基于单数码相机的三维摄影测量理论与关键技术研究
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摘要
三维坐标数据测量是逆向工程、机器人视觉自主导航、产品检测、虚拟现实等的首要步骤。本文以提高基于单数码相机的三维摄影测量的稳健性和精确性为研究目标，对测量目标的图像提取、多位姿相机自定位、三维重建中外点剔除、三维坐标数据全局最优解、系统参数辨识与校正等关键问题进行了深入研究。本文的主要研究内容和成果概况如下：
1.提出了基于Canny边缘检测算子和多项式曲面拟合的亚像素图像边缘提取方法，为实现圆形编码点中心的精确定位提供了良好的图像数据基础。编码点中心的图像坐标定位精度是影响三维测量系统精度的关键因素之一，在亚像素边缘提取基础上，提出一种改进的灰度重心方法计算编码点中心，该方法能实现编码点中心的快速、精确图像坐标定位。与传统的最小二乘椭圆拟合法、灰度重心法以及近年来提出的基于对偶二次曲线拟合方法进行比较分析，验证了本文方法具有更高的稳定性和定位精度。
2.提出了一种改进的确定两幅图像相对拍摄位姿的鲁棒估计算法，解决了本质矩阵多解问题，修正了鲁棒估计算法中的参数估计公式。与当前具有代表性的两种5点算法进行定量比较研究，实验证明本文算法的鲁棒性得到提高，为后续整个图像组相机位姿及编码点三维重建的稳定性奠定了很好的基础。结合相机全局旋转运动估计理论，提出了基于RANSAC方法的多视图相机全局坐标旋转运动估计方法，求出各相机在统一世界坐标系的全局旋转运动。运用图论中最大生成树方法构造两幅图像之间编码点匹配准确性最高的图像组，以建立编码点的全局匹配关系。
3.提出了一种快速、稳健的已知相机全局旋转姿态的多视图几何重建方法。该方法通过对空间点再投影误差函数进行线性化分析，提出了在三维重建过程中基于L1范数最小化和基于最小最大化问题的自适应外点剔除方法，通过引入松弛变量，将外点剔除问题转换为关于松弛变量的线性规划模型，一方面提高了算法的稳定性，另一方面提高了算法的效率。与三种具有代表性的基于松弛变量的外点剔除算法进行比较实验，结果表明该方法不会出现过度剔除外点的现象，能正确保留真正的内点，并且具有更小的空间点再投影误差。
4.根据凸优化理论，给出了在L2范数下多视图三维重建结果的全局最优判定方法，该方法通过判定空间点再投影误差函数的凸性，来确定已经求出的最优解是否为全局最优解。对于非凸情况，运用分支定界理论，提出了求解非凸函数比式和问题的空间点三维坐标全局最优解的方法。通过与基于L￥范数全局优化方法的比较实验证明，本文方法具有更高的计算精度和运算效率。
5.提出了一种融合简明隐式图像校正的多视图几何精确重建方法。该方法引入6个隐式畸变参数进行镜头校正，并将隐式畸变参数直接嵌入到三维重建优化模型中。实验表明这个算法能够显著提高三维摄影测量精度，在图像组多视图几何精确重建中起着关键作用。
6.在精确的多视图几何求解架构基础上，提出一般特征点和特征线的三维摄影测量方法。首先研究了两幅图像之间的特征点匹配算法，该算法在编码点的全局匹配关系下，采用形状上下文描述算子和概率松弛标记法实现了特征点的一对一匹配，再运用薄板样条函数对匹配点集的整体形状进行匹配验证。然后提出并实现了特征线提取、特征线全局匹配及特征线三维重建的方法。完成了汽车车门钣金零件、飞机机翼、管路模型架、摩托车骨架等实物的三维测量。
本文提出的算法和系统软件模块均进行了实验验证、对比和分析，应用原型系统测量了具有代表性的三维实物，获得了良好的测量稳健性、较高的测量精度和测量效率。
Three-dimensional (3D) coordinate measurement is prerequisite for reverse engineering,product quality inspection, robot visual navigation, virtual reality etc. The work in this thesisfocuses on improving the robustness and accuracy of digital close range photogrammetrymeasurement system based on a hand-held CCD camera. Several key issues in3D measurementsystem are further studied in this paper, including target extraction from image, multi-cameraposition and orientation locating, outlier removal in3D reconstruction, globally optimal solutionof3D coordinate, system parameter identification and correction etc. The main contents andcontributions of the dissertation are as follows:
1. An edge extraction method based on Canny edge detector and polynomial surface fittingwith sub-pixel accuracy is presented. It provides a good foundation for precise location of thecircular coded marker centers. The precision of the image coordinates of the coded markercenters is one of the critical factors for3D photogrammetry. An improved grey centroid methodis put forward in the paper. Compared with the least-squares ellipse fitting, the traditional greycentroid and the dual conic fitting approach presented recently, the proposed method can obtainsignificantly more stable and precise coded marker centers.
2. An improved algorithm for robust estimating the camera relative position and orientationof pairs of views is put forward. The multi-solutions problem involved in the essential matrixestimation is resolved. In addition, the parameter estimation formula in the robust model iscorrected. Comparisons with several representative five-point algorithms show that the algorithmcan advance the robustness and precision of the camera relative orientation. The algorithm serveswell as the input for the following3D reconstruction pipeline. By utilizing the rotationalconsistency theory, an approach to estimating the global rotations in multi-view reconstruction isproposed. It achieves the camera relative orientations under the world coordinate system.According to the maximum spanning tree algorithm in graph theory, image group with thehighest reliability of coded markers correspondences between image pairs is built. A globalmatching relationship can be achieved by the reliable image group.
3. A fast and stable method for multiple view geometry reconstruction with known rotationsis presented. Using the linearity of re-projection error function, this article puts forward theadaptive outlier removal method usingL1norm and minmax problem during3D reconstruction.By introducing slack variables into the objective function, the outlier removal problem can beanswered by the linear programming problem about slack variables. It improves the stability and efficiency of the algorithm simultaneously. Comparisons with three representative outlierremoval approaches based on slack variables show that the method in this paper can keepgenuine inliers and decrease the re-projection error.
4. According to the convex optimization theory, an approach to verifying global solution formultiple view geometry reconstruction problems usingL2is derived. The convexity of there-projection error function is utilized to verify whether the local solution is globally optimal. Abranch and bound algorithm for globally solving the non-convexity sum of ratios problem is putforward. Compared to L￥-norm-based global optimization, the proposed approach has highercalculation accuracy and computing efficiency.
5. A concise implicit image correction approach for accurate reconstruction of multiple viewgeometry is presented. By introducing six implicit distortion coefficients, the image correctionapproach has been integrated into the3D reconstruction optimization model. Experiments haveevidenced that the method can significantly improve the3D positioning accuracy. The methodplays a key role in accurate multiple view geometry reconstruction.
6. Based on the established accurate multiple view geometry, the methods for3Dreconstruction of points or line segments are put forward. Point correspondences between imagepairs are generated. Under the global correspondence of the coded markers, the match for twopoint set is obtained using shape context and probabilistic relaxation. Then the matching result isverified by thin plate spline function. The method for line segments feature extraction, globalcorrespondence, and3D reconstruction is proposed. Practical examples include an automobiledoor sheet metal part, the wing of an aircraft, a pipeline model frame, motorcycles and othersample objects.
The proposed algorithms and system modules have all been fully experimented, comparedand analyzed. It has been demonstrated that the representative target objects can be obtained bythe prototype system with good measurement stability, high measurement accuracy, andsatisfactory measurement efficiency.
1.提出了基于Canny边缘检测算子和多项式曲面拟合的亚像素图像边缘提取方法，为实现圆形编码点中心的精确定位提供了良好的图像数据基础。编码点中心的图像坐标定位精度是影响三维测量系统精度的关键因素之一，在亚像素边缘提取基础上，提出一种改进的灰度重心方法计算编码点中心，该方法能实现编码点中心的快速、精确图像坐标定位。与传统的最小二乘椭圆拟合法、灰度重心法以及近年来提出的基于对偶二次曲线拟合方法进行比较分析，验证了本文方法具有更高的稳定性和定位精度。
2.提出了一种改进的确定两幅图像相对拍摄位姿的鲁棒估计算法，解决了本质矩阵多解问题，修正了鲁棒估计算法中的参数估计公式。与当前具有代表性的两种5点算法进行定量比较研究，实验证明本文算法的鲁棒性得到提高，为后续整个图像组相机位姿及编码点三维重建的稳定性奠定了很好的基础。结合相机全局旋转运动估计理论，提出了基于RANSAC方法的多视图相机全局坐标旋转运动估计方法，求出各相机在统一世界坐标系的全局旋转运动。运用图论中最大生成树方法构造两幅图像之间编码点匹配准确性最高的图像组，以建立编码点的全局匹配关系。
3.提出了一种快速、稳健的已知相机全局旋转姿态的多视图几何重建方法。该方法通过对空间点再投影误差函数进行线性化分析，提出了在三维重建过程中基于L1范数最小化和基于最小最大化问题的自适应外点剔除方法，通过引入松弛变量，将外点剔除问题转换为关于松弛变量的线性规划模型，一方面提高了算法的稳定性，另一方面提高了算法的效率。与三种具有代表性的基于松弛变量的外点剔除算法进行比较实验，结果表明该方法不会出现过度剔除外点的现象，能正确保留真正的内点，并且具有更小的空间点再投影误差。
4.根据凸优化理论，给出了在L2范数下多视图三维重建结果的全局最优判定方法，该方法通过判定空间点再投影误差函数的凸性，来确定已经求出的最优解是否为全局最优解。对于非凸情况，运用分支定界理论，提出了求解非凸函数比式和问题的空间点三维坐标全局最优解的方法。通过与基于L￥范数全局优化方法的比较实验证明，本文方法具有更高的计算精度和运算效率。
5.提出了一种融合简明隐式图像校正的多视图几何精确重建方法。该方法引入6个隐式畸变参数进行镜头校正，并将隐式畸变参数直接嵌入到三维重建优化模型中。实验表明这个算法能够显著提高三维摄影测量精度，在图像组多视图几何精确重建中起着关键作用。
6.在精确的多视图几何求解架构基础上，提出一般特征点和特征线的三维摄影测量方法。首先研究了两幅图像之间的特征点匹配算法，该算法在编码点的全局匹配关系下，采用形状上下文描述算子和概率松弛标记法实现了特征点的一对一匹配，再运用薄板样条函数对匹配点集的整体形状进行匹配验证。然后提出并实现了特征线提取、特征线全局匹配及特征线三维重建的方法。完成了汽车车门钣金零件、飞机机翼、管路模型架、摩托车骨架等实物的三维测量。
本文提出的算法和系统软件模块均进行了实验验证、对比和分析，应用原型系统测量了具有代表性的三维实物，获得了良好的测量稳健性、较高的测量精度和测量效率。
Three-dimensional (3D) coordinate measurement is prerequisite for reverse engineering,product quality inspection, robot visual navigation, virtual reality etc. The work in this thesisfocuses on improving the robustness and accuracy of digital close range photogrammetrymeasurement system based on a hand-held CCD camera. Several key issues in3D measurementsystem are further studied in this paper, including target extraction from image, multi-cameraposition and orientation locating, outlier removal in3D reconstruction, globally optimal solutionof3D coordinate, system parameter identification and correction etc. The main contents andcontributions of the dissertation are as follows:
1. An edge extraction method based on Canny edge detector and polynomial surface fittingwith sub-pixel accuracy is presented. It provides a good foundation for precise location of thecircular coded marker centers. The precision of the image coordinates of the coded markercenters is one of the critical factors for3D photogrammetry. An improved grey centroid methodis put forward in the paper. Compared with the least-squares ellipse fitting, the traditional greycentroid and the dual conic fitting approach presented recently, the proposed method can obtainsignificantly more stable and precise coded marker centers.
2. An improved algorithm for robust estimating the camera relative position and orientationof pairs of views is put forward. The multi-solutions problem involved in the essential matrixestimation is resolved. In addition, the parameter estimation formula in the robust model iscorrected. Comparisons with several representative five-point algorithms show that the algorithmcan advance the robustness and precision of the camera relative orientation. The algorithm serveswell as the input for the following3D reconstruction pipeline. By utilizing the rotationalconsistency theory, an approach to estimating the global rotations in multi-view reconstruction isproposed. It achieves the camera relative orientations under the world coordinate system.According to the maximum spanning tree algorithm in graph theory, image group with thehighest reliability of coded markers correspondences between image pairs is built. A globalmatching relationship can be achieved by the reliable image group.
3. A fast and stable method for multiple view geometry reconstruction with known rotationsis presented. Using the linearity of re-projection error function, this article puts forward theadaptive outlier removal method usingL1norm and minmax problem during3D reconstruction.By introducing slack variables into the objective function, the outlier removal problem can beanswered by the linear programming problem about slack variables. It improves the stability and efficiency of the algorithm simultaneously. Comparisons with three representative outlierremoval approaches based on slack variables show that the method in this paper can keepgenuine inliers and decrease the re-projection error.
4. According to the convex optimization theory, an approach to verifying global solution formultiple view geometry reconstruction problems usingL2is derived. The convexity of there-projection error function is utilized to verify whether the local solution is globally optimal. Abranch and bound algorithm for globally solving the non-convexity sum of ratios problem is putforward. Compared to L￥-norm-based global optimization, the proposed approach has highercalculation accuracy and computing efficiency.
5. A concise implicit image correction approach for accurate reconstruction of multiple viewgeometry is presented. By introducing six implicit distortion coefficients, the image correctionapproach has been integrated into the3D reconstruction optimization model. Experiments haveevidenced that the method can significantly improve the3D positioning accuracy. The methodplays a key role in accurate multiple view geometry reconstruction.
6. Based on the established accurate multiple view geometry, the methods for3Dreconstruction of points or line segments are put forward. Point correspondences between imagepairs are generated. Under the global correspondence of the coded markers, the match for twopoint set is obtained using shape context and probabilistic relaxation. Then the matching result isverified by thin plate spline function. The method for line segments feature extraction, globalcorrespondence, and3D reconstruction is proposed. Practical examples include an automobiledoor sheet metal part, the wing of an aircraft, a pipeline model frame, motorcycles and othersample objects.
The proposed algorithms and system modules have all been fully experimented, comparedand analyzed. It has been demonstrated that the representative target objects can be obtained bythe prototype system with good measurement stability, high measurement accuracy, andsatisfactory measurement efficiency.
引文
[1]吴晓峰，张国雄.现代大尺寸空间测量方法.航空制造技术，2006，10：68-70.
[2]尚妍，徐春广.光学非接触廓形测量技术研究进展.光学技术，2008，34：216-217.
[3]张广军.视觉测量.北京：科学出版社，2008.
[4]邓文怡，桑新柱，董明利，等.三维拼接在大尺寸视觉测量中的应用.光电子激光，2002，13(11)：1145-1147.
[5]马扬颷，钟约先，戴小林.大面积形体的三维无接触精确测量的研究.机械设计与制造，2006，10：24-26.
[6] J. A. Williams, M. Bennamoun, S. Latham. Mutiple view3D registration: a review and a newtechnique. In Proceedings of IEEE International Conference on Systems, Man, and Cybernetics,1999,3:497-502.
[7] S. Kim, W. Woo. Projection-based registration using a multi-view camera for indoor scenereconstruction. Fifth International Conference on3D Digital Imaging and Modeling,2005:484-491.
[8] R. Zhang, P. S. Tsai, J. E. Cryer, et al. Shape from shading: a survey. IEEE Transactions onPattern Analysis and Machine Intelligence,1999,21(8):690-706.
[9] Q. Zheng, R. Chellappa. Estimation of illuminant direction, albedo, and shape from shading.IEEE Transactions on Pattern Analysis and Machine Intelligence,1991,13(7):680-702.
[10] I. Shimshoni, R. Kimmel, A. M. Bruckstein. Global shape from shading. Computer Visionand Image Understanding,1996,64(1):188-189.
[11] P. S. Tsai, M. Shah. A fast linear shape from shading. In Proceedings of IEEE ComputerSociety Conference on Computer Vision and Pattern Recognition,1992,6:734-736.
[12]尹爱军，秦树人，周传德.基于支持向量机的从明暗恢复形状方法.机械工程学报，2006，42(7)：186-191.
[13] A. Hertzmann, M. Steven. Example-based photometric stereo: shape reconstruction withgeneral, varying BRDFs. IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(8):1254-1264.
[14] G. Vogiatzis, C. Hernandez. Practical3D reconstruction based on photometric stereo.Computer Vision,2010,285:313-345.
[15]陈宇峰，谭文静，王海涛，等.光度立体三维重建算法.计算机辅助设计与图形学学报，2005，17(11)：2408-2414.
[16]马颂德，张正友.计算机视觉—计算理论与算法基础.北京：科学出版社，2003.
[17]张秀彬.智能视感学.北京：中国水利水电出版社，2010.
[18]张广军，李鑫，魏振忠.结构光三维双视觉检测方法研究.仪器仪表学报，2002，23(7)：604-607.
[19]张永军.基于序列图像的视觉检测理论与方法.武汉：武汉大学出版社，2008.
[20] U. R. Dhond, J. K. Aggarwal. A cost-benefit analysis of a third camera for stereocorrespondence. International Journal of Computer Vision,1991,6(1):39-58.
[21] N. Ayache, F. Lustman. Trinocular stereo vision for robotics. IEEE Transactions on PatternAnalysis and Machine Intelligence,1991,13(1):73-85.
[22]何炳蔚，林志航.利用三目视觉获得复杂曲面的边界曲线.计算机辅助设计与图形学学报，2003，15(10)：1253-1257.
[23] M. Okutomi, T. Kanada. A multi-baseline stereo. IEEE Transactions on Pattern Analysis andMachine Intelligence,1993,15(4):353-363.
[24] C. L. Zitnick, J. A. Webb. Multi-baseline stereo using surface extraction. In Technical ReportCMU-CS-96-196, Computer Science Department, Carnegie Mellon University,1996.
[25] B. Jia, Y. J. Zhang, X. G. Lin. Stereo matching using both orthogonal and multiple imagepairs. IEEE International Conference on Acoustics, Speech, and Signal Processing,2000,4:2139-2142.
[26]崔晓青，李谊，韩东峰，等.基于多幅图像组的三维重建.吉林大学学报（理学版），2008，46(1)：75-80.
[27] M. Pollefeys. Self-calibration and metric3D reconstruction from uncalibrated imagesequences[Ph. D. thesis]. Leuven, Belgium: K. U. Leuven,1999.
[28] B. Triggs. Autocalibration and the absolute quadric. In Proceedings of IEEE ComputerSociety Conference on Computer Vision and Pattern Recognition,1997:609-614.
[29] M. Pollefeys, R. Koch, L. V. Gool. Self calibration and metric reconstruction in spite ofvarying and unknown internal camera parameters. In Proceedings of the6th InternationalConference on Computer Vision, Bombay, India,1998:90-96.
[30] A. W. Fitzgibbon, G. Cross, A. Zisserman. Automatic3D model construction for turn-tablesequences. In R. Koch and L. V. Gool (Eds.).3D Structure from Multiple Images of Large-ScaleEnvironments, LNCS1506: Springer-Verlag,1998:155-170.
[31] C. Tomasi, T. Kanade. Shape and motion from image streams under orthography: afactorization method. The International Journal of Computer Vision,1992,9(2):137-154.
[32] C. J. Poelman, T. Kanade. A paraperspective factorization for shape and motion recovery. InJ.-O. Eklundh (Eds.), Proceedings of the3rd European Conference on Computer Vision, volumeB of Lecture Notes in Computer Science,1994:97-108.
[33] P. Sturm, B. Triggs. A factorization based algorithm for multi-image projective structure andmotion. In Proceedings of the4th European Conference on Computer Vision, Cambridge,England, Springer-Verlag,1996,1065:709-720.
[34] N. Snavely, S. M. Seitz, R. Szeliski. Modeling the world from internet photo collections.International Journal of Computer Vision, Springer-Verlag,2007,80(2):189-210.
[35]吴玲达，邓宝松，高宇，等.基于近似相机内参数的精确三维欧式重建算法.系统仿真学报，2007，19(10)：2235-2240.
[36]张祖勋.数字摄影测量的发展与展望.地理信息世界，2004，2(3)：1-5.
[37] R. Hartley, A. Zisserman.计算机视觉中的多视图几何（韦穗，杨尚骏，等译）.合肥：安徽大学出版社，2002.
[38] O. Faugeras. Three-dimensional computer vision-A geometric viewpoint. MIT Press,1993.
[39]朱杰杰.增强现实中多视图几何问题的研究[博士学位论文].浙江：浙江大学，2007.
[40]孙敏.多视图几何与传统摄影测量理论.北京大学学报（自然科学版），2007，43(4)：453-459.
[41]徐巧玉.大型装备在线三维视觉测量系统关键技术研究[博士学位论文].哈尔滨：哈尔滨工业大学，2007.
[42] http://www.gom.com/EN/index.html
[43] http://www.geodetic.com
[44] http://www.aicon3d.de
[45]梁晋，肖振中，刘建伟，等.大型飞机三维光学快速测量建模关键技术研究.中国机械工程，2009，20(6)：648-651.
[46]张德海，梁晋，唐正宗，等.基于近景摄影测量和三维光学测量的大幅面测量新方法.中国机械工程，2009，20(7)：817-822.
[47]石春琴.随机光照双目立体测量系统中的若干关键问题研究[博士学位论文].南京：南京航空航天大学，2011.
[48] N. Snavely. Scene reconstruction and visualization from internet photo collections: a survey.IPSJ Transactions on Computer Vision and Applications (CVA),2011,3:44-66.
[49] M. I. A. Lourakis, A. A. Argyros. The design and implementation of a generic sparse bundleadjustment software package based on the Levenberg-Marquardt algorithm. Technical ReportTR-340, FORTH-ICS,2004.
[50] D. Jacobs. Linear fitting with missing data: applications to structure from motion and tocharacterizing intensity images. In Proceedings of IEEE Computer Society Conference onComputer Vision and Pattern Recognition,1997:206-212.
[51] D. Martinec, T. Pajdla. Robust rotation and translation estimation in multiview reconstruction.In Proceedings of IEEE Computer Society Conference on Computer Vision and PatternRecognition, Minneapolis, Minnesota, USA,2007:1-8.
[52] J. P. Tardif, A. Bartoli, M. Trudeau. Algorithms for batch matrix factorization with applicationto structure-from-motion. In Proceedings of IEEE Computer Society Conference on ComputerVision and Pattern Recognition, Minneapolis, Minnesota, USA,2007:18-23.
[53] O. Enqvist, F. Kahl, C. Olsson. Non-sequential structure from motion. In Proceedings ofComputational Methods for the Innovative Design of Electrical Devices,2011:264-271.
[54] O. Enqvist, C. Olsson, F. Kahl. Stable structure from motion using rotational consistency.Technical Report, Centre for Mathematical Sciences, Lund University,2011.
[55] C. Olsson, O. Enqvist. Stable structure from motion for unordered image collections. InScandinavian Conference on Image Analysis, A. Heyden and F. Kahl (Eds.), Ystad, Sweden,2011:524-535.
[56] K. Forbes, A. Voigt, N. Bodika. An inexpensive, automatic and accurate camera calibrationmethod. In Proceedings of the Thirteenth Annual Symposium of the Pattern RecognitionAssociation of South Africa, November2002.
[57]周玲，张丽艳，郑建冬，等.近景摄影测量中标记点的自动检测.应用科学学报，2007，25(3)：288-294.
[58] M. R. Shortis, T. A. Clarke, T. Short. A comparison of some techniques for the subpixellocation of discrete target images. SPIE,1994,2350:239-250.
[59] A. Fitzgibbon, M. Pilu, R. B. Fisher. Direct least square fitting of ellipses. IEEE Transactionson Pattern Analysis and Machine Intelligence,1999,21(5):476-480.
[60] J. N. Ouellet, P. Hebert. A simple operator for very precise estimation of ellipses. InProceedings of the Fourth Canadian Conference on Computer and Robot Vision,2007:28-30.
[61] J. Canny. A computational approach to edge detection. IEEE Transactions on Pattern Analysisand Machine Intelligence,1986,8(6):679-698.
[62] R. M. Haralick. Digital step edges from zero crossing of second directional derivatives. IEEETransactions on Pattern Analysis and Machine Intelligence,1984,6(1):58-68.
[63]徐刚.由2维影像建立3维模型.武汉：武汉大学出版社，2006.
[64] R. Hartley, A. Zisserman. In defense of the8-point algorithm. In Proceedings of the5thInternational Conference on Computer Vision. Boston, MA: IEEE Computer Society Press,1995:1064-1070.
[65] X. Armangue, J. Salvi. Overall view regarding fundamental matrix estimation. Image andVision Computing,2003,21(2):205-220.
[66] B. Triggs. Routines for relative pose of two calibrated cameras from5points. France:INRIA-Technical,2000.
[67] D. Nister. An efficient solution to the five-point relative pose problem. IEEE Transactions onPattern Analysis and Machine Intelligence,2004,26(6):756-770.
[68] H. Li, R. Hartley. Five-point motion estimation made easy.18th IEEE InternationalConference on Pattern Recognition, Hong Kong,2006,1:630-633.
[69] Z. Kukelova, M. Bujnak, T. Pajdla. Polynomial eigenvalue solution to the5-pt and6-ptrelative pose problems. In BMVC2008: Proceedings of the19th British Machine VisionConference, M. Everingham, C. Needham and R. Fraile (Eds.), vol.1, Malvern, UK,2008:565-574.
[70] M. A. Fischler, R. C. Bolles. Random sample consensus: a paradigm for model fitting withapplications to image analysis and automated cartography. Communications of the ACM,1981,24(6):381-395.
[71] S. Choi, T. Kim, W. Yu. Performance evaluation of RANSAC family. In Proceedings ofBritish Machine Vision Conference, London, UK,2009:1-12.
[72] P. H. S. Torr, A. Zisserman. MLESAC: a new robust estimator with application to estimatingimage geometry. Computer Vision and Image Understanding,2000,78(1):138-156.
[73] P. H. S. Torr. Bayesian model estimation and selection for epipolar geometry and genericmanifold fitting. International Journal of Computer Vision,2002,50(1):35-61.
[74] B. J. Tordoff, D. W. Murray. Guided-MLESAC: faster image transform estimation by usingmatching priors. IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(10):1523-1535.
[75] A. Konouchine, V. Gaganov, V. Veznevets. AMLESAC: a new maximum likelihood robustestimator. In Proceedings of the International Conference on Computer Graphics and Vision(GraphiCon),2005:1-8.
[76] Z. Zhang. Determining the epipolar geomertry and its uncertainty: a review. InternationalJournal of Computer Vision,1998,27(2),161-198.
[77] C. L. Feng, Y. S. Hung. A robust method for estimating the fundamental matrix. Proc. VIIthDigital Image Computing: Techniques and Applications, C. Sun, H. Talbot, S. Ourselin and T.Adriaansen (Eds.),2003:633-642.
[78] P. J. Rousseeuw. Robust regression and outlier detection. New York: John Wiley&Sons Inc,2003.
[79] V. M. Govindu. Robustness in motion averaging. In Proceedings of the7th Asian Conferenceon Computer Vision,2006:457-466.
[80]王桂平，王衍，任嘉辰.图论算法理论、实现及应用.北京：北京大学出版社，2011.
[81] VDI2634Ι，德国工程师协会工业标准.
[82] K. Sim, R. Hartley. Removing outliers using the L￥norm. IEEE Computer SocietyConference on Computer Vision and Pattern Recognition, New York City, USA,2006:485-494.
[83] Y. Seo, H. Lee, S. W. Lee. Outlier removal by convex optimization for L-infinity approaches.In Proceedings of the3rd Pacific Rim Symposium on Advances in Image and Video Technology,2009:203-214.
[84] C. Olsson, A. Eriksson, R. Hartley. Outlier removal using duality. IEEE Conference onComputer Vision and Pattern Recognition,2010:1450-1457.
[85] J. Yu, A. Eriksson, T. J. Chin, et al. An adversarial optimization approach to efficient outlierremoval. IEEE International Conference on Computer Vision. Washington, DC: IEEE ComputerSociety,2011:399-406.
[86]薛振华，王萍，张楚涵，等.图像匹配中去除误配的对抗性优化方法的改进.计算机应用，2012，32(11)：3157-3160.
[87] S. Boyd, L. Vandenberghe. Convex optimization. New York, NY, USA: CambridgeUniversity Press,2004.
[88] F. Alizadeh, D. Goldfarb. Second-order cone programming. Mathematical ProgrammingSeries B,2003,95(1):3-51.
[89] R. Hartley, F. Kahl. Optimal algorithm in multiview geometry. In Proceedings of the8thAsian Conference on Computer Vision,2007,1:13-34.
[90]吴祈宗.运筹学与最优化方法.北京：机械工业出版社，2003.
[91] http://www.robots.ox.ac.uk/~vgg/data.
[92] http://www.maths.lth.se/matematiklth/personal/calle.
[93] F. Kahl, D. Henrion. Globally optimal estimates for geometric reconstruction problems.International Journal of Computer Vision,2007,74(1):3-15.
[94] B. Triggs, P. McLauchlan, R. Hartley, et al. Bundle adjustment-a modern synthesis. InVision Algorithms: Theory and Practice, B. Triggs, A. Zisserman and R. Szeliski (Eds.),Springer-Verlag, vol. LNCS1883,2000:298-372.
[95] H. Stewenius, F. Schaffalitzky, D. Nister. How hard is3-view triangulation really. InProceedings of the Tenth IEEE International Conference on Computer Vision,2005:686-693.
[96] E. Trucco, A. Verri. Introductory techniques for3-D computer vision. New Jersey: PrenticeHall,1998.
[97]吴福朝.计算机视觉中的数学方法.北京：科学出版社，2008.
[98] F. Kahl. Multiple view geometry and the L￥-norm. IEEE International Conference onComputer Vision,2005:1002-1009.
[99] R. Hartley, F. Schaffalitzky. L￥minimization in geometric reconstruction problems. IEEEComputer Society Conference on Computer Vision and Pattern Recognition,2004:504-509.
[100] F. Kahl. Multiple-view geometry under the linfinity-norm. IEEE Transactions on PatternAnalysis and Machine Intelligence,2008,30(9):1603-1617.
[101] H. Li. A paractical algorithm for L￥triangulation with outliers. IEEE Conference onComputer Vision and Pattern Recognition,2007:1-8.
[102]李春明.优化方法.南京：东南大学出版社，2009.
[103]申培萍.全局优化方法.北京：科学出版社，2006.
[104] R. Hartley. Veifying global minima forL2minimization problem. IEEE Conference onComputer Vision and Pattern Recognition,2008:1-8.
[105] C. Olsson, F. Kahl. Projective least-squares: global solutions with local optimization.IEEE Conference on Computer Vision and Pattern Recognition,2009:1216-1223.
[106] Y. J. Wang, K. C. Zhang. Global optimization of nonlinear sum of ratios problem.Applied Mathematics and Computation,2004,158(2):319-330.
[107] H. P. Benson. Global optimization algorithm for nonlinear sum of ration problem.Journal of Optimization Theory and Applications,2002,112(1):1-29.
[108] H. D. Sherali, H. J. Wang. Global optimization of nonconvex factorable programmingproblems. Math Programming,2001,89(3):459-478.
[109] S. Agarwal, M. Krishna, F. Kahl, et al. Practical global optimization for multiviewgeometry. In Proceedings of the9th European Conference on Computer Vision,2006:592-605.
[110] F. Kahl, S. Agarwal, M. Krishna, et al. Practical global optimization for multiviewgeometry. International Journal of Computer Vision,2008,79(3):271-284.
[111] H. P. Benson. Using concave envelopes to globally solve the nonlinear sum of ratiosproblem. Journal of Global Optimization,2002,22:343-364,
[112] J. Weng, P. Cohen, M. Herniou. Camera calibration with distortion models and accuracyevaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence,1992,14(10):965-980.
[113] J. Heikkila, O. Silven. Calibration procedure for short focal length off-the-shelf CCDcameras. In Proceedings of the13th International Conference on Pattem Recognition,1996,1:166-170.
[114] G. Q. Wei, S. D. Ma. A complete two-plane camera calibration method and experimentalcomparisons. In Proceedings of the4th International Conference on Computer Vision,1993:439-446.
[115] S. Belongie, J. Malik, J. Puzicha. Shape context: a new descriptor for shape matchingand object recognition. International Conference of Neural Information Processing System,2000:831-837.
[116] S. Belongie, J. Malik, J. Puzicha. Shape matching and object recognition using shapecontext. IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(2):509-522.
[117] J. Tang, D. Liang, N. Wang, et al. A laplacian spectral method for stereo correspondence.Pattern Recognition Letters,2007,28(12):1391-1399.
[118] Y. Zheng, D. Doermann. Robust point matching for nonrigid shapes by preserving localneighborhood structure. IEEE Transactions on Pattern Analysis and Machine Intelligence,2006,28(4):643-649.
[119] F. L. Bookstein. Principal warps: thin-plate splines and decomposition of deformations.IEEE Transactions on Pattern Analysis and Machine Intelligence,1989,11(6):567-585.
[120] H. Chui, A. Rangarajan. A new algoritm for non-rigid point matching. IEEE Conferenceon Computer Vision and Pattern Recognition,2000,2:44-51.
[121] C. Schmid, A. Zisserman. Automatic line matching across views. IEEE ComputerSociety Conference on Computer Vision and Pattern Recognition,1997:666-671.
[122] C. Schmid, A. Zisserman. The geometry and matching of lines and curves over multipleviews. International Journal of Computer Vision,2000,40(3):199-233.
[123] B. Triggs. Matching constraints and the joint image. Fifth International Conference onComputer Vision,1995:338-343.
[124] T. Werner, T. Pajdla. Oriented matching constraints. In Proceedings of British MachineVision Conference, London, UK,2001:441-450.
[2]尚妍，徐春广.光学非接触廓形测量技术研究进展.光学技术，2008，34：216-217.
[3]张广军.视觉测量.北京：科学出版社，2008.
[4]邓文怡，桑新柱，董明利，等.三维拼接在大尺寸视觉测量中的应用.光电子激光，2002，13(11)：1145-1147.
[5]马扬颷，钟约先，戴小林.大面积形体的三维无接触精确测量的研究.机械设计与制造，2006，10：24-26.
[6] J. A. Williams, M. Bennamoun, S. Latham. Mutiple view3D registration: a review and a newtechnique. In Proceedings of IEEE International Conference on Systems, Man, and Cybernetics,1999,3:497-502.
[7] S. Kim, W. Woo. Projection-based registration using a multi-view camera for indoor scenereconstruction. Fifth International Conference on3D Digital Imaging and Modeling,2005:484-491.
[8] R. Zhang, P. S. Tsai, J. E. Cryer, et al. Shape from shading: a survey. IEEE Transactions onPattern Analysis and Machine Intelligence,1999,21(8):690-706.
[9] Q. Zheng, R. Chellappa. Estimation of illuminant direction, albedo, and shape from shading.IEEE Transactions on Pattern Analysis and Machine Intelligence,1991,13(7):680-702.
[10] I. Shimshoni, R. Kimmel, A. M. Bruckstein. Global shape from shading. Computer Visionand Image Understanding,1996,64(1):188-189.
[11] P. S. Tsai, M. Shah. A fast linear shape from shading. In Proceedings of IEEE ComputerSociety Conference on Computer Vision and Pattern Recognition,1992,6:734-736.
[12]尹爱军，秦树人，周传德.基于支持向量机的从明暗恢复形状方法.机械工程学报，2006，42(7)：186-191.
[13] A. Hertzmann, M. Steven. Example-based photometric stereo: shape reconstruction withgeneral, varying BRDFs. IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(8):1254-1264.
[14] G. Vogiatzis, C. Hernandez. Practical3D reconstruction based on photometric stereo.Computer Vision,2010,285:313-345.
[15]陈宇峰，谭文静，王海涛，等.光度立体三维重建算法.计算机辅助设计与图形学学报，2005，17(11)：2408-2414.
[16]马颂德，张正友.计算机视觉—计算理论与算法基础.北京：科学出版社，2003.
[17]张秀彬.智能视感学.北京：中国水利水电出版社，2010.
[18]张广军，李鑫，魏振忠.结构光三维双视觉检测方法研究.仪器仪表学报，2002，23(7)：604-607.
[19]张永军.基于序列图像的视觉检测理论与方法.武汉：武汉大学出版社，2008.
[20] U. R. Dhond, J. K. Aggarwal. A cost-benefit analysis of a third camera for stereocorrespondence. International Journal of Computer Vision,1991,6(1):39-58.
[21] N. Ayache, F. Lustman. Trinocular stereo vision for robotics. IEEE Transactions on PatternAnalysis and Machine Intelligence,1991,13(1):73-85.
[22]何炳蔚，林志航.利用三目视觉获得复杂曲面的边界曲线.计算机辅助设计与图形学学报，2003，15(10)：1253-1257.
[23] M. Okutomi, T. Kanada. A multi-baseline stereo. IEEE Transactions on Pattern Analysis andMachine Intelligence,1993,15(4):353-363.
[24] C. L. Zitnick, J. A. Webb. Multi-baseline stereo using surface extraction. In Technical ReportCMU-CS-96-196, Computer Science Department, Carnegie Mellon University,1996.
[25] B. Jia, Y. J. Zhang, X. G. Lin. Stereo matching using both orthogonal and multiple imagepairs. IEEE International Conference on Acoustics, Speech, and Signal Processing,2000,4:2139-2142.
[26]崔晓青，李谊，韩东峰，等.基于多幅图像组的三维重建.吉林大学学报（理学版），2008，46(1)：75-80.
[27] M. Pollefeys. Self-calibration and metric3D reconstruction from uncalibrated imagesequences[Ph. D. thesis]. Leuven, Belgium: K. U. Leuven,1999.
[28] B. Triggs. Autocalibration and the absolute quadric. In Proceedings of IEEE ComputerSociety Conference on Computer Vision and Pattern Recognition,1997:609-614.
[29] M. Pollefeys, R. Koch, L. V. Gool. Self calibration and metric reconstruction in spite ofvarying and unknown internal camera parameters. In Proceedings of the6th InternationalConference on Computer Vision, Bombay, India,1998:90-96.
[30] A. W. Fitzgibbon, G. Cross, A. Zisserman. Automatic3D model construction for turn-tablesequences. In R. Koch and L. V. Gool (Eds.).3D Structure from Multiple Images of Large-ScaleEnvironments, LNCS1506: Springer-Verlag,1998:155-170.
[31] C. Tomasi, T. Kanade. Shape and motion from image streams under orthography: afactorization method. The International Journal of Computer Vision,1992,9(2):137-154.
[32] C. J. Poelman, T. Kanade. A paraperspective factorization for shape and motion recovery. InJ.-O. Eklundh (Eds.), Proceedings of the3rd European Conference on Computer Vision, volumeB of Lecture Notes in Computer Science,1994:97-108.
[33] P. Sturm, B. Triggs. A factorization based algorithm for multi-image projective structure andmotion. In Proceedings of the4th European Conference on Computer Vision, Cambridge,England, Springer-Verlag,1996,1065:709-720.
[34] N. Snavely, S. M. Seitz, R. Szeliski. Modeling the world from internet photo collections.International Journal of Computer Vision, Springer-Verlag,2007,80(2):189-210.
[35]吴玲达，邓宝松，高宇，等.基于近似相机内参数的精确三维欧式重建算法.系统仿真学报，2007，19(10)：2235-2240.
[36]张祖勋.数字摄影测量的发展与展望.地理信息世界，2004，2(3)：1-5.
[37] R. Hartley, A. Zisserman.计算机视觉中的多视图几何（韦穗，杨尚骏，等译）.合肥：安徽大学出版社，2002.
[38] O. Faugeras. Three-dimensional computer vision-A geometric viewpoint. MIT Press,1993.
[39]朱杰杰.增强现实中多视图几何问题的研究[博士学位论文].浙江：浙江大学，2007.
[40]孙敏.多视图几何与传统摄影测量理论.北京大学学报（自然科学版），2007，43(4)：453-459.
[41]徐巧玉.大型装备在线三维视觉测量系统关键技术研究[博士学位论文].哈尔滨：哈尔滨工业大学，2007.
[42] http://www.gom.com/EN/index.html
[43] http://www.geodetic.com
[44] http://www.aicon3d.de
[45]梁晋，肖振中，刘建伟，等.大型飞机三维光学快速测量建模关键技术研究.中国机械工程，2009，20(6)：648-651.
[46]张德海，梁晋，唐正宗，等.基于近景摄影测量和三维光学测量的大幅面测量新方法.中国机械工程，2009，20(7)：817-822.
[47]石春琴.随机光照双目立体测量系统中的若干关键问题研究[博士学位论文].南京：南京航空航天大学，2011.
[48] N. Snavely. Scene reconstruction and visualization from internet photo collections: a survey.IPSJ Transactions on Computer Vision and Applications (CVA),2011,3:44-66.
[49] M. I. A. Lourakis, A. A. Argyros. The design and implementation of a generic sparse bundleadjustment software package based on the Levenberg-Marquardt algorithm. Technical ReportTR-340, FORTH-ICS,2004.
[50] D. Jacobs. Linear fitting with missing data: applications to structure from motion and tocharacterizing intensity images. In Proceedings of IEEE Computer Society Conference onComputer Vision and Pattern Recognition,1997:206-212.
[51] D. Martinec, T. Pajdla. Robust rotation and translation estimation in multiview reconstruction.In Proceedings of IEEE Computer Society Conference on Computer Vision and PatternRecognition, Minneapolis, Minnesota, USA,2007:1-8.
[52] J. P. Tardif, A. Bartoli, M. Trudeau. Algorithms for batch matrix factorization with applicationto structure-from-motion. In Proceedings of IEEE Computer Society Conference on ComputerVision and Pattern Recognition, Minneapolis, Minnesota, USA,2007:18-23.
[53] O. Enqvist, F. Kahl, C. Olsson. Non-sequential structure from motion. In Proceedings ofComputational Methods for the Innovative Design of Electrical Devices,2011:264-271.
[54] O. Enqvist, C. Olsson, F. Kahl. Stable structure from motion using rotational consistency.Technical Report, Centre for Mathematical Sciences, Lund University,2011.
[55] C. Olsson, O. Enqvist. Stable structure from motion for unordered image collections. InScandinavian Conference on Image Analysis, A. Heyden and F. Kahl (Eds.), Ystad, Sweden,2011:524-535.
[56] K. Forbes, A. Voigt, N. Bodika. An inexpensive, automatic and accurate camera calibrationmethod. In Proceedings of the Thirteenth Annual Symposium of the Pattern RecognitionAssociation of South Africa, November2002.
[57]周玲，张丽艳，郑建冬，等.近景摄影测量中标记点的自动检测.应用科学学报，2007，25(3)：288-294.
[58] M. R. Shortis, T. A. Clarke, T. Short. A comparison of some techniques for the subpixellocation of discrete target images. SPIE,1994,2350:239-250.
[59] A. Fitzgibbon, M. Pilu, R. B. Fisher. Direct least square fitting of ellipses. IEEE Transactionson Pattern Analysis and Machine Intelligence,1999,21(5):476-480.
[60] J. N. Ouellet, P. Hebert. A simple operator for very precise estimation of ellipses. InProceedings of the Fourth Canadian Conference on Computer and Robot Vision,2007:28-30.
[61] J. Canny. A computational approach to edge detection. IEEE Transactions on Pattern Analysisand Machine Intelligence,1986,8(6):679-698.
[62] R. M. Haralick. Digital step edges from zero crossing of second directional derivatives. IEEETransactions on Pattern Analysis and Machine Intelligence,1984,6(1):58-68.
[63]徐刚.由2维影像建立3维模型.武汉：武汉大学出版社，2006.
[64] R. Hartley, A. Zisserman. In defense of the8-point algorithm. In Proceedings of the5thInternational Conference on Computer Vision. Boston, MA: IEEE Computer Society Press,1995:1064-1070.
[65] X. Armangue, J. Salvi. Overall view regarding fundamental matrix estimation. Image andVision Computing,2003,21(2):205-220.
[66] B. Triggs. Routines for relative pose of two calibrated cameras from5points. France:INRIA-Technical,2000.
[67] D. Nister. An efficient solution to the five-point relative pose problem. IEEE Transactions onPattern Analysis and Machine Intelligence,2004,26(6):756-770.
[68] H. Li, R. Hartley. Five-point motion estimation made easy.18th IEEE InternationalConference on Pattern Recognition, Hong Kong,2006,1:630-633.
[69] Z. Kukelova, M. Bujnak, T. Pajdla. Polynomial eigenvalue solution to the5-pt and6-ptrelative pose problems. In BMVC2008: Proceedings of the19th British Machine VisionConference, M. Everingham, C. Needham and R. Fraile (Eds.), vol.1, Malvern, UK,2008:565-574.
[70] M. A. Fischler, R. C. Bolles. Random sample consensus: a paradigm for model fitting withapplications to image analysis and automated cartography. Communications of the ACM,1981,24(6):381-395.
[71] S. Choi, T. Kim, W. Yu. Performance evaluation of RANSAC family. In Proceedings ofBritish Machine Vision Conference, London, UK,2009:1-12.
[72] P. H. S. Torr, A. Zisserman. MLESAC: a new robust estimator with application to estimatingimage geometry. Computer Vision and Image Understanding,2000,78(1):138-156.
[73] P. H. S. Torr. Bayesian model estimation and selection for epipolar geometry and genericmanifold fitting. International Journal of Computer Vision,2002,50(1):35-61.
[74] B. J. Tordoff, D. W. Murray. Guided-MLESAC: faster image transform estimation by usingmatching priors. IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(10):1523-1535.
[75] A. Konouchine, V. Gaganov, V. Veznevets. AMLESAC: a new maximum likelihood robustestimator. In Proceedings of the International Conference on Computer Graphics and Vision(GraphiCon),2005:1-8.
[76] Z. Zhang. Determining the epipolar geomertry and its uncertainty: a review. InternationalJournal of Computer Vision,1998,27(2),161-198.
[77] C. L. Feng, Y. S. Hung. A robust method for estimating the fundamental matrix. Proc. VIIthDigital Image Computing: Techniques and Applications, C. Sun, H. Talbot, S. Ourselin and T.Adriaansen (Eds.),2003:633-642.
[78] P. J. Rousseeuw. Robust regression and outlier detection. New York: John Wiley&Sons Inc,2003.
[79] V. M. Govindu. Robustness in motion averaging. In Proceedings of the7th Asian Conferenceon Computer Vision,2006:457-466.
[80]王桂平，王衍，任嘉辰.图论算法理论、实现及应用.北京：北京大学出版社，2011.
[81] VDI2634Ι，德国工程师协会工业标准.
[82] K. Sim, R. Hartley. Removing outliers using the L￥norm. IEEE Computer SocietyConference on Computer Vision and Pattern Recognition, New York City, USA,2006:485-494.
[83] Y. Seo, H. Lee, S. W. Lee. Outlier removal by convex optimization for L-infinity approaches.In Proceedings of the3rd Pacific Rim Symposium on Advances in Image and Video Technology,2009:203-214.
[84] C. Olsson, A. Eriksson, R. Hartley. Outlier removal using duality. IEEE Conference onComputer Vision and Pattern Recognition,2010:1450-1457.
[85] J. Yu, A. Eriksson, T. J. Chin, et al. An adversarial optimization approach to efficient outlierremoval. IEEE International Conference on Computer Vision. Washington, DC: IEEE ComputerSociety,2011:399-406.
[86]薛振华，王萍，张楚涵，等.图像匹配中去除误配的对抗性优化方法的改进.计算机应用，2012，32(11)：3157-3160.
[87] S. Boyd, L. Vandenberghe. Convex optimization. New York, NY, USA: CambridgeUniversity Press,2004.
[88] F. Alizadeh, D. Goldfarb. Second-order cone programming. Mathematical ProgrammingSeries B,2003,95(1):3-51.
[89] R. Hartley, F. Kahl. Optimal algorithm in multiview geometry. In Proceedings of the8thAsian Conference on Computer Vision,2007,1:13-34.
[90]吴祈宗.运筹学与最优化方法.北京：机械工业出版社，2003.
[91] http://www.robots.ox.ac.uk/~vgg/data.
[92] http://www.maths.lth.se/matematiklth/personal/calle.
[93] F. Kahl, D. Henrion. Globally optimal estimates for geometric reconstruction problems.International Journal of Computer Vision,2007,74(1):3-15.
[94] B. Triggs, P. McLauchlan, R. Hartley, et al. Bundle adjustment-a modern synthesis. InVision Algorithms: Theory and Practice, B. Triggs, A. Zisserman and R. Szeliski (Eds.),Springer-Verlag, vol. LNCS1883,2000:298-372.
[95] H. Stewenius, F. Schaffalitzky, D. Nister. How hard is3-view triangulation really. InProceedings of the Tenth IEEE International Conference on Computer Vision,2005:686-693.
[96] E. Trucco, A. Verri. Introductory techniques for3-D computer vision. New Jersey: PrenticeHall,1998.
[97]吴福朝.计算机视觉中的数学方法.北京：科学出版社，2008.
[98] F. Kahl. Multiple view geometry and the L￥-norm. IEEE International Conference onComputer Vision,2005:1002-1009.
[99] R. Hartley, F. Schaffalitzky. L￥minimization in geometric reconstruction problems. IEEEComputer Society Conference on Computer Vision and Pattern Recognition,2004:504-509.
[100] F. Kahl. Multiple-view geometry under the linfinity-norm. IEEE Transactions on PatternAnalysis and Machine Intelligence,2008,30(9):1603-1617.
[101] H. Li. A paractical algorithm for L￥triangulation with outliers. IEEE Conference onComputer Vision and Pattern Recognition,2007:1-8.
[102]李春明.优化方法.南京：东南大学出版社，2009.
[103]申培萍.全局优化方法.北京：科学出版社，2006.
[104] R. Hartley. Veifying global minima forL2minimization problem. IEEE Conference onComputer Vision and Pattern Recognition,2008:1-8.
[105] C. Olsson, F. Kahl. Projective least-squares: global solutions with local optimization.IEEE Conference on Computer Vision and Pattern Recognition,2009:1216-1223.
[106] Y. J. Wang, K. C. Zhang. Global optimization of nonlinear sum of ratios problem.Applied Mathematics and Computation,2004,158(2):319-330.
[107] H. P. Benson. Global optimization algorithm for nonlinear sum of ration problem.Journal of Optimization Theory and Applications,2002,112(1):1-29.
[108] H. D. Sherali, H. J. Wang. Global optimization of nonconvex factorable programmingproblems. Math Programming,2001,89(3):459-478.
[109] S. Agarwal, M. Krishna, F. Kahl, et al. Practical global optimization for multiviewgeometry. In Proceedings of the9th European Conference on Computer Vision,2006:592-605.
[110] F. Kahl, S. Agarwal, M. Krishna, et al. Practical global optimization for multiviewgeometry. International Journal of Computer Vision,2008,79(3):271-284.
[111] H. P. Benson. Using concave envelopes to globally solve the nonlinear sum of ratiosproblem. Journal of Global Optimization,2002,22:343-364,
[112] J. Weng, P. Cohen, M. Herniou. Camera calibration with distortion models and accuracyevaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence,1992,14(10):965-980.
[113] J. Heikkila, O. Silven. Calibration procedure for short focal length off-the-shelf CCDcameras. In Proceedings of the13th International Conference on Pattem Recognition,1996,1:166-170.
[114] G. Q. Wei, S. D. Ma. A complete two-plane camera calibration method and experimentalcomparisons. In Proceedings of the4th International Conference on Computer Vision,1993:439-446.
[115] S. Belongie, J. Malik, J. Puzicha. Shape context: a new descriptor for shape matchingand object recognition. International Conference of Neural Information Processing System,2000:831-837.
[116] S. Belongie, J. Malik, J. Puzicha. Shape matching and object recognition using shapecontext. IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(2):509-522.
[117] J. Tang, D. Liang, N. Wang, et al. A laplacian spectral method for stereo correspondence.Pattern Recognition Letters,2007,28(12):1391-1399.
[118] Y. Zheng, D. Doermann. Robust point matching for nonrigid shapes by preserving localneighborhood structure. IEEE Transactions on Pattern Analysis and Machine Intelligence,2006,28(4):643-649.
[119] F. L. Bookstein. Principal warps: thin-plate splines and decomposition of deformations.IEEE Transactions on Pattern Analysis and Machine Intelligence,1989,11(6):567-585.
[120] H. Chui, A. Rangarajan. A new algoritm for non-rigid point matching. IEEE Conferenceon Computer Vision and Pattern Recognition,2000,2:44-51.
[121] C. Schmid, A. Zisserman. Automatic line matching across views. IEEE ComputerSociety Conference on Computer Vision and Pattern Recognition,1997:666-671.
[122] C. Schmid, A. Zisserman. The geometry and matching of lines and curves over multipleviews. International Journal of Computer Vision,2000,40(3):199-233.
[123] B. Triggs. Matching constraints and the joint image. Fifth International Conference onComputer Vision,1995:338-343.
[124] T. Werner, T. Pajdla. Oriented matching constraints. In Proceedings of British MachineVision Conference, London, UK,2001:441-450.