基于数字摄影测量的大尺寸精密测试技术研究
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摘要
随着现代工业的发展,各个领域中的生产和工程都已对大尺寸测量提出了期望和明确要求,数字摄影测量技术已广泛应用于大尺寸精密测量中。工业数字摄影测量是建立在专业像机摄影和计算机图像处理基础上的一门新兴检测技术,其研究的重点是物体的几何尺寸及物体在空间的位置、姿态等。数字成像器件模型及标定和高精度亚象素定位技术是数字摄影测量中的核心技术。
本文重点分析了数字摄影测量的基本原理,结合数字摄影的成像模型,严格推导了摄影测量学中的共线方程。首先,通过对数字图像处理技术、Hough变换的基本原理和亚像素定位技术的深入研究,提出了一种基于Hough变换的用于检测直线和圆的亚像素定位算法;然后通过对经典模型和标定方法研究,提出了一个新的标定模型和基于二维直接线性变换的修正、求解方法。接下来,对亚像素定位算法和摄像机参数标定算法进行了仿真和实验,效果良好,亚像素定位精度能达到0.02pixel,摄像系统标定误差控制在0.005mm以内。最后,结合图像测量技术和本文的研究成果,实现了一套数字摄影测量系统。
With the development of modern industry, large-size measurements have been made clear expectations and requirements in various fields of production and engineering, digital photogrammetry technology has been widely used in large-size precision measurement. Industrial digital photogrammetry is a new detection technology which is based on the professional camera photography and computer image processing, its research focused on the geometry size and location of objects in space, posture and so on. Digital imaging device model and the calibration and high precision sub-pixel positioning is the core technology in digital photogrammetry.
This article focuses on the analysis of basic principles of digital photogrammetry, combined with digital photography imaging model, strictly derived photogrammetry in the total line equation. First, through the deep study of digital image processing techniques, the basic principle of Hough transform and sub-pixel positioning technology, a new method is proposed based on Hough transform for detecting straight lines and circular sub-pixel location algorithm; Second, through the research on classical model and calibration method, a new calibration model and its amendment, solution method which is based on two-dimensional direct linear transformation is proposed. Next, pairs of sub-pixel location algorithm and camera calibration algorithm have been checked by simulation and experiment,its results were very good, sub-pixel positioning accuracy can reach 0.02pixel, camera system calibration error can control within 0.005mm. Finally, with the combination of image measurement techniques and the results of the research of this paper, designed and implemented a set of digital photogrammetry measurement system.
本文重点分析了数字摄影测量的基本原理,结合数字摄影的成像模型,严格推导了摄影测量学中的共线方程。首先,通过对数字图像处理技术、Hough变换的基本原理和亚像素定位技术的深入研究,提出了一种基于Hough变换的用于检测直线和圆的亚像素定位算法;然后通过对经典模型和标定方法研究,提出了一个新的标定模型和基于二维直接线性变换的修正、求解方法。接下来,对亚像素定位算法和摄像机参数标定算法进行了仿真和实验,效果良好,亚像素定位精度能达到0.02pixel,摄像系统标定误差控制在0.005mm以内。最后,结合图像测量技术和本文的研究成果,实现了一套数字摄影测量系统。
With the development of modern industry, large-size measurements have been made clear expectations and requirements in various fields of production and engineering, digital photogrammetry technology has been widely used in large-size precision measurement. Industrial digital photogrammetry is a new detection technology which is based on the professional camera photography and computer image processing, its research focused on the geometry size and location of objects in space, posture and so on. Digital imaging device model and the calibration and high precision sub-pixel positioning is the core technology in digital photogrammetry.
This article focuses on the analysis of basic principles of digital photogrammetry, combined with digital photography imaging model, strictly derived photogrammetry in the total line equation. First, through the deep study of digital image processing techniques, the basic principle of Hough transform and sub-pixel positioning technology, a new method is proposed based on Hough transform for detecting straight lines and circular sub-pixel location algorithm; Second, through the research on classical model and calibration method, a new calibration model and its amendment, solution method which is based on two-dimensional direct linear transformation is proposed. Next, pairs of sub-pixel location algorithm and camera calibration algorithm have been checked by simulation and experiment,its results were very good, sub-pixel positioning accuracy can reach 0.02pixel, camera system calibration error can control within 0.005mm. Finally, with the combination of image measurement techniques and the results of the research of this paper, designed and implemented a set of digital photogrammetry measurement system.
引文
[1]叶声华,邾继贵.大空间坐标尺寸测量研究的现状与发展.计量学报,2008,9:1-6。
[2]黄桂平.数字近景工业摄影测量关键技术研究与应用.天津:天津大学博士论文,2005。
[3] Chin,T.T. and Harlow, C.A. Automated visual inspection a survey.IEEE Trans On PAMI, 1989:557-573.
[4]邾继贵,叶声华.工业现场近景数字摄影视觉精密测量.地理空间信息,2004,12:11-14。
[5] Fraser,C.S Digital camera self-calibration ISPRS J. Photogramm And Remote Sensing,52,1997.
[6]王保丰.高精度数字摄影测量技术在50米大型天线中的应用.精密与大型工程测量技术研讨交流会论文集,2006:221-229。
[7]黄桂平.大型网状天线型面检测技术与工程实践.2006年空间电子学学术年会论文集,2006:399-403。
[8] K.Edmundson,L Baker Photogrammetric measurement of the Arecibo primary reflector surface the Coordinate Measurement Systems Committee Conf,Albuquerque,NM,2001.8:l 3-l7.
[9]金超.DNS大天线的摄影测量.河北:中国电子科技集团公司第五十四研究所。
[10]黎绍发.基于计算机视觉图像精密测量的关键技术研究.华南理工大学博士生论文,2004。
[11]邾继贵.基于近景数字摄影的坐标精密测量关键技术研究.计量学报,2005-7:207-211。
[12]于起峰,陆宏伟,刘肖琳.基于图像的精密测量与运动测量.北京:科学出版社,2002。
[13] Kenneth R.Castleman著,朱志刚等译.数字图像处理.北京:电子工业出版社,2004。
[14] Henri Maitre著,孙洪译.现代数字图像处理.北京:电子工业出版社,2006:190-210。
[15] A.Soini. Machine vision technology take-up in industrial applications. Image and Signal Processing and Analysis,2001:332~338.
[16] Prager J M. Extracting and labeling boundary in natural scenes,IEEE PAMI.1980,l2:16-27.
[17] Nalwa V.S. On Detecting Edges .IEEE Trans on Pattern Analysis and Machine Intelligence, 1986,8, (8) :669-714.
[18] Sobel, Cameral Model and Machine Perception, Ph.D dissertation, Stanford University.
[19] Englander A Expanding machine vision gauging with sub-pixel techniques. Senaors-J:Machine perception,1987.4(6):9-18.
[20] Prager J M. Extracting and labeling boundary in natural scenes,IEEE PAMI. 1980,l2:16-27.
[21]范生宏.Canny算子对人工标志中心的亚像素精度定位.测绘科学技术学报, 2006-2:76-78。
[22]王成亮.亚像素定位的关键问题研究.海洋测绘,2007-1:70-73。
[23] Jensen K,Anastassiou D.Subpixel Edge Localization and the Interpolation of Still Images. IEEE Trans. on IP ,1995 ,4 (3) :285～295.
[24]李强兵,刘文予.基于Hough变换的快速矩形检测算法.微计算机信息,2007,23(11-1):248-250。
[25]刘良江,王耀南.一种基于Hough变换的圆的检测方法.湖南:湖南大学,2007。
[26]周富强.CCD摄像机快速标定技术.光学精密工程,2000:96-100。
[27]张洪涛.基于网格靶标快速高精度摄像机标定方法.光电工程,2006:57-60。
[28]邱茂林,马颂德,李毅.计算机视觉中摄像机标定综述.自动化学报,2000,26(1):43-55。
[29]段发阶.CCD摄像机参数标定新技术.计量学报,1997:296-299。
[30]唐阳山.交通事故摄影测量中相机标定的扩展两步法.交通运输工程学报,2007:81-84。
[31] Dainis A,Juberts M. Accurate remote measurement of robot trajectory motion. In:Proc. Int. Conference on Robotics and Automation. 1985.92-99.
[32] BrownDC. Decentering Distortion of Lenses. Photogrammetric Eng.Remote Sensing 1966.444-462.
[33] Wong KW. Mathematical foundation and digital analysis in close-Range photogrammetry. In:Proc.13th Congress of the Int Society for Photogrammetry. 1976,1355-1373.
[34]王欣.摄影测量在数字辅助直播系统中的应用.西安:西安电子科技大学硕士论文,2008。
[35]杨化超,邓喀中.利用2维DLT和共线方程分解相机外方位元素.测绘科学技术学报,2006-6:232-234。
[36]吴晓波,杨永琴.图像测量技术的新应用[J].光学精密工程,1998:6-10。
[37]容观澳.计算机图象处理.北京:清华大学出版社.2000:6-10。
[38]王建民,浦昭邦,刘国栋等.提高图像测量系统精度的细分算法的研究.光学精密工程,1998,6(4):44-50。
[39] Beghdadi A, Negrate A L. Contrast enhancement technique based on local detection of edge .Computer Vision,Graphics and Image Processing, 1989,462, 46(2) :162-174。
[40]戴华.矩阵论.北京:科学出版社,2001:3。
[2]黄桂平.数字近景工业摄影测量关键技术研究与应用.天津:天津大学博士论文,2005。
[3] Chin,T.T. and Harlow, C.A. Automated visual inspection a survey.IEEE Trans On PAMI, 1989:557-573.
[4]邾继贵,叶声华.工业现场近景数字摄影视觉精密测量.地理空间信息,2004,12:11-14。
[5] Fraser,C.S Digital camera self-calibration ISPRS J. Photogramm And Remote Sensing,52,1997.
[6]王保丰.高精度数字摄影测量技术在50米大型天线中的应用.精密与大型工程测量技术研讨交流会论文集,2006:221-229。
[7]黄桂平.大型网状天线型面检测技术与工程实践.2006年空间电子学学术年会论文集,2006:399-403。
[8] K.Edmundson,L Baker Photogrammetric measurement of the Arecibo primary reflector surface the Coordinate Measurement Systems Committee Conf,Albuquerque,NM,2001.8:l 3-l7.
[9]金超.DNS大天线的摄影测量.河北:中国电子科技集团公司第五十四研究所。
[10]黎绍发.基于计算机视觉图像精密测量的关键技术研究.华南理工大学博士生论文,2004。
[11]邾继贵.基于近景数字摄影的坐标精密测量关键技术研究.计量学报,2005-7:207-211。
[12]于起峰,陆宏伟,刘肖琳.基于图像的精密测量与运动测量.北京:科学出版社,2002。
[13] Kenneth R.Castleman著,朱志刚等译.数字图像处理.北京:电子工业出版社,2004。
[14] Henri Maitre著,孙洪译.现代数字图像处理.北京:电子工业出版社,2006:190-210。
[15] A.Soini. Machine vision technology take-up in industrial applications. Image and Signal Processing and Analysis,2001:332~338.
[16] Prager J M. Extracting and labeling boundary in natural scenes,IEEE PAMI.1980,l2:16-27.
[17] Nalwa V.S. On Detecting Edges .IEEE Trans on Pattern Analysis and Machine Intelligence, 1986,8, (8) :669-714.
[18] Sobel, Cameral Model and Machine Perception, Ph.D dissertation, Stanford University.
[19] Englander A Expanding machine vision gauging with sub-pixel techniques. Senaors-J:Machine perception,1987.4(6):9-18.
[20] Prager J M. Extracting and labeling boundary in natural scenes,IEEE PAMI. 1980,l2:16-27.
[21]范生宏.Canny算子对人工标志中心的亚像素精度定位.测绘科学技术学报, 2006-2:76-78。
[22]王成亮.亚像素定位的关键问题研究.海洋测绘,2007-1:70-73。
[23] Jensen K,Anastassiou D.Subpixel Edge Localization and the Interpolation of Still Images. IEEE Trans. on IP ,1995 ,4 (3) :285～295.
[24]李强兵,刘文予.基于Hough变换的快速矩形检测算法.微计算机信息,2007,23(11-1):248-250。
[25]刘良江,王耀南.一种基于Hough变换的圆的检测方法.湖南:湖南大学,2007。
[26]周富强.CCD摄像机快速标定技术.光学精密工程,2000:96-100。
[27]张洪涛.基于网格靶标快速高精度摄像机标定方法.光电工程,2006:57-60。
[28]邱茂林,马颂德,李毅.计算机视觉中摄像机标定综述.自动化学报,2000,26(1):43-55。
[29]段发阶.CCD摄像机参数标定新技术.计量学报,1997:296-299。
[30]唐阳山.交通事故摄影测量中相机标定的扩展两步法.交通运输工程学报,2007:81-84。
[31] Dainis A,Juberts M. Accurate remote measurement of robot trajectory motion. In:Proc. Int. Conference on Robotics and Automation. 1985.92-99.
[32] BrownDC. Decentering Distortion of Lenses. Photogrammetric Eng.Remote Sensing 1966.444-462.
[33] Wong KW. Mathematical foundation and digital analysis in close-Range photogrammetry. In:Proc.13th Congress of the Int Society for Photogrammetry. 1976,1355-1373.
[34]王欣.摄影测量在数字辅助直播系统中的应用.西安:西安电子科技大学硕士论文,2008。
[35]杨化超,邓喀中.利用2维DLT和共线方程分解相机外方位元素.测绘科学技术学报,2006-6:232-234。
[36]吴晓波,杨永琴.图像测量技术的新应用[J].光学精密工程,1998:6-10。
[37]容观澳.计算机图象处理.北京:清华大学出版社.2000:6-10。
[38]王建民,浦昭邦,刘国栋等.提高图像测量系统精度的细分算法的研究.光学精密工程,1998,6(4):44-50。
[39] Beghdadi A, Negrate A L. Contrast enhancement technique based on local detection of edge .Computer Vision,Graphics and Image Processing, 1989,462, 46(2) :162-174。
[40]戴华.矩阵论.北京:科学出版社,2001:3。