空间多目标运动参数图像测试技术研究
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摘要
计算机视觉技术通过分析空间目标的多幅图像得到目标的空间信息,其目标是实现三维场景的感知、识别和理解。现有的计算机视觉技术主要采用复杂的相机标定技术实现,测量精度取决于相机标定的精度,测量精度不易控制,在实际应用中操作复杂等缺点。
本论文借鉴计算机视觉技术的特点建立了适用于空间静态目标测量和动态目标测量的测量系统,利用图像处理技术实现了空间目标测量的自动化。本论文建立的测量系统不需要复杂的相机标定,同时具有对测量场地要求低、实现简单、精度高、稳定性高、应用范围广的特点。
本文首先分析了双目立体视觉技术,分别建立了双相机光轴面垂直测量系统和优化双相机测量系统,同时为测量系统建立了完整的数学模型,两种测量系统不需要在测量时对相机参数进行标定,对相机放置位置要求较低,适用于测量环境不确定的目标测量;其次,分析了相机标定方法和相机标定时标定点的设置方法,根据标定标杆和标定模版的特点建立了标定模块,它具有标定点空间相对位置稳定,且满足现有标定算法的要求;然后,分析了图像特征点检测技术和特征匹配技术;最后本论文通过静态建筑物中目标点的空间位置测量验证了双相机光轴面垂直测量系统的正确性,通过静态建筑物中目标点的空间位置测量和空间多目标运动速度和轨迹的测量验证了优化双相机测量系统的正确性,通过实验结果证明了本论文提出的测量系统满足实现空间多目标运动参数的测量要求。
Computer vision is the method to acquire the 3D space information of objects by multiple images. The existing computer vision technology mainly uses the complex camera calibration to realize survey, the measuring accuracy is decided by the precision of camera calibration, and not easy to control, operates complex in the practical application.
The dissertation use computer vision technology characteristic to establish two kinds of novel simplified measurement systems, which is suitable to survey spatial static goal and dynamic goal, has realized the automatic measure by image processing technology. The measurement systems of this dissertation do not need the complex camera calibration, and have characteristic of low requirements for measuring position, simple, high precision, high stability, wide application scope.
First,the binocular stereo vision measurement systems are studied in dissertation, two kinds of novel simplified dual-camera measurement systems, namely one system in which dual-camera measurement system of vertical camera axes’surface and the other in which excellent dual-camera measurement system, is proposed and the corresponding mathematic model is established. Second, the camera calibration methods and the establishment method of calibration points are studied in detail. Second, a calibration module is proposed, base on the characteristic of calibration pole and calibration template, which has stable location among points, and is suitable to the requirements of existing calibration algorithm, at the same time it is simply in using.Third, image feature detection technology and feature matching technology is studied in dissertation.The last, The dissertation test the dual-camera measurement system which has vertical surface of cameras axes is right by measuring the space points coordinates of building; test excellent dual-camera measurement system is right by measuring the space points coordinates in building and measuring speed and path of space multi-target; test the measurement can satisfies of measurement requirement of parameter for space moving multi-target by the experimental results.
本论文借鉴计算机视觉技术的特点建立了适用于空间静态目标测量和动态目标测量的测量系统,利用图像处理技术实现了空间目标测量的自动化。本论文建立的测量系统不需要复杂的相机标定,同时具有对测量场地要求低、实现简单、精度高、稳定性高、应用范围广的特点。
本文首先分析了双目立体视觉技术,分别建立了双相机光轴面垂直测量系统和优化双相机测量系统,同时为测量系统建立了完整的数学模型,两种测量系统不需要在测量时对相机参数进行标定,对相机放置位置要求较低,适用于测量环境不确定的目标测量;其次,分析了相机标定方法和相机标定时标定点的设置方法,根据标定标杆和标定模版的特点建立了标定模块,它具有标定点空间相对位置稳定,且满足现有标定算法的要求;然后,分析了图像特征点检测技术和特征匹配技术;最后本论文通过静态建筑物中目标点的空间位置测量验证了双相机光轴面垂直测量系统的正确性,通过静态建筑物中目标点的空间位置测量和空间多目标运动速度和轨迹的测量验证了优化双相机测量系统的正确性,通过实验结果证明了本论文提出的测量系统满足实现空间多目标运动参数的测量要求。
Computer vision is the method to acquire the 3D space information of objects by multiple images. The existing computer vision technology mainly uses the complex camera calibration to realize survey, the measuring accuracy is decided by the precision of camera calibration, and not easy to control, operates complex in the practical application.
The dissertation use computer vision technology characteristic to establish two kinds of novel simplified measurement systems, which is suitable to survey spatial static goal and dynamic goal, has realized the automatic measure by image processing technology. The measurement systems of this dissertation do not need the complex camera calibration, and have characteristic of low requirements for measuring position, simple, high precision, high stability, wide application scope.
First,the binocular stereo vision measurement systems are studied in dissertation, two kinds of novel simplified dual-camera measurement systems, namely one system in which dual-camera measurement system of vertical camera axes’surface and the other in which excellent dual-camera measurement system, is proposed and the corresponding mathematic model is established. Second, the camera calibration methods and the establishment method of calibration points are studied in detail. Second, a calibration module is proposed, base on the characteristic of calibration pole and calibration template, which has stable location among points, and is suitable to the requirements of existing calibration algorithm, at the same time it is simply in using.Third, image feature detection technology and feature matching technology is studied in dissertation.The last, The dissertation test the dual-camera measurement system which has vertical surface of cameras axes is right by measuring the space points coordinates of building; test excellent dual-camera measurement system is right by measuring the space points coordinates in building and measuring speed and path of space multi-target; test the measurement can satisfies of measurement requirement of parameter for space moving multi-target by the experimental results.
引文
[1] Linada G..Shapiro George C.Stockman 著.赵清洁等译.计算机视觉[M].北京:机械工业出版社,2005.
[2] 刘李杰,蔡德钧,翁南衫.一种面向运动的视频对象分割算法[J].计算机学报.2000,23(12):1326～1331
[3] Roberts,L.G. Machine perception of three-dimensional solids[J], In: Tippet J. T. ed.Optical and Electro-Optical Information Processing, 1965:159-197.
[4] Marr.D, Poggio.T. A Computational Theory of Human Stereo Vision[J], Proc R Soc Lond B Biol Sci. 1979 May 23;204(1156):301-28..
[5] 张晓玲. 远距离运动物体动态参数测试技术的理论和实验研究[D],[博士学位论文],天津大学,2005.
[6] Faig.W. Calibration of Close-Range Photogrammetry System: Mathematical Formulation[J]. Photometric Eng. Remote Sensing, 1975, 41(12):1479-1486.
[7] Y.I.Abdel-Aziz, H.M.Karara. Photogrammetric Potential of Nonmetric Cameras[J]. Civil Engineering Studies, Photogrammetry Series No.36, Urbana,Illinois, 1974,3:1~18.
[8] Dainis.A. Juberts.M. Accurate Remote Measurement of Robot Trajectory motion[J], Robotics and Automation. Proceedings. 1985 IEEE International Conference on,1985, 92-99.
[9] Yakimovsky Y, Cunningham R. A System for Extracting 3D[J], Image Processing, 1978,7:195~210.
[10] Tsai R. Y. An Efficient and Accurate Camera Calibration Technique for 3D Machine Vision[J], In Proc. IEEE Conference on Computer Vision and Patern Recognition, Miami Beach, FL, 1986:364-374.
[11] Grosky W. I, Tamburino L. A., A unified approach to the linear camera calibration problem[J], IEEE Trans. on PAMI-12, 1990, 663~671.
[12] Shin S. S., Hung Y. P., Lin W. S., Accurate Linear Technique for Camera CalibrationConsidering Lens Distortion by Solving an Eigenvalue Problem[J], Optical Engineering, 1993, 32(1): 138-149.
[13] Wang L. L., Tsai W. H., Camera Calibration by Vanishing Lines for 3D Computer Vision, IEEE Trans on PAMI-13, 1991.
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[15] A.Kuijper, L.M.J.Florack, On the Creations of Critical Points in Scale Space With Applications to Medical Image Analysis[J], UU-CS-2001-25 TR.
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[17] R.V.Ee,C.M.Schor,Unconstrained stereoscopic matching of lines[J], Vision Research, 2000,40:151~162.
[18] H.Loaiza,J.Triboulet,S.Lelandais. Matching Segments in Stereoscopic Vision[J], IEEE Instrumentation&Measurement Magazine,2001,3:37~42.
[19] B.J.Supa,W.N.Klariquist. Patch-based stereo in a general binocular viewing[J], Theory and Experiment, 1997, PAMI-19(3):247~253.
[20] Rosenfeld A, Weszka J S. An Improved Method of Angel Detection on Digital Curves[J]. IEEE Trans. Computers, 1975, C-24(9):940-941.
[21] Freeman H, Davis L S.A Corner Finding Algorithm for Chain Coded Curves[J].IEEETrans.Computers,1977,26(3):297-303.
[22] Asada H, Brady M. The Curvature Primal Sketch[J]. IEEE Trans. PAMI, 1986, 8(1): 2-14.
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[30] L.Kitchen, A.Rosenfeld. Analysis of Gray Level Corner Detection[J]. Pattern Recognition Letters, 1999,20(2):149-162.
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[34] Miroslav.Trajkovic,Mark.Hedley. Fast Corner Detection[J]. Image Vision Computing, 1998, 16(1):75-87.
[35] Smith S M, Brady J M. Susan: A New Approach to Low Level Image Processing[J]. International Journal of Computer Vision, 1997,23(1):45-78.
[36] Y.Ohta,T.Kanade. Stereo by Intra and Inter-Scanline Search Using Dynamic Programming[J], IEEE Transaction Pattern Analysis and Machine Intelligence, 1985,7.
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[41] Veksler.O. Fast variable window for stereo correspondence using integral images[J], Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on.
[42] Marr.D,PoggioT. Cooperative Computation of a Theory of Human Stereo Vision[J], Phil Trans. Roy. Soc., 1981, B292:217-253.
[43] Marr.D,PoggioT. Cooperative Computation of Stereo Disparity[J], Science 15 October 1976:Vol.194.no.4262,pp.283-287.
[44] Grimson,William.Eric.Leifur. From Image to Surface: A computational Study of the Human Early Visual System[J], Cambridge, MA, MIT Press, 1981:247-267.
[45] W.E.L.Grimson. A Computer Implementation of a Theory of Human Stereo Vision[J], Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, Vol.292, No.1058,1981:217-253.
[46] R.D.Arnold, T.O.Binford. Geometric Constraints in Stereo Vision[J], In Image Processing for Missile Guidance, 1980:281-292.
[47] R.D.Arnold. Automated Stereo Perception[D], Ph.D. dissertation, Dept. of Computer Science, Stanford University, CA 1983.
[48] H.H. Baker, T.O. Binford. Depth From Edge and Intensity-Based Stereo[J], In Proc. 7th IJCAI,1981:631-636.
[49] H.H. Baker. Edge Based Stereo Correction[J], In Proc. Im. Understand Workshop, 1980:168-175.
[50] G.D. Forney Jr. The Viterbi Algorithm[J], Proc. IEEE, 1973,61:268-278.
[51] Y.Ohta, T.Kanade. Stereo by Intra-and Interscanline Search Using Dynamic Programming[J], IEEE Trans. on PAMI-6,1984(1):139-154.
[52] G.V.S. Raju, T.O. Binford, S. Shekhar. Stereo Matching Using Viterbi Algorithm[J], In Proc. Im. Understand Workshop, 1987:766-776.
[53] G. Medioni, R. Nevatia. Segment-Based Stereo Matching[J], CVGIP,1985, 31:2-18
[54] 吴立德编.计算机视觉[M],上海:复旦大学出版社,1993.
[55] 郑南宁编.计算机视觉与模式识别[M],国防工业出版社,1998.
[56] 王之卓著.摄影测量原理[M].北京:测绘出版社.1979.
[57] 求是科技,苏彦华等.Visual C++数字图像识别技术典型案例[M],人民邮电出版社 2004.8.
[58] M.Armstrong, A.Zisserman, R.Hartley. Euclidean reconstruction from image triplets[J], European Conference on Computer Vision, 1996.
[59] 吴文琪,孙增圻.机器视觉中的摄像机定标方法综述[J].计算机应用研究.2004.2.4-6.
[60] 刘勇.基于图像的空间三维数据获取及建模[D].武汉大学.2004.
[61] 宋长波.基于线特征的立体视觉匹配研究[D].武汉大学.2004.
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[2] 刘李杰,蔡德钧,翁南衫.一种面向运动的视频对象分割算法[J].计算机学报.2000,23(12):1326～1331
[3] Roberts,L.G. Machine perception of three-dimensional solids[J], In: Tippet J. T. ed.Optical and Electro-Optical Information Processing, 1965:159-197.
[4] Marr.D, Poggio.T. A Computational Theory of Human Stereo Vision[J], Proc R Soc Lond B Biol Sci. 1979 May 23;204(1156):301-28..
[5] 张晓玲. 远距离运动物体动态参数测试技术的理论和实验研究[D],[博士学位论文],天津大学,2005.
[6] Faig.W. Calibration of Close-Range Photogrammetry System: Mathematical Formulation[J]. Photometric Eng. Remote Sensing, 1975, 41(12):1479-1486.
[7] Y.I.Abdel-Aziz, H.M.Karara. Photogrammetric Potential of Nonmetric Cameras[J]. Civil Engineering Studies, Photogrammetry Series No.36, Urbana,Illinois, 1974,3:1~18.
[8] Dainis.A. Juberts.M. Accurate Remote Measurement of Robot Trajectory motion[J], Robotics and Automation. Proceedings. 1985 IEEE International Conference on,1985, 92-99.
[9] Yakimovsky Y, Cunningham R. A System for Extracting 3D[J], Image Processing, 1978,7:195~210.
[10] Tsai R. Y. An Efficient and Accurate Camera Calibration Technique for 3D Machine Vision[J], In Proc. IEEE Conference on Computer Vision and Patern Recognition, Miami Beach, FL, 1986:364-374.
[11] Grosky W. I, Tamburino L. A., A unified approach to the linear camera calibration problem[J], IEEE Trans. on PAMI-12, 1990, 663~671.
[12] Shin S. S., Hung Y. P., Lin W. S., Accurate Linear Technique for Camera CalibrationConsidering Lens Distortion by Solving an Eigenvalue Problem[J], Optical Engineering, 1993, 32(1): 138-149.
[13] Wang L. L., Tsai W. H., Camera Calibration by Vanishing Lines for 3D Computer Vision, IEEE Trans on PAMI-13, 1991.
[14] 段发阶.计算机视觉检测基础理论及应用技术研究[D],[博士学位论文],天津大学,1994.
[15] A.Kuijper, L.M.J.Florack, On the Creations of Critical Points in Scale Space With Applications to Medical Image Analysis[J], UU-CS-2001-25 TR.
[16] H.P.Moravec, Towards Automatic Visual Obstacle Avoidance[J], In Proc. 5th Int Joint Conf Artificial Intelligent,Cambridge,MA,1977.
[17] R.V.Ee,C.M.Schor,Unconstrained stereoscopic matching of lines[J], Vision Research, 2000,40:151~162.
[18] H.Loaiza,J.Triboulet,S.Lelandais. Matching Segments in Stereoscopic Vision[J], IEEE Instrumentation&Measurement Magazine,2001,3:37~42.
[19] B.J.Supa,W.N.Klariquist. Patch-based stereo in a general binocular viewing[J], Theory and Experiment, 1997, PAMI-19(3):247~253.
[20] Rosenfeld A, Weszka J S. An Improved Method of Angel Detection on Digital Curves[J]. IEEE Trans. Computers, 1975, C-24(9):940-941.
[21] Freeman H, Davis L S.A Corner Finding Algorithm for Chain Coded Curves[J].IEEETrans.Computers,1977,26(3):297-303.
[22] Asada H, Brady M. The Curvature Primal Sketch[J]. IEEE Trans. PAMI, 1986, 8(1): 2-14.
[23] Witkin A P. Scale Space Filtering[C]. Karlsruhe Proceedings of the 8th International Joint Conferenceon Artificial Intelligence,1983.1019-1021.
[24] Koenderink J J. The Structure of Image[J]. Biological Cybernetics, 1984, 50(5): 363-370.
[25] Anothai Rattarangs, iChin Roland T. Scale-based Detectionof Corners of Planar Curves[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 1992,14(4):430-449.
[26] G.Giraudon, R.Derich. On Corner and Vertex Detection[J]. Computer Vision and Pattern Recognition, 1991, 3(6):650-655.
[27] 陈燕新,戚飞虎.一种新的提取轮廓特征点的方法[J].红外与毫米波学报,1998, 17(3):171-176.
[28] 罗斌,E.R.Hancock.图像角点检测的矢量场方法[J].中国图像图形学报,1998, 3(10): 832-835.
[29] 刘文予,朱光喜. 二值图像角点检测的形态骨架法[J].信号处理,2000,16(3): 276-280.
[30] L.Kitchen, A.Rosenfeld. Analysis of Gray Level Corner Detection[J]. Pattern Recognition Letters, 1999,20(2):149-162.
[31] Chris Harris, Mike Stephens. A Combined Corner and Edge Detector[C]. Manchester: Proceedings of the 4th Alvey Vision Conference, 1988.147-151.
[32] C.Tomas, I.T.Kanade. Detection and Tracking of Point Features[R].Carnegie Mellon University,1991.
[33] Jianbo.Shi, Carlo.Tomasi. Good Features to Track [C]. IEEE Conference on Computer Vision and Pattern Recognition (CVPR'94), 1994.593-600.
[34] Miroslav.Trajkovic,Mark.Hedley. Fast Corner Detection[J]. Image Vision Computing, 1998, 16(1):75-87.
[35] Smith S M, Brady J M. Susan: A New Approach to Low Level Image Processing[J]. International Journal of Computer Vision, 1997,23(1):45-78.
[36] Y.Ohta,T.Kanade. Stereo by Intra and Inter-Scanline Search Using Dynamic Programming[J], IEEE Transaction Pattern Analysis and Machine Intelligence, 1985,7.
[37] Yalin.Xiong, Larry.Matthies. Error Analysis of a Real-Time Stereo System[J], IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1997:1087-1093.
[38] Myron.Z.Brown,Darius.Burschka,Gregory.D.Hager. Advances in computational stereo[J], IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(8): 993~1008.
[39] 马颂德,张正友.计算机视觉-计算理论与算法基础[M],北京:科学出版社,2003.
[40] Kurt.Konolige. Small Vision Systems: Hardware and Implementation[J], In Proceedings of the 8th International Symposium in Robotic Research,1997,203–212.
[41] Veksler.O. Fast variable window for stereo correspondence using integral images[J], Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on.
[42] Marr.D,PoggioT. Cooperative Computation of a Theory of Human Stereo Vision[J], Phil Trans. Roy. Soc., 1981, B292:217-253.
[43] Marr.D,PoggioT. Cooperative Computation of Stereo Disparity[J], Science 15 October 1976:Vol.194.no.4262,pp.283-287.
[44] Grimson,William.Eric.Leifur. From Image to Surface: A computational Study of the Human Early Visual System[J], Cambridge, MA, MIT Press, 1981:247-267.
[45] W.E.L.Grimson. A Computer Implementation of a Theory of Human Stereo Vision[J], Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, Vol.292, No.1058,1981:217-253.
[46] R.D.Arnold, T.O.Binford. Geometric Constraints in Stereo Vision[J], In Image Processing for Missile Guidance, 1980:281-292.
[47] R.D.Arnold. Automated Stereo Perception[D], Ph.D. dissertation, Dept. of Computer Science, Stanford University, CA 1983.
[48] H.H. Baker, T.O. Binford. Depth From Edge and Intensity-Based Stereo[J], In Proc. 7th IJCAI,1981:631-636.
[49] H.H. Baker. Edge Based Stereo Correction[J], In Proc. Im. Understand Workshop, 1980:168-175.
[50] G.D. Forney Jr. The Viterbi Algorithm[J], Proc. IEEE, 1973,61:268-278.
[51] Y.Ohta, T.Kanade. Stereo by Intra-and Interscanline Search Using Dynamic Programming[J], IEEE Trans. on PAMI-6,1984(1):139-154.
[52] G.V.S. Raju, T.O. Binford, S. Shekhar. Stereo Matching Using Viterbi Algorithm[J], In Proc. Im. Understand Workshop, 1987:766-776.
[53] G. Medioni, R. Nevatia. Segment-Based Stereo Matching[J], CVGIP,1985, 31:2-18
[54] 吴立德编.计算机视觉[M],上海:复旦大学出版社,1993.
[55] 郑南宁编.计算机视觉与模式识别[M],国防工业出版社,1998.
[56] 王之卓著.摄影测量原理[M].北京:测绘出版社.1979.
[57] 求是科技,苏彦华等.Visual C++数字图像识别技术典型案例[M],人民邮电出版社 2004.8.
[58] M.Armstrong, A.Zisserman, R.Hartley. Euclidean reconstruction from image triplets[J], European Conference on Computer Vision, 1996.
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