图像测量技术及其应用研究
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摘要
图像测量是近年来在测量领域新兴的一种高性能测量技术,在机器视觉、图像匹配、图像检测和模式识别中都有重要应用。它以图像处理技术为基础,将光学、电子技术、计算机技术、几何测量和信号与信息处理技术等多种现代技术融为一体,构成综合体的测量系统。图像测量就是把测量对象的图像作为检测和传递信息的载体加以利用的精确测量技术,可有效地进行定位和识别。
本文以图像处理技术为基础,深入研究了图像测量系统的总体方案及其实现技术。分别从数字图像测量系统的建立、图像预处理、直线的Hough变换、最小二乘法拟合、亚像素边缘定位等进行了理论分析和探讨,并在此基础上,构造了图像测量系统。用实验系统完成了对基本图像的线、圆、椭圆、不规则元件和边界分割等进行测量,对存在的问题和产生的误差进行了理论分析。实验结果表明,本文中提出的方法理论上正确,方法切实可行。采用通常非专用摄像机构造的系统,图像测量系统可以达到0.05-0.1mm精度,可以在一般精度要求系统中应用。
Image measuring is a novel measure technique of high quality in the fields of measuring recently, and it has played an important role in machine video, image matching, and image detecting and pattern recognition. On the basis of image processing, the modern techniques of optics, electronics, computer, geometric, signal and information processing is melt into it as a whole to construct a measuring system. Image measuring is that the measured object image is considered as media to detect and transmit the measured object information, which takes advantages of accurately measuring technique, and the position and recognition of objects is efficiently preceded.
On the basis of image processing, a total device and it's implementation applications of image measuring system is deeply researched in this paper. Digital image measuring system setup, image preprocessing, line Hough transform, least-square fitting, sub-pixel edge location and so on are studied and analyzed, then an image measuring system is constructed on the basis of these work. The basic image elements of line, circle, ellipse, non-normal device and border segmentation are measured by our experimental system, and the existed problems and error derived from experiments are analyzed in theory. The experimental results show that the measuring method is correct in theory and can be implemented. When a normal camera is used to construct the image measuring system, a measure resolution of 0.05-0.10mm can be got by this system, and it can be used in normal applications.
本文以图像处理技术为基础,深入研究了图像测量系统的总体方案及其实现技术。分别从数字图像测量系统的建立、图像预处理、直线的Hough变换、最小二乘法拟合、亚像素边缘定位等进行了理论分析和探讨,并在此基础上,构造了图像测量系统。用实验系统完成了对基本图像的线、圆、椭圆、不规则元件和边界分割等进行测量,对存在的问题和产生的误差进行了理论分析。实验结果表明,本文中提出的方法理论上正确,方法切实可行。采用通常非专用摄像机构造的系统,图像测量系统可以达到0.05-0.1mm精度,可以在一般精度要求系统中应用。
Image measuring is a novel measure technique of high quality in the fields of measuring recently, and it has played an important role in machine video, image matching, and image detecting and pattern recognition. On the basis of image processing, the modern techniques of optics, electronics, computer, geometric, signal and information processing is melt into it as a whole to construct a measuring system. Image measuring is that the measured object image is considered as media to detect and transmit the measured object information, which takes advantages of accurately measuring technique, and the position and recognition of objects is efficiently preceded.
On the basis of image processing, a total device and it's implementation applications of image measuring system is deeply researched in this paper. Digital image measuring system setup, image preprocessing, line Hough transform, least-square fitting, sub-pixel edge location and so on are studied and analyzed, then an image measuring system is constructed on the basis of these work. The basic image elements of line, circle, ellipse, non-normal device and border segmentation are measured by our experimental system, and the existed problems and error derived from experiments are analyzed in theory. The experimental results show that the measuring method is correct in theory and can be implemented. When a normal camera is used to construct the image measuring system, a measure resolution of 0.05-0.10mm can be got by this system, and it can be used in normal applications.
引文
[1]美Rafael C.Gonzalez,Richard E.Woods and Steven L.Eddins 著 阮秋琪等译。Digital Image Processing Using MATLAB:数字图像处理(MATLAB版)北京:电子工业出版社,2005.9
[2]W.H.Peters,W.F.Ranson.Digital imaging techniques in experiment.[J]Opt.Engng,1982,21(3):427-43
[3]吴晓波,钟先信,刘厚泉,等.高精度图像测量系统[J].光学精密工程,1995,3(1):28-33.
[4]黄志辉,龙赛琼等.基于图像处理技术的机械零件小孔径尺寸测量方法[J].计算机测量与控制,2006.14(5)592-594.
[5]张国玉.非接触光电尺寸检测研究.敏通科技.1996,9:34-36.
[6]曾延安.CCD在光谱分析系统中的应用研究[J].光学技术.1998(4):3-4
[7]陈爱弟,王信义,王忠民,贾玉平零件尺寸的在线检测方法研究:上海大学-2345机器人中心
[8]薛琴1 陈玮1 蒋雯2 数字图像在零件尺寸测量中的应用 计算机与数字工程 第35卷(2007)第7期
[9]田捷,沙飞,张新生.实用图像分析与处理技术[M].北京:电子工业出版社,1995.
[10]于起峰,陆宏伟等.基于图像的精密测量与运动测量.北京:科学出版社.2002:28-33
[11]娄小平,吕乃光等.基于机器视觉复杂形状工件的非接触测量方法[J].工具技术.2005,Vol.39(7):82-85.
[12]Kapur J N,Sahoo P K,Wong A K C.A new method for grey-level picture thresholding using the entropy of the histogram[J].Computer Vision,Graphics,and Image Processing,1985,29(3):273-285.
[13]OTSU N.A threshold selection method from gray-level histograms[J].IEEE Trans SMC,1979,9(1):62-69.
[14]G.L.Foresti & C.Regazzoni,A hierarchical approach to feature extraction and grouping,IEEE IP Vol.9 No.6,2000
[15]曾欢,王浩.图像边缘检测算法的性能比较与分析[J].现代电子技术,2006,14:53-55.
[16]Fleck M M.Some defects in finite-difference edge finders[J].IEEE Trans on PAMI,1992,14(3):337-345.
[17]Xiangdong Liu,R.W.Ehrich.Subpixel edge location in binary images using ditherin[J].IEEE Trans on PAMI,1995,17(6):629-634.
[18]王郑耀.数字图像的边缘检测[M].西安:西安交通大学出版社,1990:75-149.
[19]吴晓波.图像边缘特性分析[J].光学精密工程,1999,7(1):59-62.
[20]AlyA.Faragand E.J.Delp,Edge linking by Sequential search,PR Vol.28.No.5,611-633,1995
[21]L.Lam,S.W.Lee and C.Y.Suen,Thinning Methodologies-A Comprehensive survey,IEEE PAMI.Vol.14.No.9.PP.869-885,1992
[22]E.R.Davies and A.P.N.Plammer,Thinning algorithms:A critique and a new methodology,PR.Vol.14.No.1.PP.53-63.1981
[23]B.Jang & R.T.Chin,Analysis of thinning algorithm using mathematical morphology,IEEE PAMI-12,No.6,1990
[24]N Guil,E.L Zapata.Lower Order Circle and Ellipse Hough Transforms[J].Pattern Recognition,1997,30(10):729-744.
[25]胡占义,杨长江,马颂德.Hough 变换的新定义[J].计算机学报,1997,20(8):744-752.
[26]Ya-Jun Zhang,Zhi-Qiang Liu.Curve detection using a new clustering approach in the Hough space[A].Procedding of IEEE International Conference on System,Man and Cybernetics[C].Nashiville,Tennessee:IEEE Computer Society Press,2000,4:2746-2751.
[27]O.Chutatape,L.F.Guo,A modified Hough transform for line detection and its performance.PR 32(1999)
[28]V.F.Leavers,The dynamic generaliredHough transform;Its relationship to the probabilistic Hough transforms and an application to the concurrent detection of circles and ellipses,CVGIP:IU vol.56.No.3.381-398,1992
[29]K Rangarajan,M.Shah & D.V.Brackle,Optimal Corner Detector,CVGIP vol.48.No.2230-245.1989
[30]S.Ghosal & R.Mehrotra,A moment-based unified approach to image feature detection,IEEE IP,Vol.6,No.6,1997
[31]Kimme C,Ballard D H,Sklansky J.Finding Circles by An Array of Accumulators[J].Communications of the ACM,1975(18):120-122.
[2]W.H.Peters,W.F.Ranson.Digital imaging techniques in experiment.[J]Opt.Engng,1982,21(3):427-43
[3]吴晓波,钟先信,刘厚泉,等.高精度图像测量系统[J].光学精密工程,1995,3(1):28-33.
[4]黄志辉,龙赛琼等.基于图像处理技术的机械零件小孔径尺寸测量方法[J].计算机测量与控制,2006.14(5)592-594.
[5]张国玉.非接触光电尺寸检测研究.敏通科技.1996,9:34-36.
[6]曾延安.CCD在光谱分析系统中的应用研究[J].光学技术.1998(4):3-4
[7]陈爱弟,王信义,王忠民,贾玉平零件尺寸的在线检测方法研究:上海大学-2345机器人中心
[8]薛琴1 陈玮1 蒋雯2 数字图像在零件尺寸测量中的应用 计算机与数字工程 第35卷(2007)第7期
[9]田捷,沙飞,张新生.实用图像分析与处理技术[M].北京:电子工业出版社,1995.
[10]于起峰,陆宏伟等.基于图像的精密测量与运动测量.北京:科学出版社.2002:28-33
[11]娄小平,吕乃光等.基于机器视觉复杂形状工件的非接触测量方法[J].工具技术.2005,Vol.39(7):82-85.
[12]Kapur J N,Sahoo P K,Wong A K C.A new method for grey-level picture thresholding using the entropy of the histogram[J].Computer Vision,Graphics,and Image Processing,1985,29(3):273-285.
[13]OTSU N.A threshold selection method from gray-level histograms[J].IEEE Trans SMC,1979,9(1):62-69.
[14]G.L.Foresti & C.Regazzoni,A hierarchical approach to feature extraction and grouping,IEEE IP Vol.9 No.6,2000
[15]曾欢,王浩.图像边缘检测算法的性能比较与分析[J].现代电子技术,2006,14:53-55.
[16]Fleck M M.Some defects in finite-difference edge finders[J].IEEE Trans on PAMI,1992,14(3):337-345.
[17]Xiangdong Liu,R.W.Ehrich.Subpixel edge location in binary images using ditherin[J].IEEE Trans on PAMI,1995,17(6):629-634.
[18]王郑耀.数字图像的边缘检测[M].西安:西安交通大学出版社,1990:75-149.
[19]吴晓波.图像边缘特性分析[J].光学精密工程,1999,7(1):59-62.
[20]AlyA.Faragand E.J.Delp,Edge linking by Sequential search,PR Vol.28.No.5,611-633,1995
[21]L.Lam,S.W.Lee and C.Y.Suen,Thinning Methodologies-A Comprehensive survey,IEEE PAMI.Vol.14.No.9.PP.869-885,1992
[22]E.R.Davies and A.P.N.Plammer,Thinning algorithms:A critique and a new methodology,PR.Vol.14.No.1.PP.53-63.1981
[23]B.Jang & R.T.Chin,Analysis of thinning algorithm using mathematical morphology,IEEE PAMI-12,No.6,1990
[24]N Guil,E.L Zapata.Lower Order Circle and Ellipse Hough Transforms[J].Pattern Recognition,1997,30(10):729-744.
[25]胡占义,杨长江,马颂德.Hough 变换的新定义[J].计算机学报,1997,20(8):744-752.
[26]Ya-Jun Zhang,Zhi-Qiang Liu.Curve detection using a new clustering approach in the Hough space[A].Procedding of IEEE International Conference on System,Man and Cybernetics[C].Nashiville,Tennessee:IEEE Computer Society Press,2000,4:2746-2751.
[27]O.Chutatape,L.F.Guo,A modified Hough transform for line detection and its performance.PR 32(1999)
[28]V.F.Leavers,The dynamic generaliredHough transform;Its relationship to the probabilistic Hough transforms and an application to the concurrent detection of circles and ellipses,CVGIP:IU vol.56.No.3.381-398,1992
[29]K Rangarajan,M.Shah & D.V.Brackle,Optimal Corner Detector,CVGIP vol.48.No.2230-245.1989
[30]S.Ghosal & R.Mehrotra,A moment-based unified approach to image feature detection,IEEE IP,Vol.6,No.6,1997
[31]Kimme C,Ballard D H,Sklansky J.Finding Circles by An Array of Accumulators[J].Communications of the ACM,1975(18):120-122.