9)：750-754.
[46]马苗,樊养余,谢松云,等.基于灰色系统理论的图象边缘检测新算法[J].中国图象图形学报：A辑.2003,8(10)：1136-1139.
[47]肖锋.基于BP神经网络的数字图像边缘检测算法的研究[J].西安科技大学学报.2005,25(3)：372-375.
[48]Abdel-Aziz Y I K H M. Direct linear transformation into object space coordinates in close-range photogrammetry[J]. Close-Range Photogrammetry.1971:1-18.
[49]Tsai R. A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses[J]. Robotics and Automation, IEEE Journal of.1987,3(4):323-344.
[50]Bacakoglu H, Kamel M S. A three-step camera calibration method[J]. Instrumentation and Measurement, IEEE Transactions on.1997,46(5):1165-1172.
[51]Zhang Z. A flexible new technique for camera calibration[J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on.2000,22(11):1330-1334.
[52]Danuser G. Photogrammetric calibration of a stereo light microscope[J]. Journal of Microscopy.1999, 193(1):62-83.
[53]Zhou Y, Nelson B J. Calibration of a parametric model of an optical microscope[J]. Optical Engineering. 1999,38(12):1989-1995.
[54]J F. On chromatic and geometrical calibration[D]. Lyngby:Technical University of Denmark,1999.
[55]Zhang D, Luo M, Arola D D. Displacement/strain measurements using an optical microscope and digital image correlation[J]. Optical Engineering.2006,45(3).
[56]王跃宗,刘冲,王立鼎,等.微操作成像系统中体视显微镜的参数标定[J].机械工程学报.2003(9)： 81-86.
[57]Kiyono S, Gao W, Zhang S, et al. Self-calibration of a scanning white light interference microscope[J]. Optical Engineering.2000,39(10):2720-2725.
[58]王兵振,刘文耀.一种显微图像测量系统的标定方法[J].光电工程.2006,33(2)：119-122.
[59]郭宝龙,朱娟娟,孙伟.电子稳像的分层位平面全局运动估计算法[J].光子学报.2009(11)：2993-2998.
[60]郑志彬,叶中付.基于相位相关的图像配准算法[J].数据采集与处理.2006,21(4)：444-449.
[61]王晓燕,郑建宏.用于快速块匹配运动估计的自适应十字模式搜索[J].电子与信息学报.2005,27(1)：104-107.
[62]于辉,左洪福,黄传奇.基于立体视觉技术的磨粒显微测量方法[J].交通运输工程学报.2003(1)：88-92.
[63]陈治,胡晓东,傅星,等.基于块匹配的MEMS平面纳米精度运动测量[J].光学精密工程.2008(3)：505-510.
[64]高思田,陈治,胡晓东,等.纳米精度MEMS平面运动测量技术[J].天津大学学报.2008(4)：423-428.
[65]陈治,朱洪程,胡晓东,等.基于相位相关技术的MEMS旋转角度高分辨力测量[J].光学精密工程.2009(8)：1884-1889.
[66]Wei Sun C Q. Dynamic characterization of a microgyroscope by digital image spectrum correlation[J]. Optical engineering.2008,47(3).
[67]胡晓东,李晓俊,孙彬,等.MEMS微结构旋转角度的快速测量方法[J].纳米技术与精密工程.2009(4)：328-332.
[68]冷汹涛.基于光流技术和滤波器方法的多尺度微运动测量[J].西安科技大学学报.2009,29(3)：364-368.
[69]林晓春,李存志.采用AMMDFM方法对水下微光图像进行增强处理[J].西安电子科技大学学报(自然科学版).2006(4)：543-546.
[70]于起峰等著.基于图像的精密测量与运动测量[M].北京：科学出版社,2002：217.
[71]卢清华,张宪民,范彦斌.计算机微视觉微运动测量系统图像噪声分析[J].机械设计与制造.2008(4)：90-92.
[72]Rafaelc美.数字图像处理[M].北京：电子工业出版社,2007.
[73]Barnard K A F B. Investigations into multi-scaleretinex[J]. Colour Imaging Vision and Technology.1999: 17-36.
[74]Chunming L, Gatenby C, Li W, et al. A robust parametric method for bias field estimation and segmentation of MR images[C].2009.
[75]Zheng Y, Grossman M, Awate S P, et al. Automatic correction of intensity nonuniformity from sparseness of gradient distribution in medical images[Z]. London, United kingdom:2009,852-859.
[76]Olshausen B A, Field D J. Emergence of simple-cell receptive field properties by learning a sparse code for natural images[J]. Nature.1996,381(6583):607.
[77]Kim B G, Shim J I, Park D J. Fast image segmentation based on multi-resolution analysis and wavelets[J]. Pattern Recognition Letters.2003,24(16):2995-3006.
[78]Jianqing L, Yee-Hong Y. Multiresolution color image segmentation[J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on.1994,16(7):689-700.
[79]王跃宗,刘京会,李德胜.基于SLM显微立体视觉模型的3D微定位研究[J].计算机工程与应用.2005,41(20)：29-32.
[80]侯晓萍.显微立体视觉系统的标定与三维重建技术研究[D].河北工业大学,2004.
[81]Introduction to Stereomicroscopy[Z].
[82]张正友马颂德.计算机视觉：计算理论与算法基础[M].北京：科学出版社,2006.
[83]Dong-Min W, Dong-Chul P. An Efficient Method for Camera Calibration Using MultiLayer Perceptron Type Neural Network[C].2009.
[84]刘庆民.基于计算机视觉的小尺寸零件精密测量技术研究[D].吉林大学,2006.
[85]杨福生著.小波变换的工程分析与应用[M].北京：科学出版社,1999：271.
[86]丁兴号.基于小波变换的亚像素边缘检测[J].仪器仪表学报.2005,26(8)：801-804.
[87]Toussaint G T. SOLVING GEOMETRIC PROBLEMS WITH THE'ROTATING CALIPERS'.[Z]. Athens, Greece:1983n8.
[88]Carstensteger德,Markusulrich德,Christianwiedemann著德,等.机器视觉算法与应用[M].北京：清华大学出版社,2008：497.
[89]Goodson P Z K E. Subpixel displacement and deformation grandient measurement using digital image/speckle correlation(DISC)[J]. Optical Engineering.2001,8(40):1613-1620.
[90]Jean-Yves Bouguet. Pyramidal implementation of the Lucas Kanade feature tracker description of the algorithm [Z]. Intel Corporation-Microprocessor Research Labs,1999.