计算机视觉中三维重构的研究与应用
|
摘要
三维重构是计算机视觉技术的主要内容之一。二维图像中的非线性畸变、拍摄图像的相机内外参数的标定是计算机视觉中三维重构的技术核心。尤其是计算机技术和数字成像技术的发展，要求有新的系统方案和技术措施，以充分利用先进仪器和设备的有利因素，增加计算机视觉技术在多种不同场合的适用性和灵活性。本文是在以数码相机为成像设备的条件下，在开发公路路产赔偿管理系统项目过程中，对三维重构中的理论和应用技术进行了深入的研究。
本文在分析了数码相机特点的基础上，提出了一种适合于数码相机特点的三维重构系统方案。在这一方案中建立二维图像中非线性畸变的处理、数码相机的内部和外部参数的标定各自的独立模型，分别求解。使得图像非线性畸变和数码相机的内部参数的求解，能在实验室中完成。实现了过程分解、模块独立，减少了参数间的互相干扰和充分利用了设备现有条件。
通过理论分析和实验验证，在数码相机成像中，二维图像中的非线性畸变主要来自于相机镜头中透镜的组合误差。图像中的非线性畸变与相机拍摄时的状态有关。在目前的数码相机条件下，影响图像处理的主要是图像中的径向畸变。
在分析和研究了图像中非线性畸变的大小与图像像素多少之间的关系基础上，提出了定量表示图像中所含非线性畸变大小的方法。
在建立了非线性畸变数学模型的基础上，提出了采用标准图形法求解相机镜头产生的非线性畸变方法。这种方法的求解过程可在实验室中进行。设计了求解方案，研究了对应的计算方法和计算步骤。这样可以在进行三维重构的处理之前，先对图像进行线性化的校正，消除图像中的非线性畸变，使后续的所有处理都在线性条件下进行。
文中提出了数码相机参数分组标定的方法，即将数码相机的内部参数和外部参数分别标定，相机的内部参数标定在实验室中进行。内部参数的标定有两种方法，一种是快速简易求解的办法，另一种是精确求解的方法。数码相机外部参数标定采用了透视矩阵法。
在对数码相机外部参数标定方面，本文还提出了一种称之为直角三点标定法。这种方法简化了标定装置的结构，仅需要成直角关系的三点作为标定装置。在标定装置三点成像的光线上建立一个直角三角形，使这个直角三角形与标定装置的三角形相似，由此来确定相机的所有外部参数。文中对这种方法进行了详细的理论推导和过程设计。
公路路产赔偿管理系统的成功开发和应用，证实了上述研究成果的正确性和实用性。
One of the main components of the computer vision is 3D reconstruction. Processing of the nonlinear distortion for 2D images and Calibration of camera's interior and external parameters are key techniques of 3D reconstruction of the computer vision. With development of the computer and digital imaging technique, new system scheme and many new techniques are required. Advanced apparatus should be adequately used. Applicability and facility of the computer vision should be enhanced in different occasion. In the dissertation, theory and application technique of 3D reconstruction are deep studied, based digital camera and in the developing process of management system of the paying for highway road-equipment.
A system scheme of 3D reconstruction is brought forward after characteristic of digital camera in the dissertation, that is based digital camera imaging. Solving of nonlinear distortion and calibration of digital camera's interior and external parameters are respectively modeled and solved. Portions of them are solved in the laboratory.
Nonlinear distortion of images by digital camera is made of combined errors of lens in the camera. It is confirmed through theoretic analysis and experimental proof. Nonlinear distortion of the images is relative to camera state when it shoots the images. At present, radial distortion of image affect to image processing in imaging by digital camera.
A method that is used to expressing magnitude of the image nonlinear distortion is brought forward, after relation of the magnitudes of nonlinear distortion and the image pixels is studied.
Mathematical model of image nonlinear distortion is built. Standard graphics method is brought forward that is used to solving coefficient of nonlinear distortion of the images. The method is made in the laboratory. Solving plan of the method is designed. The calculating means and steps of the method are studied in the here.
Parameters subgroup calibration of the digital camera is brought forward. Interior and external parameters of digital camera are separately calibrated in the method. The interior parameters can be calibrated in laboratory. Two techniques of the interior parameters calibration are studied in here. A technique is quick and facility solving method. Another is precision solving method. A technique that is called perspective matrix calibration is studied for solving external parameters of the digital camera.
Another calibration technique that is called right angle and three points is studied for digital camera external parameters. Calibration device is simple and only 3 points constitute it in the method. A right-angled triangle that is similar to the calibration deice is built in 3 light lines that are imaging lines of 3 point of the calibration device. The relation can solve the external parameters of the digital camera. Theory analysis and process design are made detailed in here.
Management system of the paying for highway road-equipment is developed and applied. The project has approved validity and practicability of the studies in the dissertation.
本文在分析了数码相机特点的基础上，提出了一种适合于数码相机特点的三维重构系统方案。在这一方案中建立二维图像中非线性畸变的处理、数码相机的内部和外部参数的标定各自的独立模型，分别求解。使得图像非线性畸变和数码相机的内部参数的求解，能在实验室中完成。实现了过程分解、模块独立，减少了参数间的互相干扰和充分利用了设备现有条件。
通过理论分析和实验验证，在数码相机成像中，二维图像中的非线性畸变主要来自于相机镜头中透镜的组合误差。图像中的非线性畸变与相机拍摄时的状态有关。在目前的数码相机条件下，影响图像处理的主要是图像中的径向畸变。
在分析和研究了图像中非线性畸变的大小与图像像素多少之间的关系基础上，提出了定量表示图像中所含非线性畸变大小的方法。
在建立了非线性畸变数学模型的基础上，提出了采用标准图形法求解相机镜头产生的非线性畸变方法。这种方法的求解过程可在实验室中进行。设计了求解方案，研究了对应的计算方法和计算步骤。这样可以在进行三维重构的处理之前，先对图像进行线性化的校正，消除图像中的非线性畸变，使后续的所有处理都在线性条件下进行。
文中提出了数码相机参数分组标定的方法，即将数码相机的内部参数和外部参数分别标定，相机的内部参数标定在实验室中进行。内部参数的标定有两种方法，一种是快速简易求解的办法，另一种是精确求解的方法。数码相机外部参数标定采用了透视矩阵法。
在对数码相机外部参数标定方面，本文还提出了一种称之为直角三点标定法。这种方法简化了标定装置的结构，仅需要成直角关系的三点作为标定装置。在标定装置三点成像的光线上建立一个直角三角形，使这个直角三角形与标定装置的三角形相似，由此来确定相机的所有外部参数。文中对这种方法进行了详细的理论推导和过程设计。
公路路产赔偿管理系统的成功开发和应用，证实了上述研究成果的正确性和实用性。
One of the main components of the computer vision is 3D reconstruction. Processing of the nonlinear distortion for 2D images and Calibration of camera's interior and external parameters are key techniques of 3D reconstruction of the computer vision. With development of the computer and digital imaging technique, new system scheme and many new techniques are required. Advanced apparatus should be adequately used. Applicability and facility of the computer vision should be enhanced in different occasion. In the dissertation, theory and application technique of 3D reconstruction are deep studied, based digital camera and in the developing process of management system of the paying for highway road-equipment.
A system scheme of 3D reconstruction is brought forward after characteristic of digital camera in the dissertation, that is based digital camera imaging. Solving of nonlinear distortion and calibration of digital camera's interior and external parameters are respectively modeled and solved. Portions of them are solved in the laboratory.
Nonlinear distortion of images by digital camera is made of combined errors of lens in the camera. It is confirmed through theoretic analysis and experimental proof. Nonlinear distortion of the images is relative to camera state when it shoots the images. At present, radial distortion of image affect to image processing in imaging by digital camera.
A method that is used to expressing magnitude of the image nonlinear distortion is brought forward, after relation of the magnitudes of nonlinear distortion and the image pixels is studied.
Mathematical model of image nonlinear distortion is built. Standard graphics method is brought forward that is used to solving coefficient of nonlinear distortion of the images. The method is made in the laboratory. Solving plan of the method is designed. The calculating means and steps of the method are studied in the here.
Parameters subgroup calibration of the digital camera is brought forward. Interior and external parameters of digital camera are separately calibrated in the method. The interior parameters can be calibrated in laboratory. Two techniques of the interior parameters calibration are studied in here. A technique is quick and facility solving method. Another is precision solving method. A technique that is called perspective matrix calibration is studied for solving external parameters of the digital camera.
Another calibration technique that is called right angle and three points is studied for digital camera external parameters. Calibration device is simple and only 3 points constitute it in the method. A right-angled triangle that is similar to the calibration deice is built in 3 light lines that are imaging lines of 3 point of the calibration device. The relation can solve the external parameters of the digital camera. Theory analysis and process design are made detailed in here.
Management system of the paying for highway road-equipment is developed and applied. The project has approved validity and practicability of the studies in the dissertation.
引文
1．David Marr，视觉计算理论，科学出版社，1988，姚国正、刘磊、汪云九译。
2．罗宇华，计算机视觉，人民邮电出版社，1990。
3．程存学 朱晓昆，计算机视觉---低层处理技术，电子工业出版社，1993。
4．吴健康、肖锦玉，计算机视觉基本理论和方法，中国科学技术大学出版社，1991。
5．D．H．Ballard．C．M．Brown，计算机视觉，科学出版社，1987，王东泉、徐心平、赵经伦译。
6．夏德深、傅德胜，现代图像处理技术与应用，东南大学出版社，1997。
7．沈庭芳、方子文，数字图像处理及模式识别，北京理工大学出版社，1998。
8．章毓晋，图像处理和分析，清华大学出版社，1999。
9．Kenneth、R．Castleman，数字图像处理，清华大学出版社，1998。
10．黄智，图像处理和识别程序库，天津科学出版社，1989。
11．唐荣锡，计算机辅助飞机制造，航空工业出版社，1993。
12．郑南宁，计算机视觉与模式识别，国防工业出版社，1998。
13．马颂德、张正友，计算机视觉---计算理论与算法基础，科学出版社，1998。
14．吴立德，计算机视觉，复旦大学出版社，1993。
15．贾云得，机器视觉，科学出版社，2000，
16．于洪川，基于主动视觉的摄像机自动标定方法，机器人，1999，1，pp1-7。
17．邱茂林、马颂德，计算机视觉中摄像机定标综述，自动化学报，第26卷，2000，1，pp43-55。
18．李新肖、郑南宁、乔瑛，利用分层深度全景图实现基于图像绘制，计算机辅助设计与图形学报，2000，1。
19．沈向洋、王立峰，基于图像的绘制技术综述，中国计算机用户，第418期2000．6
20. Ding Qin Lin, B. J. Avies, Surface engineering geometry for computer—aided design and manufacture, Publishers Chichester, 1987 First
21. Juyang Weng, Paul Cohen, Marc Herniou, Camera calibration with distortion models and accuracy evaluation, Trail. Patt. Anal. Machine Intell., Vol. 14, no.10, pp.965-980, Oct. 1992.
22. W. faig, Calibration of close-range photogrammetric systems: Mathematical formulation, Photogrammetic Eng. Remote Sensing, vol. 41, pp. 1479-1486, 1975.
23. R. Y. Tsai, An efficient and accurate camera calibration technique for 3D machine vision, Proc. Int. Conf. Comput. Vision Part. Recogn., June 1986, pp. 364-374.
24. R. Y. Tsai, A versatile camera calibration technique for high-accuracy 3d machine 469-480.
vision metrology using off-the-shelf TV cameras and lenses, IEEE J. Robotics Automat., vol. RA-3,no. 4, pp. 323-344, Aug. 1987．
25． K.tarabanis and R. Y. Tsai, Computing viewpoints that satisfy optical constrains, Proc. Int. Conf. Comput Vision Patt. Recogn., May 1991, pp. 152-158．
26． R. K. Lenz and R. Y. Tsai, Techniques for calibration of the scale factor and image center for high accuracy 3D machine vision metrology, Proc. IEEE Int. Conf. Robotics Automat, Mar. 1987, pp. 68-75．
27． O. D. Faugeras, G. toscant, The calibration problem for stereo, Proc. Int. Conf. Comput. Vision Patt. Recogn., June 1986, pp. 15-20．
28． L. L. Wang and W. H. Tsai, Camera calibration lines for 3-d computer vision, Tran. Patt. Anal. Machine Intell., Vol. 13, no. 4, pp. 370-376, April. 1991．
29． S. K. Kopparapu and P. Corke, The effect of Measurement noise on Intrinsic calibration parameters, Proc. IEEE Int. Conf. Robotics Automat, May 1999, pp. 1281-1286．
30． C. Cauchois, E. Brassart, C. Drocourt, P. vasseur, Calibration of the omnidirectional vision sensor. SYCLOP, Proc. IEEE Int. Conf. Robotics Automat, May 1999, pp. 1287-1292．
31． A. Guiducci, 3D road reconstruction from a single view, Computer vision and image understanding, Vol. 70． No. 2, May 1998, pp. 212-226．
32． M. A. Turk, D. G. Morgenthaler, K. D. Germban and M. Marra, VTTS-A vision system for autonomous land vehicle navigation, IEEE Trans. Patt. Anal. Machine Intell. Vol. 10, No. 3, May 1998, pp. 342-361．
33． E. D. Dickmanns and B. D. Mysliwetz, Recursive 3-D road and relative ego-state recognition, IEEE Trans. Patt. Anal. Machine Intell. Vol. 14, no. 2, Feb. 1992, pp. 199-213．
34． S. C. Pei and L. G. Liou, Vehicle-type motion estimation by the fusion of image point and line features, Pattern recognition, Vol. 31, no.3, 1998, pp. 333-344．
35． S. S. Sinha and G. Schunck, A two-stage algorithm for discontinuity-preserving surface reconstruction, IEEE Trans. Patt. Anal. Machine Intell. Vol. 14, no. 1, Janu.1992, pp. 36-55．
36． G. Q. Wei and S. D. Ma, Two plane camera calibration: a unified model, Proc. IEEE Int. Conf. Robotics Automat, Mar. 1991, pp. 133-144
37． G. Q. Wei and S. D. Ma, Implicit and explicit camera calibration: theory and experiments, IEEE Trans. Patt. Anal. Machine Intell. Vol. 16, no. 5． May 1994, pp.
38. D. DeMenthon, A zero-bank algorithm for inverse perspective of a road from a single image, IEEE Int. Conf. Robotics Automat, Mar. 1987, pp. 1444-1449.
39. D. DeMenthon and L. S. Davis, Reconstruction of road by local image matches and global 3D optimization, IEEE Int. Conf. Robotics Automat, Janu. 1990, pp. 1337-2342.
40．黄海斌、戚飞虎，一种精确标定摄像机的遗传算法方案，红外与毫米波学报，Vol．19，No．1，Feb．2000，pp1-6．
41．王金刚、李伟、刘颖，全帧读出型面阵CCD光电传感器在图像采集中的应用，电子技术应用，2000．7，pp68-70．
42．吴福朝、于洪川、袁波，摄像机内参数自标定——理论与算法，自动化学报，Vol．25，NO．6，Nov．1999，pp770-776．
43．陈从顺、刘京南、余玲玲、内田悦行，结构光投影法在三维检测和物象重建系统中的应用，计算机应用研究，1999．7，pp78-79．
44．刘金、孙济洲，基于图像的计算机三维造型和绘声绘制技术综述，计算机应用研究，2000．12，pp94-98．
45．李国正、裴仁清、袁庆丰、陈瑛，基于机器视觉的螺纹检验仪软件的研制，上海大学学报(自然科学版)，Vol．6，No．4，Aug．2000，pp308-310．
46．邹国辉，袁保宗，一种新的摄像机自定标方法，机器人，Vol．21，No．6，Nov．1999，pp432-435．
47．徐逢亮、李树楷，基于激光测距的航空扫描影像中建筑物的自动提取，遥感学报，Vol．3，No．3，Aug．1999，pp171-174．
48．赵凯华、钟锡华，光学，北京大学出版社，1984年。
49．潘家寅译，摄影光学，徐氏基金出版社，1975年。
50．安文化，现代照相技术，北京理在大学出版社，1992。
51．薛建平、高景林，新型照相机，中国摄影出版社，1994。
52．陈坚、孙志月，数码相机的使用，西安电子科技大学出版社，1998。
53．袁祥辉，固体图像传感器及其应用，重庆大学出版社，1996。
54．方德植、陈奕培，射影几何，高等教育出版社，1983。
55．梅向明、刘增贤、林向岩，高等几何，高等教育出版社，1983。
56. B. Prescott and G. F. Mclean, Line-based correction of radial lens distortion, Graphical Models and Image Processing, Vol. 59, No. 1, Jan. 1997, pp39-47.
57. Tomas Pajdla and Vaclav Hlavac, Camera calibration and euclidean reconstruction from known obserever translations, IEEE Computer Society Conference on
Computer Vision and Pattern Recognition 1998． 6, pp421-426．
58． Heung-Yeung Shum,Mei Han and Richard Szeliski, Interactive construction of 3D modles from panoramic mosaics, IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1998． 6, pp427-433．
59． Simon Baker and Richard Szeliski, A layered approach to stereo reconstruction, IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1998． 6, pp434-441．
60． M.Daum and G. Dudeek, On 3-D surface reconstruction using shape from shadows, IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1998． 6,pp461-468．
61． Sing Bing Kang and Richard Szeliski, 3-D scene data recovery using omnidirectional Mutibaseline stereo, IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1996． 9, pp364-370．
62． Antonio Guiducci, Parametric model of the perspective projection of a road with applications to lane keeping and 3D road reconstruction, Computer Vison and Image Understanding, Vol. 73, No.3, March,1999, pp414-427．
63． Antonio Guiducci, Camera calibration for road applications, Computer Vision and Image Understanding ,Vol.79, 2000,pp250-266．
64． Konstantions Tarabanis and Roger Y. Tsai, Computing viewpoits that satisfy optical constraits, IEEE Int. conf. Robotics Automation 1991． 5, pp152-158．
65． Daniel DeMenthon and Larry S. Davis, Reconstruction of a of road by local images and global 3D optimization, IEEE Int. conf. Robotics Automation, 1990． 1, pp1337-1342．
66． M. A. Sid-Ahmed and Mohamed T. Boraie, Dual camera calibration for 3-D machine vision metrology, IEEE Transactions on Instrumentation and Measurement, Vol.39, No.3, June, 1990． pp512-516．
67． B.Prescott and GF.McLean, Line-base correction of radial lens distortion, Graphical Models and Image Processing, Vol.59,No. 1, January,1997, pp39-47．
68． William I. Grosky and Louis A. Tamburino, A unified approach to the linear camera calibration problem, IEEE Transactions on Pattern Analysis and Machine Intelligence Vol.12, No.7, July 1990, pp663-671．
69． Janne Heikkila and Olli Silven, A four-step camera calibration procedure with implicit image correction, IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1997． 9, ppl 106-1112．
70． Hadqi Zhuang and H. Motaghedi, Robot calibration with planar constraints, IEEE Int. Conf. On Robotics and Automation, Detroit Michigan, May, 1999, pp805-810．
71． E. Trucco, R. B. Fisher and A. W. Fitzgibbon, Calibration data consistency and model acquisition with laser stripers, Computer Integrated Manufacturing Vol.11, No. 4, 1998,pp293-310．
72． S. Hossein Cheraghi, A new lead-to-pad alignment technique for surface-mount component placement, Computer Integrated Manufacturing Vol. 11, No.2, 1998, pp95-102．
73． Saeid Motavalli and Jorge Valenzuela, A system for reverse engineering of prismatic parts using orthographic images, Computer Integrated Manufacturing Vol. 11, No.2, 1998,pp103-110．
74． Shang-Hong Lai and Chang-Wu Fu, A generalized depth estimation algorithm with a single image, IEEE Transactions on Pattern Analysis and Machine Intelligence Vol.14, No.4, April, 1992, pp405-411．
75． M. Bober, N. Georgis and J. Kittler, On accurate and robust estimation of fundamental matrix, Computer Vision and Image Understanding, Vol.72, No.1, October, 1998, pp39-53．
76． Moses W. Chan, Zygmunt Pizlo and David M Chelberg, Binocular shape reconstruction: psychological plausibility of the 8-point algorithm, Computer Vision and Image Understanding, Vol.74, No.2, May, 1999, pp121-137．
77． Long Quan, Uncalibrated 1D projective camera and 3D affine reconstruction of lines, IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1997． 9, pp60-65．
78． A. N. Rajagopalan and S. Chaudhuri, Optimal selection of camera parameters for recovery of depth from defocused images, IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1997． 9, pp219-224．
79． Zhengyou Zhang and Veit Schenk, Self-maintaining camera calibration over time, IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1997． 9, pp231-236．
80． Yalin Xiong and Ken Turkowski, Creating image-based VR using a self-calibration fisheye lens, IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1997． 9, pp237-243．
81． David Beymer, Philip McLauchlan, Benn Coifman and Jitendra Malik, A real-time computer vsion system for measuring traffic parameters, IEEE Computer Society
Conference on Computer Vision and Pattern Recognition 1997． 9, pp495-501．
82． Marc Pollefeys and Luc Van Gool, A stratified approach to metric self-calibration, IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1997． 9, pp407-412．
83． Ling-Ling Wang and Weng-Hsiang Tsai , Camera calibration by vanishing lines for 3-D computer vision, IEEE Transactions on Pattern Analysis and machine Intelligence, Vol.13, No.4, April, 1991, pp370-376．
84． Sarvajit S. Sinha and Brian G . Schunck, A Two-stge algorithm for discontinuity-preserving surface reconstruction, IEEE Transactions on Pattern Analysis and machine Intelligence, Vol.14, No. 1, April, 1992, pp36-55．
85． Yi Ma and Stefano Soatto, Euclidean reconstruction and reprojection up to subgroups, International Journal of Computer Vision, 38(3) , 2000, PP219-229．
86． Kiriakos N. Kutulakos and Steven M. Seitz, A theory of shape by space carving, International Journal of Computer Vision, 38(3) , 2000, PP199-218．
87． Bao Hua Zhuang and Wen Wei Zhang, Machine vision system for inner wall surface inspection, International Journal of Computer Vision, 38(3) , 2000, PP149-155
88． Pat V. Costa and Rvsi, Vision system go from bust to best, Assembly, August, 1998, pp18-19．
89． Valerie C. Bolhouse, Integrating machine vision with automation begins with knowing the technology basic. Assembly, march, 1998, pp46-50．
90． John Sprovieri, Careful inspection at the right time prevents wasting resources on defective products, Assembly, April, 1998, pp62-70．
91． Frank Newburn, Future of vision is simple solutions, Assembly, January, 1999, pp18-19．
92． John Sprovieri, 'Smart camera ' promise to simplify implementation of machine vision systems, Assembly, March, 1999, pp42-44．
93． John Sprovieri, Machine vision system help assemblers improve quality, reduce scrap and control their processes, Assembly, November, 1999, pp40-44．
94． Valerie C. Bolhouse, Vision system that can see parts in three dimensions are making flexible robotic assembly possible, Assembly, August, 1999, pp42-45．
95． John Sprovien, Robots take on flexible assembly, Assembly, October, 2000, pp30-32．
96． John Sprovieri, Advances inprocessing power benefit machine vision, Assembly, December, 2000． pp30-33．
97． John Sprovieri, A close look at assembly, Assembly, September, 2000, pp68-74．
98． Shawn Becker and Matt Lennon, Visually assisted modeling techniques, www.wvision.com, 9, 2000．
99． Camera fundamentals for machine vision, www.machinevision.com ,3,2001．
100． Bala P. Amavasai, Robert B. Yates and Stuart Meikle, Vision central for a MEMS robotic system, www.ipr.ira.uka.de , 12,2000．
101． Jitendra Malik and Dror Maydan, Recovering three-dimensional shape from a single image of curved objects, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 11, No.6, June, 1989, pp555-566．
102． William Hoff and Narendra Ahuja, Surfaces from stereo: integrating feature matching disparity Estimation and contour detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 11, No.2, February, 1989, pp121-136．
103． Ilan Shimshoni, On estimating uncertainty in the location of image point in 3D recognition from match sets of different sizes, Computer Viosion and Image Understanding, Vol.74, No.3, June, 1999, pp 163-173．
104． F. Dornaika and R. Chung, Cooperative stereo-motion: matching and reconstruction, Computer Viosion and Image Understanding, Vol.79, No.3, June, 2000, pp 408-4273．
105． Z. Zhang, Determining the epipolar geometry and its uncertainty: A review, Int. J. Computer Vision 27(2) , 1998, pp43-76．
106． C. Rothwell, G Csurka and O. Faugeras, A comparison of projective reconstruction methods for pans of views, in Proc. Of the International Conference on Computer Vision, 1995, pp932-937．
107． M. Pilu, A direct method for stereo correspondence based on singular value decomposition, in Proc. Of the IEEE Conference on computer Vision and Pattern Recognition, June, 1995,pp326-341．
108． R. Mohr, F. Veillon and L. Quan, 3D relative reconstruction using multiple uncalibrated images, in Proceedings of the EEEE Conference on Computer Vision and Pattern Recognition, 1993, pp543-548．
109． R. I. Hartley, Projective reconstruction and invariants from multiple images, IEEE Trans. Pattern Anal. Mach. Intell., 16(10) , 1994,pp1036-1041．
110． R. I. Hartley, An algorithm for self calibration from several views, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1994, pp908-912．
111． Bascle B. and Deriche R. , Stereo matching reconstruction and refinement of 3D
curves using deformable contours, in Proc. 4th Int. conf. Computer vision, 1993, pp421-430．
112． Collins R. T. , A space-sweep approach to true multi-image matching, in Proc. Computer Vision and Pattern Recognition Conference, 1996, pp358-363．
113． Kang S. B. and Szeliski R. 3-D scene data recovery using omnidirectional multibaseline stereo, in Proc. Computer Vision and Pattern Recognition Conference, 1996, pp364-370．
114． Zhao C. and Mohr R. , Global three-dimensional surface reconstruction from occluding contours, Computer Vision and Images Understanding, 64(1) , 1996, pp62-96．
115． Heyden A. , Reduced multilinear constrains-Theory and experiments, International Journal of Computer Vision, 30(2) , 1998, pp5-26．
116． R. I. Hartley, In defence of the 8-point algorithm, in proceedings, International Conference on Computer Vision, 1995, ppl064-1070．
117． Z. Zhang, R. Deriche and O. Faugeras, A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry, Artificial intelligence, Vol. 78, 1995, pp87-119．
118． Q. T. Luong and O. D. Faugeras, On the direct determination of epipoles. A case study in algebraic methods for geometric problem, in International Conference on Pattern Recognition, 1993, pp243-247．
119． M. Dhome, The inverse perspective problem from a single view for polyhedra location, in Proc. IEEE Conf. Computer Vision and Pattern Recognition, June, 1988, pp61-66．
120． S. Shah and J. K. Aggarwal, A simple calibration procedure for fisheye lens camera, in Proc. International Conference on Robotics and Automation, 1994, pp3422-3427．
121． M. Subbarao, Parallel depth recovery by changing camera parameters, in Proc, IEEE Intl. Conf. On Computer Vision, 1988, pp149-155．
122． B. Triggs. The geometry of projective reconstruction in matching constraints and joint image, in International Conference on Computer Vision, 1995, pp992-1006．
123． S. S. Sinha and B.G Schunck, Discontinuity-preserving surface reconstruction in vision processing, in Proc. Conf. Of Computer Vision and pattern Recognition, 1989． pp43-48．
124． Grosky William and Tamburino L.A. , A unified approach to the linear camera problem, IEEE Trans. On PattemAnalysis and Machine Intelligence, 12(7) . 1990,
pp663-671．
125． Zhou Guo-Qing and Feng Wen-Hao, CCD camera calibration based on natural landmarks, Pattern Recognition, 31(11) , 1998, pp1715-1724．
126． Bacakoglu Hakan and Kamelm S., A three-step camera calibration method, IEEE Trans. On Instrumentation and Measurement, 46(5) , 1997, pp1165-1172．
127． Weng Ju-Yang, Cohen P. and Herniou M., Camera calibration with distortion model and accuracy evaluation, IEEE Trans. On Pattern Analysis and Machine Intelligence, 14(10) , 1992, pp965-980．
128． A. L. Abbott and N. Ahuja, Surface reconstruction by dynamic integration og focus, camera vergence and stereo, in Int. conf. Computer vision, 1988, pp532-543．
129． Soucy M. and Laurendal D. , Multi-resolution surface modeling from mutiple rage views, in Proc. Of the IEEE conference on Computer Vision and Pattern Recognition, 1992, pp250-256．
130． Mooring B. W., Z. S. roth and M. Driels, Fundamentals of Manipulator calibration, Robotics and Autonomous systems, Vol.9,1992, pp255-269．
131． Zhuang H., Self-calibration of parallel mechanism with a case study on Stewart platforms, IEEE Trans. Robotics and Automation, Vol.13, No.3, June 1997, pp387-397．
132． Ikits M. and J. Hollerbach, Kinematic calibration using a plane constraint, in Proc. IEEE. Intl. Conf. Robotics and Automation, April 1997, pp3191-3196．
133． Khalil W., P. Lemoine and M. Gauter, Autonomous calibration of robots using planar points, Robotics and Manufacturing. Vol.3,1996, pp383-388．
134． L. Quan and R. Mohr, Matching perspective images using geometric constraints and perceptual grouping, Intl. Conf. Computer Vision, Dec. 1988, pp679-684．
135． K. Kanatani and K. Watanable, Reconstruction of 3D road geometry from images for autonomous land vehicles, IEEE Tans. Robotics Automation ,6(1) , 1990, pp127-132．
136． D. Demethon and L. S. Davis, Reconstruction of road by local images matches and global 3D optimization, in Proc. IEEE Int. Conf. Robotics Automation, May 1990, pp1337-1342．
137． Heikkila j. and Silven O., Calibration procedure for short focal length of-the-shelf CCD camera, in Proc. 13th Int. Conf. On Pattern Recognition, 1996, pp166-170．
138． M. A. Sid-Ahmed and Mohamed T. Boraie, Dual camera calibration for 3-D machine vision metrology. IEEE Trans. Instrument Meas., 39(3) , 1990, pp512-516．
139． K. D. Gremban, C. E. Thorpe and T. Kanade, Geometric camera calibration using
system of linear equations, in Proc. IEEE Int. Conf. On Robotics and Automation, 1988, pp562-567.
140. Y. Lamadan, J. Schwartz and H. Wolfson, On reconstruction of 3-d objects from 2-D images, in Proc. IEEE Int. Conf. Robotics and Automation, 1988, pp1407-1413.
141. R. Mohr and L. Morin, Relative positioning from geometric invariants, in Proc. IEEE Conf. On Computer Vision and Pattern Recognition, 1991, pp139-144.
142. M. A. Penna, Camera calibration: a quick and easy way to determine the scale factor, IEEE Trans. Pattern Anal. Machine Intell., Vol. 12, NO.12, 1991, pp1240-1245.
143. J. Weng, P. Cohen and M. Hemiou, Calibration of stereo cameras using a non-linear distortion model, in Proc. Int. Conf. Pattern Recognition, 1990, pp246-253.
144. Chris and A. Shufelt, Projective and object space geometry for monocular building extraction, in Proc. IEEE Int. Conf. Computer Vision and Pattern Recognition, 1994, pp54-61.
145. Hartley RI., A linear method for reconstruction from lines and points, in Proc. Int. Conf. on Computer Vision, 1995, pp882-887.
146. A. Cuiducci, Parametric model of the perspective projection of road with application to lane keeping and 3D road reconstruction, Computer Vision and Image Understanding, 73, 1999, pp414-427.
147. W. Chen and B. Jiang, 3D camera calibration user point concept, Pattern Recognition, Vol. 24, 1991, pp57-67.
148. M. Seibert and A. Waxman, Adaptive 3-D object recognition from multiple view, IEEE Transaction on Pattern analysis and Machine Intelligence Vol. 11 1992, pp107-124.
149. Y. Leedan and P. Meer, Estimation with bilinear constraints in computer vision, in Proc, 6th, Int. Conf. on Computer Vision, January 1998, pp733-738.
150．姜大志、孙闵、丁秋林等，数码相机标定方法的研究，南京航空航天大学学报，Vol，33，No．1，Feb．2001，pp55-59．
151．姜大志、郁倩、丁秋林等，计算机视觉中的设备标定和三维图形重构综述，计算机工程与应用，Vol．37，2001，pp53-55．
152．姜大志、郁倩、丁秋林等，标准图形法求解相机镜头非线性畸变的研究，东南大学学报，Vol．31，No．4，2001，pp111-116．
153．姜大志、郁倩、丁秋林，基于光学镜头成像的非线性畸变研究与综述，计算机工程，2001，
2．罗宇华，计算机视觉，人民邮电出版社，1990。
3．程存学 朱晓昆，计算机视觉---低层处理技术，电子工业出版社，1993。
4．吴健康、肖锦玉，计算机视觉基本理论和方法，中国科学技术大学出版社，1991。
5．D．H．Ballard．C．M．Brown，计算机视觉，科学出版社，1987，王东泉、徐心平、赵经伦译。
6．夏德深、傅德胜，现代图像处理技术与应用，东南大学出版社，1997。
7．沈庭芳、方子文，数字图像处理及模式识别，北京理工大学出版社，1998。
8．章毓晋，图像处理和分析，清华大学出版社，1999。
9．Kenneth、R．Castleman，数字图像处理，清华大学出版社，1998。
10．黄智，图像处理和识别程序库，天津科学出版社，1989。
11．唐荣锡，计算机辅助飞机制造，航空工业出版社，1993。
12．郑南宁，计算机视觉与模式识别，国防工业出版社，1998。
13．马颂德、张正友，计算机视觉---计算理论与算法基础，科学出版社，1998。
14．吴立德，计算机视觉，复旦大学出版社，1993。
15．贾云得，机器视觉，科学出版社，2000，
16．于洪川，基于主动视觉的摄像机自动标定方法，机器人，1999，1，pp1-7。
17．邱茂林、马颂德，计算机视觉中摄像机定标综述，自动化学报，第26卷，2000，1，pp43-55。
18．李新肖、郑南宁、乔瑛，利用分层深度全景图实现基于图像绘制，计算机辅助设计与图形学报，2000，1。
19．沈向洋、王立峰，基于图像的绘制技术综述，中国计算机用户，第418期2000．6
20. Ding Qin Lin, B. J. Avies, Surface engineering geometry for computer—aided design and manufacture, Publishers Chichester, 1987 First
21. Juyang Weng, Paul Cohen, Marc Herniou, Camera calibration with distortion models and accuracy evaluation, Trail. Patt. Anal. Machine Intell., Vol. 14, no.10, pp.965-980, Oct. 1992.
22. W. faig, Calibration of close-range photogrammetric systems: Mathematical formulation, Photogrammetic Eng. Remote Sensing, vol. 41, pp. 1479-1486, 1975.
23. R. Y. Tsai, An efficient and accurate camera calibration technique for 3D machine vision, Proc. Int. Conf. Comput. Vision Part. Recogn., June 1986, pp. 364-374.
24. R. Y. Tsai, A versatile camera calibration technique for high-accuracy 3d machine 469-480.
vision metrology using off-the-shelf TV cameras and lenses, IEEE J. Robotics Automat., vol. RA-3,no. 4, pp. 323-344, Aug. 1987．
25． K.tarabanis and R. Y. Tsai, Computing viewpoints that satisfy optical constrains, Proc. Int. Conf. Comput Vision Patt. Recogn., May 1991, pp. 152-158．
26． R. K. Lenz and R. Y. Tsai, Techniques for calibration of the scale factor and image center for high accuracy 3D machine vision metrology, Proc. IEEE Int. Conf. Robotics Automat, Mar. 1987, pp. 68-75．
27． O. D. Faugeras, G. toscant, The calibration problem for stereo, Proc. Int. Conf. Comput. Vision Patt. Recogn., June 1986, pp. 15-20．
28． L. L. Wang and W. H. Tsai, Camera calibration lines for 3-d computer vision, Tran. Patt. Anal. Machine Intell., Vol. 13, no. 4, pp. 370-376, April. 1991．
29． S. K. Kopparapu and P. Corke, The effect of Measurement noise on Intrinsic calibration parameters, Proc. IEEE Int. Conf. Robotics Automat, May 1999, pp. 1281-1286．
30． C. Cauchois, E. Brassart, C. Drocourt, P. vasseur, Calibration of the omnidirectional vision sensor. SYCLOP, Proc. IEEE Int. Conf. Robotics Automat, May 1999, pp. 1287-1292．
31． A. Guiducci, 3D road reconstruction from a single view, Computer vision and image understanding, Vol. 70． No. 2, May 1998, pp. 212-226．
32． M. A. Turk, D. G. Morgenthaler, K. D. Germban and M. Marra, VTTS-A vision system for autonomous land vehicle navigation, IEEE Trans. Patt. Anal. Machine Intell. Vol. 10, No. 3, May 1998, pp. 342-361．
33． E. D. Dickmanns and B. D. Mysliwetz, Recursive 3-D road and relative ego-state recognition, IEEE Trans. Patt. Anal. Machine Intell. Vol. 14, no. 2, Feb. 1992, pp. 199-213．
34． S. C. Pei and L. G. Liou, Vehicle-type motion estimation by the fusion of image point and line features, Pattern recognition, Vol. 31, no.3, 1998, pp. 333-344．
35． S. S. Sinha and G. Schunck, A two-stage algorithm for discontinuity-preserving surface reconstruction, IEEE Trans. Patt. Anal. Machine Intell. Vol. 14, no. 1, Janu.1992, pp. 36-55．
36． G. Q. Wei and S. D. Ma, Two plane camera calibration: a unified model, Proc. IEEE Int. Conf. Robotics Automat, Mar. 1991, pp. 133-144
37． G. Q. Wei and S. D. Ma, Implicit and explicit camera calibration: theory and experiments, IEEE Trans. Patt. Anal. Machine Intell. Vol. 16, no. 5． May 1994, pp.
38. D. DeMenthon, A zero-bank algorithm for inverse perspective of a road from a single image, IEEE Int. Conf. Robotics Automat, Mar. 1987, pp. 1444-1449.
39. D. DeMenthon and L. S. Davis, Reconstruction of road by local image matches and global 3D optimization, IEEE Int. Conf. Robotics Automat, Janu. 1990, pp. 1337-2342.
40．黄海斌、戚飞虎，一种精确标定摄像机的遗传算法方案，红外与毫米波学报，Vol．19，No．1，Feb．2000，pp1-6．
41．王金刚、李伟、刘颖，全帧读出型面阵CCD光电传感器在图像采集中的应用，电子技术应用，2000．7，pp68-70．
42．吴福朝、于洪川、袁波，摄像机内参数自标定——理论与算法，自动化学报，Vol．25，NO．6，Nov．1999，pp770-776．
43．陈从顺、刘京南、余玲玲、内田悦行，结构光投影法在三维检测和物象重建系统中的应用，计算机应用研究，1999．7，pp78-79．
44．刘金、孙济洲，基于图像的计算机三维造型和绘声绘制技术综述，计算机应用研究，2000．12，pp94-98．
45．李国正、裴仁清、袁庆丰、陈瑛，基于机器视觉的螺纹检验仪软件的研制，上海大学学报(自然科学版)，Vol．6，No．4，Aug．2000，pp308-310．
46．邹国辉，袁保宗，一种新的摄像机自定标方法，机器人，Vol．21，No．6，Nov．1999，pp432-435．
47．徐逢亮、李树楷，基于激光测距的航空扫描影像中建筑物的自动提取，遥感学报，Vol．3，No．3，Aug．1999，pp171-174．
48．赵凯华、钟锡华，光学，北京大学出版社，1984年。
49．潘家寅译，摄影光学，徐氏基金出版社，1975年。
50．安文化，现代照相技术，北京理在大学出版社，1992。
51．薛建平、高景林，新型照相机，中国摄影出版社，1994。
52．陈坚、孙志月，数码相机的使用，西安电子科技大学出版社，1998。
53．袁祥辉，固体图像传感器及其应用，重庆大学出版社，1996。
54．方德植、陈奕培，射影几何，高等教育出版社，1983。
55．梅向明、刘增贤、林向岩，高等几何，高等教育出版社，1983。
56. B. Prescott and G. F. Mclean, Line-based correction of radial lens distortion, Graphical Models and Image Processing, Vol. 59, No. 1, Jan. 1997, pp39-47.
57. Tomas Pajdla and Vaclav Hlavac, Camera calibration and euclidean reconstruction from known obserever translations, IEEE Computer Society Conference on
Computer Vision and Pattern Recognition 1998． 6, pp421-426．
58． Heung-Yeung Shum,Mei Han and Richard Szeliski, Interactive construction of 3D modles from panoramic mosaics, IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1998． 6, pp427-433．
59． Simon Baker and Richard Szeliski, A layered approach to stereo reconstruction, IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1998． 6, pp434-441．
60． M.Daum and G. Dudeek, On 3-D surface reconstruction using shape from shadows, IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1998． 6,pp461-468．
61． Sing Bing Kang and Richard Szeliski, 3-D scene data recovery using omnidirectional Mutibaseline stereo, IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1996． 9, pp364-370．
62． Antonio Guiducci, Parametric model of the perspective projection of a road with applications to lane keeping and 3D road reconstruction, Computer Vison and Image Understanding, Vol. 73, No.3, March,1999, pp414-427．
63． Antonio Guiducci, Camera calibration for road applications, Computer Vision and Image Understanding ,Vol.79, 2000,pp250-266．
64． Konstantions Tarabanis and Roger Y. Tsai, Computing viewpoits that satisfy optical constraits, IEEE Int. conf. Robotics Automation 1991． 5, pp152-158．
65． Daniel DeMenthon and Larry S. Davis, Reconstruction of a of road by local images and global 3D optimization, IEEE Int. conf. Robotics Automation, 1990． 1, pp1337-1342．
66． M. A. Sid-Ahmed and Mohamed T. Boraie, Dual camera calibration for 3-D machine vision metrology, IEEE Transactions on Instrumentation and Measurement, Vol.39, No.3, June, 1990． pp512-516．
67． B.Prescott and GF.McLean, Line-base correction of radial lens distortion, Graphical Models and Image Processing, Vol.59,No. 1, January,1997, pp39-47．
68． William I. Grosky and Louis A. Tamburino, A unified approach to the linear camera calibration problem, IEEE Transactions on Pattern Analysis and Machine Intelligence Vol.12, No.7, July 1990, pp663-671．
69． Janne Heikkila and Olli Silven, A four-step camera calibration procedure with implicit image correction, IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1997． 9, ppl 106-1112．
70． Hadqi Zhuang and H. Motaghedi, Robot calibration with planar constraints, IEEE Int. Conf. On Robotics and Automation, Detroit Michigan, May, 1999, pp805-810．
71． E. Trucco, R. B. Fisher and A. W. Fitzgibbon, Calibration data consistency and model acquisition with laser stripers, Computer Integrated Manufacturing Vol.11, No. 4, 1998,pp293-310．
72． S. Hossein Cheraghi, A new lead-to-pad alignment technique for surface-mount component placement, Computer Integrated Manufacturing Vol. 11, No.2, 1998, pp95-102．
73． Saeid Motavalli and Jorge Valenzuela, A system for reverse engineering of prismatic parts using orthographic images, Computer Integrated Manufacturing Vol. 11, No.2, 1998,pp103-110．
74． Shang-Hong Lai and Chang-Wu Fu, A generalized depth estimation algorithm with a single image, IEEE Transactions on Pattern Analysis and Machine Intelligence Vol.14, No.4, April, 1992, pp405-411．
75． M. Bober, N. Georgis and J. Kittler, On accurate and robust estimation of fundamental matrix, Computer Vision and Image Understanding, Vol.72, No.1, October, 1998, pp39-53．
76． Moses W. Chan, Zygmunt Pizlo and David M Chelberg, Binocular shape reconstruction: psychological plausibility of the 8-point algorithm, Computer Vision and Image Understanding, Vol.74, No.2, May, 1999, pp121-137．
77． Long Quan, Uncalibrated 1D projective camera and 3D affine reconstruction of lines, IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1997． 9, pp60-65．
78． A. N. Rajagopalan and S. Chaudhuri, Optimal selection of camera parameters for recovery of depth from defocused images, IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1997． 9, pp219-224．
79． Zhengyou Zhang and Veit Schenk, Self-maintaining camera calibration over time, IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1997． 9, pp231-236．
80． Yalin Xiong and Ken Turkowski, Creating image-based VR using a self-calibration fisheye lens, IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1997． 9, pp237-243．
81． David Beymer, Philip McLauchlan, Benn Coifman and Jitendra Malik, A real-time computer vsion system for measuring traffic parameters, IEEE Computer Society
Conference on Computer Vision and Pattern Recognition 1997． 9, pp495-501．
82． Marc Pollefeys and Luc Van Gool, A stratified approach to metric self-calibration, IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1997． 9, pp407-412．
83． Ling-Ling Wang and Weng-Hsiang Tsai , Camera calibration by vanishing lines for 3-D computer vision, IEEE Transactions on Pattern Analysis and machine Intelligence, Vol.13, No.4, April, 1991, pp370-376．
84． Sarvajit S. Sinha and Brian G . Schunck, A Two-stge algorithm for discontinuity-preserving surface reconstruction, IEEE Transactions on Pattern Analysis and machine Intelligence, Vol.14, No. 1, April, 1992, pp36-55．
85． Yi Ma and Stefano Soatto, Euclidean reconstruction and reprojection up to subgroups, International Journal of Computer Vision, 38(3) , 2000, PP219-229．
86． Kiriakos N. Kutulakos and Steven M. Seitz, A theory of shape by space carving, International Journal of Computer Vision, 38(3) , 2000, PP199-218．
87． Bao Hua Zhuang and Wen Wei Zhang, Machine vision system for inner wall surface inspection, International Journal of Computer Vision, 38(3) , 2000, PP149-155
88． Pat V. Costa and Rvsi, Vision system go from bust to best, Assembly, August, 1998, pp18-19．
89． Valerie C. Bolhouse, Integrating machine vision with automation begins with knowing the technology basic. Assembly, march, 1998, pp46-50．
90． John Sprovieri, Careful inspection at the right time prevents wasting resources on defective products, Assembly, April, 1998, pp62-70．
91． Frank Newburn, Future of vision is simple solutions, Assembly, January, 1999, pp18-19．
92． John Sprovieri, 'Smart camera ' promise to simplify implementation of machine vision systems, Assembly, March, 1999, pp42-44．
93． John Sprovieri, Machine vision system help assemblers improve quality, reduce scrap and control their processes, Assembly, November, 1999, pp40-44．
94． Valerie C. Bolhouse, Vision system that can see parts in three dimensions are making flexible robotic assembly possible, Assembly, August, 1999, pp42-45．
95． John Sprovien, Robots take on flexible assembly, Assembly, October, 2000, pp30-32．
96． John Sprovieri, Advances inprocessing power benefit machine vision, Assembly, December, 2000． pp30-33．
97． John Sprovieri, A close look at assembly, Assembly, September, 2000, pp68-74．
98． Shawn Becker and Matt Lennon, Visually assisted modeling techniques, www.wvision.com, 9, 2000．
99． Camera fundamentals for machine vision, www.machinevision.com ,3,2001．
100． Bala P. Amavasai, Robert B. Yates and Stuart Meikle, Vision central for a MEMS robotic system, www.ipr.ira.uka.de , 12,2000．
101． Jitendra Malik and Dror Maydan, Recovering three-dimensional shape from a single image of curved objects, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 11, No.6, June, 1989, pp555-566．
102． William Hoff and Narendra Ahuja, Surfaces from stereo: integrating feature matching disparity Estimation and contour detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 11, No.2, February, 1989, pp121-136．
103． Ilan Shimshoni, On estimating uncertainty in the location of image point in 3D recognition from match sets of different sizes, Computer Viosion and Image Understanding, Vol.74, No.3, June, 1999, pp 163-173．
104． F. Dornaika and R. Chung, Cooperative stereo-motion: matching and reconstruction, Computer Viosion and Image Understanding, Vol.79, No.3, June, 2000, pp 408-4273．
105． Z. Zhang, Determining the epipolar geometry and its uncertainty: A review, Int. J. Computer Vision 27(2) , 1998, pp43-76．
106． C. Rothwell, G Csurka and O. Faugeras, A comparison of projective reconstruction methods for pans of views, in Proc. Of the International Conference on Computer Vision, 1995, pp932-937．
107． M. Pilu, A direct method for stereo correspondence based on singular value decomposition, in Proc. Of the IEEE Conference on computer Vision and Pattern Recognition, June, 1995,pp326-341．
108． R. Mohr, F. Veillon and L. Quan, 3D relative reconstruction using multiple uncalibrated images, in Proceedings of the EEEE Conference on Computer Vision and Pattern Recognition, 1993, pp543-548．
109． R. I. Hartley, Projective reconstruction and invariants from multiple images, IEEE Trans. Pattern Anal. Mach. Intell., 16(10) , 1994,pp1036-1041．
110． R. I. Hartley, An algorithm for self calibration from several views, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1994, pp908-912．
111． Bascle B. and Deriche R. , Stereo matching reconstruction and refinement of 3D
curves using deformable contours, in Proc. 4th Int. conf. Computer vision, 1993, pp421-430．
112． Collins R. T. , A space-sweep approach to true multi-image matching, in Proc. Computer Vision and Pattern Recognition Conference, 1996, pp358-363．
113． Kang S. B. and Szeliski R. 3-D scene data recovery using omnidirectional multibaseline stereo, in Proc. Computer Vision and Pattern Recognition Conference, 1996, pp364-370．
114． Zhao C. and Mohr R. , Global three-dimensional surface reconstruction from occluding contours, Computer Vision and Images Understanding, 64(1) , 1996, pp62-96．
115． Heyden A. , Reduced multilinear constrains-Theory and experiments, International Journal of Computer Vision, 30(2) , 1998, pp5-26．
116． R. I. Hartley, In defence of the 8-point algorithm, in proceedings, International Conference on Computer Vision, 1995, ppl064-1070．
117． Z. Zhang, R. Deriche and O. Faugeras, A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry, Artificial intelligence, Vol. 78, 1995, pp87-119．
118． Q. T. Luong and O. D. Faugeras, On the direct determination of epipoles. A case study in algebraic methods for geometric problem, in International Conference on Pattern Recognition, 1993, pp243-247．
119． M. Dhome, The inverse perspective problem from a single view for polyhedra location, in Proc. IEEE Conf. Computer Vision and Pattern Recognition, June, 1988, pp61-66．
120． S. Shah and J. K. Aggarwal, A simple calibration procedure for fisheye lens camera, in Proc. International Conference on Robotics and Automation, 1994, pp3422-3427．
121． M. Subbarao, Parallel depth recovery by changing camera parameters, in Proc, IEEE Intl. Conf. On Computer Vision, 1988, pp149-155．
122． B. Triggs. The geometry of projective reconstruction in matching constraints and joint image, in International Conference on Computer Vision, 1995, pp992-1006．
123． S. S. Sinha and B.G Schunck, Discontinuity-preserving surface reconstruction in vision processing, in Proc. Conf. Of Computer Vision and pattern Recognition, 1989． pp43-48．
124． Grosky William and Tamburino L.A. , A unified approach to the linear camera problem, IEEE Trans. On PattemAnalysis and Machine Intelligence, 12(7) . 1990,
pp663-671．
125． Zhou Guo-Qing and Feng Wen-Hao, CCD camera calibration based on natural landmarks, Pattern Recognition, 31(11) , 1998, pp1715-1724．
126． Bacakoglu Hakan and Kamelm S., A three-step camera calibration method, IEEE Trans. On Instrumentation and Measurement, 46(5) , 1997, pp1165-1172．
127． Weng Ju-Yang, Cohen P. and Herniou M., Camera calibration with distortion model and accuracy evaluation, IEEE Trans. On Pattern Analysis and Machine Intelligence, 14(10) , 1992, pp965-980．
128． A. L. Abbott and N. Ahuja, Surface reconstruction by dynamic integration og focus, camera vergence and stereo, in Int. conf. Computer vision, 1988, pp532-543．
129． Soucy M. and Laurendal D. , Multi-resolution surface modeling from mutiple rage views, in Proc. Of the IEEE conference on Computer Vision and Pattern Recognition, 1992, pp250-256．
130． Mooring B. W., Z. S. roth and M. Driels, Fundamentals of Manipulator calibration, Robotics and Autonomous systems, Vol.9,1992, pp255-269．
131． Zhuang H., Self-calibration of parallel mechanism with a case study on Stewart platforms, IEEE Trans. Robotics and Automation, Vol.13, No.3, June 1997, pp387-397．
132． Ikits M. and J. Hollerbach, Kinematic calibration using a plane constraint, in Proc. IEEE. Intl. Conf. Robotics and Automation, April 1997, pp3191-3196．
133． Khalil W., P. Lemoine and M. Gauter, Autonomous calibration of robots using planar points, Robotics and Manufacturing. Vol.3,1996, pp383-388．
134． L. Quan and R. Mohr, Matching perspective images using geometric constraints and perceptual grouping, Intl. Conf. Computer Vision, Dec. 1988, pp679-684．
135． K. Kanatani and K. Watanable, Reconstruction of 3D road geometry from images for autonomous land vehicles, IEEE Tans. Robotics Automation ,6(1) , 1990, pp127-132．
136． D. Demethon and L. S. Davis, Reconstruction of road by local images matches and global 3D optimization, in Proc. IEEE Int. Conf. Robotics Automation, May 1990, pp1337-1342．
137． Heikkila j. and Silven O., Calibration procedure for short focal length of-the-shelf CCD camera, in Proc. 13th Int. Conf. On Pattern Recognition, 1996, pp166-170．
138． M. A. Sid-Ahmed and Mohamed T. Boraie, Dual camera calibration for 3-D machine vision metrology. IEEE Trans. Instrument Meas., 39(3) , 1990, pp512-516．
139． K. D. Gremban, C. E. Thorpe and T. Kanade, Geometric camera calibration using
system of linear equations, in Proc. IEEE Int. Conf. On Robotics and Automation, 1988, pp562-567.
140. Y. Lamadan, J. Schwartz and H. Wolfson, On reconstruction of 3-d objects from 2-D images, in Proc. IEEE Int. Conf. Robotics and Automation, 1988, pp1407-1413.
141. R. Mohr and L. Morin, Relative positioning from geometric invariants, in Proc. IEEE Conf. On Computer Vision and Pattern Recognition, 1991, pp139-144.
142. M. A. Penna, Camera calibration: a quick and easy way to determine the scale factor, IEEE Trans. Pattern Anal. Machine Intell., Vol. 12, NO.12, 1991, pp1240-1245.
143. J. Weng, P. Cohen and M. Hemiou, Calibration of stereo cameras using a non-linear distortion model, in Proc. Int. Conf. Pattern Recognition, 1990, pp246-253.
144. Chris and A. Shufelt, Projective and object space geometry for monocular building extraction, in Proc. IEEE Int. Conf. Computer Vision and Pattern Recognition, 1994, pp54-61.
145. Hartley RI., A linear method for reconstruction from lines and points, in Proc. Int. Conf. on Computer Vision, 1995, pp882-887.
146. A. Cuiducci, Parametric model of the perspective projection of road with application to lane keeping and 3D road reconstruction, Computer Vision and Image Understanding, 73, 1999, pp414-427.
147. W. Chen and B. Jiang, 3D camera calibration user point concept, Pattern Recognition, Vol. 24, 1991, pp57-67.
148. M. Seibert and A. Waxman, Adaptive 3-D object recognition from multiple view, IEEE Transaction on Pattern analysis and Machine Intelligence Vol. 11 1992, pp107-124.
149. Y. Leedan and P. Meer, Estimation with bilinear constraints in computer vision, in Proc, 6th, Int. Conf. on Computer Vision, January 1998, pp733-738.
150．姜大志、孙闵、丁秋林等，数码相机标定方法的研究，南京航空航天大学学报，Vol，33，No．1，Feb．2001，pp55-59．
151．姜大志、郁倩、丁秋林等，计算机视觉中的设备标定和三维图形重构综述，计算机工程与应用，Vol．37，2001，pp53-55．
152．姜大志、郁倩、丁秋林等，标准图形法求解相机镜头非线性畸变的研究，东南大学学报，Vol．31，No．4，2001，pp111-116．
153．姜大志、郁倩、丁秋林，基于光学镜头成像的非线性畸变研究与综述，计算机工程，2001，