基于直线特征的空间目标三维结构重建和位姿测量方法研究
|
摘要
本文以空间平台对空间目标采用光测图像方法进行观测为背景,研究了基于直线特征重建空间非合作目标三维结构和测量空间合作目标分离后与空间平台相对位姿的有关算法。
空间环境中存在大量不可控制或者不可预知因素,执行空间任务时摄像机系统的参数容易受到影响,因而在轨对摄像机参数进行标定具有实用意义。同时,大部分空间目标包含丰富的直线特征信息。本文充分利用直线特征的性质,展开了以下研究工作。
1、空间非合作目标提供的目标特征信息有限,不能简单采用合作目标的光测图像方法进行观测。由于直线之间存在平行、垂直和相交等相互关系,以基于直线特征的中心透视投影方程来描述摄像机成像几何模型,以场景中直线的几何关系作为算法约束,针对单幅图、图像对和序列图像的情况,开发了标定摄像机内外参数并重建空间非合作目标三维结构的新算法。这是全文的重点。
1)关于未标定的单幅图,提出了一种标定摄像机内参数的新方法,该方法利用了直线的射影不变性中的平行直线相交以及直线的相似不变性中的正交直线在相似变换中保持正交两个性质。在此基础上,基于直线的仿射不变性中的平行直线在仿射空间保持平行以及直线的相似不变性中的虚圆点在相似变换下保持不变的性质重建平面结构。
定量分析了摄像机参数选择对消影点精度的影响,研究了如何合理选择摄像机的拍摄位置和角度来高精度求取消影点。针对近景测量像差畸变大的情况,讨论了基于直线的射影不变性来纠正鱼眼镜头像差畸变的方法。
2)关于未标定的图像对,提出了基于直线的射影不变性中的交比不变和极线约束标定摄像机内外参数并重建目标三维结构的算法。
3)关于未标定的序列图像,提出了基于直线的相似不变性中的正交直线在相似变换中保持正交和光束法平差标定摄像机内外参数并重建目标三维结构的算法。
针对本文研究的空间非合作目标,设计仿真实验研究了空间平台与非合作卫星非共面一般接近以及空间平台绕飞非合作目标时,分别采用基于图像对和序列图像的方法来重建非合作目标三维结构的问题。
2、本论文提出了采用固连主副摄像机的测量方案,基于直线特征测量合作目标分离后与空间平台的相对位姿的方法。该方法分别使用主摄像机和与之固连的副摄像机实时拍摄分离系统和空间平台上的基准标定物的图像;通过提取图像中的直线特征,利用合作目标中已知的空间直线精确位置关系,推导分离目标相对合作平台位姿的解算公式,获得合作目标与空间平台的相对位姿。
3、作为图像分析的预处理步骤,研究了直线特征的提取和匹配算法。
1)提出了一种最大梯度方向Hough变换与一维高斯模板拟合相结合提取直线的两步算法,既合理利用了已有的经典算法,又提高了直线提取的精度。
2)针对三维重建的需求,提出了一种基于极线约束和RANSAC算法的直线段匹配方法,有效解决了图像中被部分遮挡直线段的匹配问题。
数字仿真和地面实验表明,本文提出的这些采用光测图像的算法对约束条件要求低,易于实现,抗噪性强,具有较高的精度,为解决空间目标观测中的摄像机参数标定、目标位姿解算和结构重建提供了若干种基于目标直线特征的实用算法。
In this dissertation, the methods and algorithms based on line feature are studied, by using which 3D structure of non-cooperative targets is recovered and relative pose between separated cooperative targets and the space platform is measured. The background of the works is to observe space targets on the space platform with the images of targets and scenes captured by cameras.
It's useful to calibrate camera parameters on-line for the camera parameters tend to be disturbed by the unpredictable factors in the harsh environment. In the meantime, there are plenty of line features in the most of the space targets, so the main contents of the works in the dissertation are done as follows by using the characteristics of line sufficiently.
1 There is little 3D information in non-cooperative targets so that 3D structure could not be reconstructed with the methods of cooperative targets simply.
The algorithms used to measure the cooperative targets can not be simply used to measure the non-cooperative targets for they can only provide limited features.
There are correlations of parallel, vertical and intersection between the straight lines, so the geometric model of camera is described with the central perspective equations based on the straight line feature and geometric constraint of the straight lines in the scene is used. The methods of camera self-calibration and 3D structure reconstruction from non-cooperative targets images, including single image, image pairs and sequential images, based on line feature are introduced in detail. This is the emphasis of the article.
(1) An algorithm is proposed from uncalibrated single image to calibrate the camera inside parameters, based on the projective invariance that parallel lines should be intersect at one point and the similarity invariance that the vertical lines remain vertical. Then, the method to reconstruct the plane is introduced when the inside parameters of camera is calibrated, based on the affine invariance that parallel lines remain parallel and the similarity invariance that the circular points are fixed points.
The effect about the choice of the camera parameters on the precision of vanishing point is analyzed quantitatively and how to choose the location and the angle of camera parameters reasonably to get the vanishing point of high precision is discussed. A method to correct the fish-eye aberration distortion, which was based on the projective invariance that straight line remains straight, was introduced to the situation of aberration distortion being deteriorated in close-range measuring.
(2) An algorithm is put forward from uncalibrated image pairs to calibrate the parameters of camera and reconstruct the 3D structure, based on the projective invariance that cross-ratio should be constant and epipolar line constraint.
(3) An algorithm is presented from uncalibrated image sequence to calibrate the parameters of camera and reconstruct the 3D structure, based on the similarity invariance that vertical lines remain vertical and bundle adjustment.
In order to study non-cooperative targets, the simulation experiments are conducted. The algorithm of image pairs is applied when the space platform and the non-cooperative target are generally approaching. The algorithm of image sequence is used when the space platform flies around the non-cooperative target.
2 The relative pose measurement methods using the system of fixed main camera and accessorial camera are applied.
The images of the space separating targets and the reference calibrated target on the space platform are captured in real-time by the main camera and accessorial camera respectively. Relative pose between the cooperative targets and the space platform is computed provided that the lines of the images captured by the cameras are extracted and the location of 3D lines in cooperative target is known accurately.
3 As steps of image pre-processing, the straight line feature extraction and the match algorithm are studied.
(1) A two-step algorithm of the straight line extraction based on the Hough transform with the gradient's maximum direction and 1D Gaussian templet fitting is proposed. It not only reasonably uses the existed classical algorithms, but also improves the accuracy of the straight line extraction.
(2) A straight-line-segment match method based on the epipolar line constraint and RANSAC algorithm is applied with regard to the demand of the 3D reconstruction, which effectively solved the matching problem of sheltering straight line segment in the image.
The algorithms presented in this dissertation are all based on line feature. They have effectively solved the problems of camera self-calibration, space targets' 3D structure and pose measurements. Their precision and efficiency are verified by a lot of digital and ground synthetic experiments.
空间环境中存在大量不可控制或者不可预知因素,执行空间任务时摄像机系统的参数容易受到影响,因而在轨对摄像机参数进行标定具有实用意义。同时,大部分空间目标包含丰富的直线特征信息。本文充分利用直线特征的性质,展开了以下研究工作。
1、空间非合作目标提供的目标特征信息有限,不能简单采用合作目标的光测图像方法进行观测。由于直线之间存在平行、垂直和相交等相互关系,以基于直线特征的中心透视投影方程来描述摄像机成像几何模型,以场景中直线的几何关系作为算法约束,针对单幅图、图像对和序列图像的情况,开发了标定摄像机内外参数并重建空间非合作目标三维结构的新算法。这是全文的重点。
1)关于未标定的单幅图,提出了一种标定摄像机内参数的新方法,该方法利用了直线的射影不变性中的平行直线相交以及直线的相似不变性中的正交直线在相似变换中保持正交两个性质。在此基础上,基于直线的仿射不变性中的平行直线在仿射空间保持平行以及直线的相似不变性中的虚圆点在相似变换下保持不变的性质重建平面结构。
定量分析了摄像机参数选择对消影点精度的影响,研究了如何合理选择摄像机的拍摄位置和角度来高精度求取消影点。针对近景测量像差畸变大的情况,讨论了基于直线的射影不变性来纠正鱼眼镜头像差畸变的方法。
2)关于未标定的图像对,提出了基于直线的射影不变性中的交比不变和极线约束标定摄像机内外参数并重建目标三维结构的算法。
3)关于未标定的序列图像,提出了基于直线的相似不变性中的正交直线在相似变换中保持正交和光束法平差标定摄像机内外参数并重建目标三维结构的算法。
针对本文研究的空间非合作目标,设计仿真实验研究了空间平台与非合作卫星非共面一般接近以及空间平台绕飞非合作目标时,分别采用基于图像对和序列图像的方法来重建非合作目标三维结构的问题。
2、本论文提出了采用固连主副摄像机的测量方案,基于直线特征测量合作目标分离后与空间平台的相对位姿的方法。该方法分别使用主摄像机和与之固连的副摄像机实时拍摄分离系统和空间平台上的基准标定物的图像;通过提取图像中的直线特征,利用合作目标中已知的空间直线精确位置关系,推导分离目标相对合作平台位姿的解算公式,获得合作目标与空间平台的相对位姿。
3、作为图像分析的预处理步骤,研究了直线特征的提取和匹配算法。
1)提出了一种最大梯度方向Hough变换与一维高斯模板拟合相结合提取直线的两步算法,既合理利用了已有的经典算法,又提高了直线提取的精度。
2)针对三维重建的需求,提出了一种基于极线约束和RANSAC算法的直线段匹配方法,有效解决了图像中被部分遮挡直线段的匹配问题。
数字仿真和地面实验表明,本文提出的这些采用光测图像的算法对约束条件要求低,易于实现,抗噪性强,具有较高的精度,为解决空间目标观测中的摄像机参数标定、目标位姿解算和结构重建提供了若干种基于目标直线特征的实用算法。
In this dissertation, the methods and algorithms based on line feature are studied, by using which 3D structure of non-cooperative targets is recovered and relative pose between separated cooperative targets and the space platform is measured. The background of the works is to observe space targets on the space platform with the images of targets and scenes captured by cameras.
It's useful to calibrate camera parameters on-line for the camera parameters tend to be disturbed by the unpredictable factors in the harsh environment. In the meantime, there are plenty of line features in the most of the space targets, so the main contents of the works in the dissertation are done as follows by using the characteristics of line sufficiently.
1 There is little 3D information in non-cooperative targets so that 3D structure could not be reconstructed with the methods of cooperative targets simply.
The algorithms used to measure the cooperative targets can not be simply used to measure the non-cooperative targets for they can only provide limited features.
There are correlations of parallel, vertical and intersection between the straight lines, so the geometric model of camera is described with the central perspective equations based on the straight line feature and geometric constraint of the straight lines in the scene is used. The methods of camera self-calibration and 3D structure reconstruction from non-cooperative targets images, including single image, image pairs and sequential images, based on line feature are introduced in detail. This is the emphasis of the article.
(1) An algorithm is proposed from uncalibrated single image to calibrate the camera inside parameters, based on the projective invariance that parallel lines should be intersect at one point and the similarity invariance that the vertical lines remain vertical. Then, the method to reconstruct the plane is introduced when the inside parameters of camera is calibrated, based on the affine invariance that parallel lines remain parallel and the similarity invariance that the circular points are fixed points.
The effect about the choice of the camera parameters on the precision of vanishing point is analyzed quantitatively and how to choose the location and the angle of camera parameters reasonably to get the vanishing point of high precision is discussed. A method to correct the fish-eye aberration distortion, which was based on the projective invariance that straight line remains straight, was introduced to the situation of aberration distortion being deteriorated in close-range measuring.
(2) An algorithm is put forward from uncalibrated image pairs to calibrate the parameters of camera and reconstruct the 3D structure, based on the projective invariance that cross-ratio should be constant and epipolar line constraint.
(3) An algorithm is presented from uncalibrated image sequence to calibrate the parameters of camera and reconstruct the 3D structure, based on the similarity invariance that vertical lines remain vertical and bundle adjustment.
In order to study non-cooperative targets, the simulation experiments are conducted. The algorithm of image pairs is applied when the space platform and the non-cooperative target are generally approaching. The algorithm of image sequence is used when the space platform flies around the non-cooperative target.
2 The relative pose measurement methods using the system of fixed main camera and accessorial camera are applied.
The images of the space separating targets and the reference calibrated target on the space platform are captured in real-time by the main camera and accessorial camera respectively. Relative pose between the cooperative targets and the space platform is computed provided that the lines of the images captured by the cameras are extracted and the location of 3D lines in cooperative target is known accurately.
3 As steps of image pre-processing, the straight line feature extraction and the match algorithm are studied.
(1) A two-step algorithm of the straight line extraction based on the Hough transform with the gradient's maximum direction and 1D Gaussian templet fitting is proposed. It not only reasonably uses the existed classical algorithms, but also improves the accuracy of the straight line extraction.
(2) A straight-line-segment match method based on the epipolar line constraint and RANSAC algorithm is applied with regard to the demand of the 3D reconstruction, which effectively solved the matching problem of sheltering straight line segment in the image.
The algorithms presented in this dissertation are all based on line feature. They have effectively solved the problems of camera self-calibration, space targets' 3D structure and pose measurements. Their precision and efficiency are verified by a lot of digital and ground synthetic experiments.
引文
[1]冯志刚,方昌华.世界各国反卫星策略综述[J].中国航天,2006,3:38-41
[2]Braun C,Stokes G H,Sridharan R,et al.The Space-Based Visible Program[J].Lincoln Laboratory Journal,1998,11(2):205-238
[3]张世杰,曹喜滨,陈闽.非合作航天器间相对位姿的单目视觉确定算法[J].南京理工大学学报(自然科学版),2006,30(05):564-568
[4]林来兴.空间交会对接技术[M].北京:国防工业出版社,1995
[5]Baker T,Davis T,Belchak T,et al.XSS-10 Micro-Satellite Demonstration Program[A].In.SSC03-IV-1,17th AIAA/USU Conference on Small Satellites[C].Logan,UT,2003.
[6]Macke J G,David P M,Michael A S,et al.Deployable Inspector Spacecraft for Distributed Field Measurements[A].In.AIAA ISSSC Conference[C].2004.
[7]Swartwout M.Deployable Inspacecraft:Technology Development and Future Applications with Solar Sails[A].In.Technology Applications Conference[C].Greenbelt,MD,2004.
[8]Wilde D,Kerstein L,Frumkin Y,et al.Inspector[A].In.49th IAF Congress[C].Melbourne,Australia,1998.
[9]Steckling M,Hil W 1.MICROSim[A].In.IAC-02-A.3.02,34th COSPAR Scientific Assembly,The Second World Space Congress[C].Houston,TX,USA,2002.
[10]Guy R,Craig I U,Jerome S.In-orbit Results from the SNAP-1 Nanosatellite and its Future Potential[J].Philosophical Transactions:Mathematical,Physical and Engineering Sciences,2003,361(1802):199-203.
[11]Hashida Y,Steyn W H.In-Orbit Attitude Performance of the 3-Axis Stabilised SNAP-1Nanosatellite[A].In.SSC01-V-1,15th Annual AIAA/USU Conference on Small Satellites [C].Itah State University,Logan Utah USA,2001
[12]百度百科.“月亮女神”号[EB/OL].http://baike.baidu.com/view/1193554.htm。
[13]于清华.微纳米技术推动国际微小卫星研发:皮、纳卫星发展现状[EB/OL].http://www.istis.sh.cn/list/list.asp?id=2561,2006-1-25
[14]王功波,郗晓宁.小卫星在轨安全分离速度设计[J].中国空间科学技术,2007,6:33-38
[15]Suzuki M,Satori S,et al.Nanosatellite Mothership-Daughtership Experiment by Japanese Universities[A].In.Proceedings of IEEE Aerospace Conference[C].Big Sky,MT,USA,2000.109-115
[16]马颂德等.计算机视觉—计算理论与算法基础[M].北京:科学出版社,1998
[17]Faugeras O,Luong Q T,Maybank S.Camera self-calIbration:Theory and experiments[A].In.Proceedings of the 2nd European Conference on Computer Vision[C].Italy,1992.321-334.
[18]Maybank S,Faugeras O.A Theory of a Moving Camera[J].International Jounal of Computer Vision.1992,8(2):123-151.
[19]孟晓桥,胡占义.摄像机自标定方法的研究与进展[J].自动化学报,2003,29(1):110-124
[20]Lourakis M A,Deriche R.Camera self-calibration using the Kruppa equations and the SVD of the fundamental matrix:The case of varying intrinsic parameters[R].Technical Report RR-3911,March 2000
[21]Lei C,Wu F,Hu Z,et al.A New Approach To Solving Kruppa Equations for Camera Self-Calibration[A].In.Int.conf.on Pattern recognition[C].Quebec City,2002.
[22]Pollefeys M,Van Gool L,Oosterlinck A.The Modulus Constraint:A New Constraint for Self-Calibration[A].In.Proc.13th International Conference on Pattern Recognition[C]. IEEE Computer Soc. Press, 1996. 349-353
[23] Pollefeys M, Van Gool L. Self-Calibration from the Absolute Conic on the Plane at Infinity [A]. In.Proceedings Of Computer Analysis Of Images And Patterns, Lecture Notes In Computer Science [C]. Spring-Verlag, 1997. 1296: 1175-1182
[24] Triggs B. Auto-Calibration and the Absolute Quadric [A]. In.Proceedings of Computer Vision and Pattern Recognition [C]. 1997. 609-614
[25] Heyden A, Astrom K. Euclidean Reconstruction from Image Sequences with Varying and Unknown Focal Length and Principal Point [A]. In.Proc. IEEE CVPR [C]. 1997. 438-443
[26] Pollefeys M, Khoch R, Gool L Van. Self-Calibration and Metric Reconstruction in Spite of Varying and Unknown Internal Camera Parameters [A]. In.Proc. 6th Int. Conf. on Computer Vision [C]. Bombay, India, January 1998. 90-95
[27] Pollefeys M, Koch R, Gool L Van. Self-Calibration and Metric Reconstruction in Spite of Varying and Unknown Intrinsic Camera Parameters [J]. Int. Journal of Computer Vision,August 1999, 32(1): 7-25
[28] Heyden A, Astrom K. Minimal Conditions on Intrinsic Parameters for Euclidean Reconstruction [A]. In.Asian Conference on Computer Vision [C]. Hong Kong, 1998.
[29] Hartley R I, Agapito L, Reid I D, et al. Camera calibration and the search for infinity [A].In.proc. 7th Int.Conf. on Computer Vision [C]. kerkyra, Greece, 1999. 510-517
[30] Bougnoux S. From Projective to Euclidean Space under Any Practical Situation, a Criticism of Self-Calibration [A]. In.Proc. 6th Int. Conf. on Computer Vision [C]. India, Jan.1998.790-796
[31] Mendonca P ,Cipolla R. A Simple Technique for Self-Calibration [A]. In.Proc. CVPR [C].Colorado: Fort Collins, June 1999. 500-505
[32] Seo Y, Heyden A. Auto-Calibration from the Orthogonality Constraints [A]. In.Proc. of the Int. Conf. on Pattern Recognition [C]. Barcelona, Spain, 2000. 1067-1072
[33] Hartley R. Estimation of Relative Camera Positions For Uncalibrated Cameras [A]. In: G.Sandini.Proc. 2nd Europ.Conf. on Comp. Vision[C]. Santa Margherita, Italy,Springer-Verlag, May 1992. 579-587
[34] Rousso B, Shilat E. Varying Focal Length Selfcalibration and Pose Estimation from Two Images [A]. In.Proc. of IEEE Conf. on Computer Vision and Pattern Recognition [C]. Santa Barnara, California, June 1998. 469-475
[35] Caprile B, Torrera V. Using Vanishing Points for Camera Calibration [J]. International Journal of Computer Vision, 1990, vol.4(2): 127-140
[36] Hartley R, Zisserman A. Multiple View Geometry in Computer Vision [M]. Cambridge University Press, 2000.
[37] Jelinek D, Taylor C J. Reconstruction of Linearly Parameterized Models from Single Images With A Camera of Unknown Focal Length [J]. IEEE Trans .on PAMI, 2001,vol.23(7): 767-774
[38] Heyden A, Huynh D Q. Auto-calibration via the Absolute Quadric And Scene Constraints [A]. In.16th International Conference On Pattern Recognition [C]. Aug. 2002, 631-634
[39] Triggs W. Auto-calibration from planar scenes [A]. In.proc.5th European Conference on Computer Vision [C]. Freiburg, Germany, 1998. 89-105
[40] Malis E, Cipolla R. Multi-view Constraints between Collineations: Application to Self-Calibration From Unknown Planar Structures [A]. In.Proc. of European Conf on Computer Vision [C]. 2000. 610-624
[41] Malis E, Cipolla R. Camera self-calibration from Unknown Planar Structures Enforcing The Multiview Constraints between Collineations [J]. IEEE Tran. on Pattern Analysis and Machine Intelligence, Sept. 2002, 24(9): 1268-1272
[42] Sturm P, Maybank S. On Plane Based Camera Calibration: A General Algorithm,Singularities, Applications [A]. In.Proc. IEEE Conf. CVPR [C]. 1999. 432-437
[43] Zhang Z. A flexible new technique for camera calibration [J]. IEEE Trans. PAMI, 2000,22(11): 1330-1334
[44]Xu G,Terai J,Shum H.A Linear Algorithm for Camera Self-Calibration,Motion And Structure Recovery For Multi-Planar Scenes From Two Perspective Images[A].In.Proc.of Conf.CVPR[C].Hilton Head Island:SC,June 2000.474-479
[45]Lv F,Zhao T,Nevatia R.Self-calibration of A Camera From Video Of A Walking Human[A].In.Proc.Of The Int.Conf.On Pattern Recognition[C].2002.562-567
[46]Mendelsohn J,Daniilidis K.Constrained selfcalibration[A].In.Proc.IEEE Int.CVPR[C].1999.501-587
[47]于起峰,陆宏伟,刘肖琳.基于图像的精密测量和运动测量[M].北京:科学出版社,2002
[48]Peter S,Triggs B.A Factorization Based Algorithm for Mulit-lmage Projective Structure and Motion[A].In.4th European Conference on Computer Vision[C].Cambridge,Apr.1996,709-720
[49]Quan L,Kanade T.Affine Structure from Line Correspondences with Uncalibrated Affine Cameras[J].In IEEE Trans.on PAMI,1997,volume 19(8):834-845
[50]Carlo T,Takeo K.Shape and Motion from Image Streams Under Orthography:A Factorization Method[J].International Journal of Computer Vision,Nov.1992,9(2):137-154
[51]王之卓.摄影测量学[M].北京:测绘出版社,1982
[52]李德仁等.基础摄影测量学[M].北京,1995
[53]单杰.立体视觉的摄影测量理论[J].武汉测绘科技大学学报,1998,23(4):
[54]Allan J,Philip F.McLauchlan.Image Mosaicing using Sequential Bundle Adjustment[A].In.British Machine Vision Conference[C].University of Bristol,September 2000.11-14
[55]Hartley R I.In Defense of the Eight-Point Algorithm[J].IEEE Trans.on PAMI,1997,19(6):580-593
[56]Faugeras O D.Stratification Of 3-D Vision:Projective,Affine and Metric Representations [J].Journal of Optical Society of America,1995,12(3):465-484
[57]Hartly R.Projective Reconstruction and Invariants From Multiple Images[J].IEEE PAMI,1994,16(10):1036-1041
[58]Sturm P,Triggs.B.A Factorization based Algorithm for Multi-lmage Projective Structure And Motion[A].In.Proceeding of the European Conference on Computer Vision[C].1996.pp.709-720
[59]Tomita F,Ueshiba T.A Factorization Method for Projective and Euclidean Reconstruction from Multiple Perspective Views via Iterative Depth Estimation[A].In.Proceeding of the European Conference on Computer Vision[C].1998.
[60]Hartley R.Euclidean Reconstruction and Invariants from Multiple Images[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1994,Vol.16(10):1036-1041
[61]Heyden A,Astrom K.Euclidean Reconstruction from Constant Intrinsic Parameters[A].In.Proceedings of International Conference of Pattern Recognition[C].Vienna,1996.339-343
[62]Pollefeys M,Van Gool L,Costerlinck A.The Modulus Constraint:A New Constraint for Self-calibration[A].In.Proceedings of International Conference of Pattern Recognition[C].Vienna,1996.349-353
[63]Szeliski R,Torr P.Geometrically Constrained Structure from Motion:Points on Planes[A].In.Proc.Eur.Workshop on 3D Structure from Multiple Images of Large-Scale Environments(SMILE)[C].1998.pp.171-186
[64]Wilczkowiak M,Boyer E,Sturm P.3D Modeling using Geometric Constraints:a Parallelepiped based Approach[A].In.Proc.7th Eur.Conf.Comput.Vision[C].2002.vol.Ⅳ,221-237.
[65]Boufama B,Ohrand R M,Veillon F.Euclidean Constraints for Uncalibrated Reconstruction[A].In.Proc.ICCV'93[C].Berlin,Germany,1993.466-470
[66]Sparr G.Euclidean and Affine Structure/Motion for Uncalibrated Cameras from Affine Shape And Subsidiary Information[A].In.Proc.SMILE Workshop[C].Freiburg,Germany,1998.187-207
[67]百度百科.嫦娥一号[EB/OL].http://baike.baidu.com/view/32160.htm。
[68]Fong C K,Cham W K.3D Object Reconstruction from Single Perspective Distorted Line Drawing Image Using Vanishing Points[A].In.Proceedings of 2005 International Symposium on Intelligent Signal Processing and Communication Systems[C].Hong Kong,December 13-16,2005.53-56
[69]李德仁等.基础摄影测量学[M].北京:测绘出版社,1995
[70]李德仁.摄影测量与遥感的现状及发展趋势[J].武汉测绘科技大学学报,2000,25(1):1-6
[71]Horn.Photogrammetry and Computer Vision[M].Optical Society of America,1987.
[72]Legac A.Videogrammetry or Digital Photogrammetry:General Remarks,Methodology,Applications[A].In.Preceedings of SPIE[C].1994.16-21
[73]龙泽斌.几何变换[M].湖南科学技术出版社,1984年3月第1版
[74]梅向明,刘增贤等.高等几何[M].高等教育出版社,1983年11月第1版
[75]Faugeras O,Toscani G.The Calibration Problem for Stereo[A].In.Proceeding of IEEE Conference of Computer Vision and Pattern Recognition[C].1986.
[76]Weng J,Cohen P,Herniou M.Camera Calibration with Distortion Models and Accuracy Evaluation[J].IEEE Trans.on Pattern Analysis and Machine Intelligence,October 1992,14(10):965-980
[77]Bougnoux S.From Projective to Euclidean Space under Any Practical Situation,A Criticism of Self-Calibration[A].In.Proceedings of the 6th International Conference on Computer Vision[C].Bombay,India,1998.790-796
[78]Huynh D Q,Heyden A.Auto-Calibration via the Absolute Quadric and Scene Constraints [A].In.16th International Conference on Pattern Recognition(ICPR'02)[C].2002.631-634
[79]Guillou E,Meneveaux D,Maisel E,et al.Using Vanishing Point for Camera Calibration and Coarse 3D Reconstruction from a Single Image[J].The Visual Computer,2000,16(7):396-410
[80]Habed A,Boufama B S.Camera Self-Calibration from Two Views[A].In.2002 IEEE International Conference on Systems,Man and Cybernetics[C].Windsor Univ.Ont.,Canada,2002.
[81]Klein F.Elementary Mathematics from an Advanced Standpoint[M].Macmillan,New York,1939
[82]王光辉.基于图像的测量及三维重建研究[D].北京:中国科学院自动化研究所,2004
[83]Riminisi A C.Accurate Visual Metrology from Single and Multiple Uncalibrated Images-Distinguished Dissertations[M].Springer Verlag Press,2001
[84]Liebowitz D,Riminisi A C,Zisserman A.Creating Architectural Models from Images[J].In:Proc.Eurographics,Milan,Italy,Sep,1999:39-50
[85]Lenz R K,Tsai R Y.Calibrating a Cartesian Robot with eye-on-hand Configuration Independent of eye-to-hand Relationship[J].IEEE Trans on PAMI,1989.11:67-75
[86]Quan L,Lan Zh.Linear N-point Camera Pose Determination[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1999,21:774-780
[87]Oberkampf D,DeMenthon D F,Davis L S.Iterative Pose Estimation using Coplanar Feature Points[J].Computer Vision and Image Understanding,1996,63(3):495-511
[88]Aggarwal J K,Wang Y F.Analysis of a Squence of Image Using Point and Line Correspondences[A].In:P.o.I.I.C.o.R.a.Automation[C].1987.1275-1280
[89]Ma S,Wei G,Huang J.Segment Based Camera Calibration[J].Journal of Computer Science and Technology,1993,8(1):11-12
[90]高满屯.基于长度和角度约束的线图解释[J].模式识别与人工智能,1993,6(3):267-272
[91]Shang Yang,Yu Qifeng,Zhang Xiaohu.A linear and Analytical Method for Camera Calibration from a Single Image with Four Coplanar Control Lines[J].Applied Optics,2004,43(28):5364-5369
[92]Kanatani K.Statistical Analysis of Focul-length Calibration Using Vanishing Points[J].IEEE Trans.on RA,Dec.1992,vol.8(6):767-775
[93]Ilkka Niini.On the Algorithmic Aspects of the Projective Block Adjustment[A].In.International Archieves of Photogrammetry and Remote Sensing[C].Columbus,Ohio,USA,1998.81-86
[94]邱志强.基于仿射近似从航空图象重建目标三维结构[D].长沙:国防科学技术大学,2004.
[95]Grossmann E.Maximum Likelihood 3D Reconstruction from One or More Uncalibrated Views under Geometric Constraints[D].Potugal:universidade Tecnica de Kisboa,2002.
[96]Criminisi A,Reid I,Zisserman A.Single view metrology[A].In:The Proceedings of the Seventh IEEE International Conference on Computer Vision[C].1999,1:434-441
[97]黄凤荣.三维重建问题研究[D].北京:中国科学院自动化研究所,2004.
[98]Liebowitz D,Zisserman A.Metric Rectificationfor Perspective Images of Planes[J].IEEE Computer Society Conference on Computer Vision and Pattern Recognition,1998,482-488
[99]Shah S,Aggarwal J K.Intrinsic Parameter Calibration Procedure for A(High-Distortion)Fish-Eys Lens Camera with Distortion Model and Accuracy Estimation[J].Pattern Recognition,1996,29(11):1175-1788
[100]Becker S,Bove V.Semi-automatic 3D Model Extraction from Uncalibrated 2D Camera Views[A].In.SPIE Visual Data Exploration and Analysis Ⅱ[C].1995.447-461
[101]Penna M.Camera Calibration:A Quick and Easy Way To Determine The Scale Factor[J].IEEE Trans.Pattern Anal.Mach.Intell,1991,13(10):1240-1245
[102]Zhang Z.Determining the Epipolar Geometry and Its Uncertainty:A Review[R].Technical Report RR-29 27,INRIA,1996.
[103]Ahmed M,Farag A.Nonmetric Calibration of Camera Lens Distortion:Differential Methods and Robust Estimation[[J].IEEE Transactions On Image Processing,August 2005,14(8):1215-1230
[104]徐士良.C常用算法程序集[M].北京,2004
[105]Mundy J L,Zisserman A.Geometric Invariance in Computer Vision[M].MIT Press,Cambridge,MA,1992
[106]Robert L,Faugeras O.Relative 3D Positioning And 3D Convex Hull Computation From A Weakly Calibrated Stereo Pair[R].In.Technical Report RR-2349.INRIA,1994.
[107]于洪川.视觉基础矩阵的算法及其应用研究[D].北京:中国科学院等离子体物理研究所,2000
[108]Longuet-Higgins H C.A Computer Algorithm for Reconstructing A Scene from Two Projections[J].Nature,1981,293:133-135
[109]Quan L,Deriche R.On Determining the Fundamental Matrix:Analysis of Different Methods and Experimental Results[R].Technical Report RR-1894,INRIA,1993.
[110]Hartley R I.In Defence of the 8-point Algorithm[C].Cambridge(MA):Proceeding 5th International Conference on Computer Vision,1995,1064-1070
[111]Kruppa E.Zur Ermittlung eines Objektes aus zwei Perspektiven mit innerer Orientierung.Akad.Wiss.Wien.Math.-Naturwiss.K1.Sitzungsber[J].1913,22:1939-1948
[112]Horn.Photogrammetry and Computer Vision[J].J.Optical Soc.Am.A,1987,4:629-642
[113]Ullman S.The Interpretation of Visual Motion[M].M.I.T.Press,Cambridge,MA 1979.
[114]Kontsevich L L.Pairwise Comparison Technique:a Simple Solution for Depth Reconstruction[J].J.Opt.Soc.Am.A,1993,10(6):1129-1135
[115]Zhang Z.Estimating Motion and Structure from Correspondences of Line Segments between Two Perspective images[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1995:1129-1139
[116]Hartley R.Extracting of Focal Lengths from The Fundamental Matrix[A].In.GE internal report[C].CRD,Schenectady,NY,1993.
[117]Pollefeys M.Self-calibration and Metric 3D Reconstruction from Uncalibrated Image Sequences[D].Katholieke Universiteit Leuven,1999
[118]Foroosh H,Xiaochun Cao,Balci M.Metrology in Uncalibrated Images Given One Vanishing Point[A].In.ICIP Genoa[C].Italy,2005.361-364
[119]Quan L,Kanade T.Affine Structure from Line Correspondences with Uncalibrated Affine Cameras[J].In IEEE Trans on PAMI,1997,volume 19(8):834-845
[120]Kahi F,Heyden A.Affine Structure and Motion from Points,Lines and Conics[J].In IJC,1999,33(3):163-180
[121]Hu M L,Zhang Q B,Wei S.Projective Reconstruction from Line Based on SVD[A].In.Proceedings of International Conference on Image and Gaphics[C].2002.896-902
[122]Heuvel E A.A Line-photogrammetry Mathematical Model for The Reconstruction of Polyhedral Objects[J].Proceedings of SPIE,1999,3641:60-71
[123]Paul E,Debevec.Modelling and Rendering Architecture from Photographs:A hybrid Geometry-and Image-based Approach[J].SIGGRAPH,1996.
[124]张永军.基于序列图像的工业钣金件三维重建与视觉检测[D].武汉:武汉大学博士论文,2002.
[125]雷成,吴福朝,胡占义.Kruppa方程与摄像机自标定[J].自动化学报,2001,27(5):621-630
[126]Hartley R,Gupta R.Stereo from Uncalibrated Cameras[J].Computer Vision and Pattern Recognition,1992:761-764
[127]Hartley R I.Computer Society Conference on Projective Reconstruction from Line Correspondences[J].Computer Vision and Pattern Recognition,1994:903-907
[128]Hartley R I.A Linear Method for Reconstruction from Lines and Points[A].In.Fifth International Conference on Computer Vision[C].1995.882-887
[129]Quan L,Kanade T.Affine Structure from Line Correspondences with Uncalibrated Affine Cameras[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1997:834-845
[130]Quan L,Kanade T.A factorization Method for Affine Structure from Line Correspondences[A].In.IEEE Computer Society Conference on Computer Vision and Pattern Recognition[C].1996.803-808
[131]Chen S,Tsai W.A Systematic Approach to Analytic Determination of Camera Parameters by Line Features[J].pattern Recongnition,1990,23(8):859-877
[132]Frank A,van den Heuvel.Estimation of Interior Oriertation Parameters from Constraints on Line Measurements in A Single Image[J].International Archives of photogrammetry and Remote sensing,1999,32(t5):81-88
[133]Chen T,Shibasaki R S.Determination of Camera's Orientation Parameters Based on Line Features[J].International Archives of Photogrammetry and Remote sensing,Hakodate 1998.
[134]Cipolla R,Robertson D,Edmend boyer.Photobuilder-3D Models of Architectural Scenes from Uncalibrated Images[A].In.Proc.IEEE International Conference on Multimedia Computing and Systems[C].Firenze,1999.1:25-31
[135]Mass H G.Image Sequence Based Automatic Multi-Camera System Calibration Technique[EB/OL].http://www.geo.tudelft.nl/frs/papers/1998/Masslmage.pdf.
[136]Heyden A,Huynh D Q.Auto-calibration via the Absolute Quadric and Scene Constraints [A].In.16th International Conference on Pattern Recognition[C].11-15 Aug.2002.
[137]Ha J E,Kweon I S.3D Structure Recovery And Calibration under Varying Intrinsic Parameters using Known Angles[J].Pattern Recognition,2001,34:351-359
[138]Pollefeys M,Van Gool L,Oosterlinck A.The Modulus Constraint:A New Constraint for Self-Calibration[A].In.Proc.13th International Conference on Pattern Recognition[C].IEEE Computer Soc.Press,1996.349-353
[139]Ma S D.A Self-CalibratiOn Technique for Active Vision System.IEEE Transactions on Robot Automation,1996,12(1):114-120
[140]孟云鹤.近地轨道航天器编队飞行控制与应用研究[D].长沙:国防科学技术大学,2006.
[141]Homer H Chen.Pose Determination from Line-To-Plane Correspondences:Existence Condition and Closed-Form Solutions[J].IEEE Transactions on Patten Analysis and Machine Intelligence,1991,13(6):530-541
[142]Hyonam Joo Robert,Haralick M,Chung-Nanlee.Pose Estimation from Corresponding Point Data[J].IEEE Transactions on Systems,Man.and Cybernetics,1989,19(6):1426-1446
[143]David P,Dementhon D,Duraiswami R.,et al.Simultaneous Pose and Correspondence Determination Using Line Feature[A].In.Proceedings of IEEE Computer Vision and Pattern Recognition[C].2003.424-431
[144]Dornaika F,Garcia C.Pose Estimation Using Point and Line Correspondences[J].Journal of Real-Time Imaging,1999,5(3):217-232
[145]Cropp A,Palmer P L.Estimation the Pose of a Known Target Satellite[J].IEE Electronic letters,2000,36(5):1331-1332
[146]Cropp A,Palmer P L,Underwood C I.Pose Estimation of the Target Satellite for Proximity Operations[A].In.14th Annual/USU Conference on Small Statellite[C].Utah,logan,USA,2000.
[147]高满屯.利用直线对应计算纯旋转运动参数的一种线性方法[J].西北工业大学学报,1992,14(3):24-27
[148]Chang C,Tsai W.Reliable Determination of Object Pose from Line Features by Hypothesis Testing[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1999,21(12):1235-1241
[149]Holt J R,Netravali A N.Uniqueness of Solutions to Structure and Motion from Combinations of Point and Line Correspondences[J].Journal of Visual Communication and Image Representation,1996,7(2):126-136
[150]Phong T Q,Horaud R,Yassine A.Object Pose from 2-D to 3-D Point and Line Correspondences[J].International Journal of Computer Vision,1999,15(3):225-243
[151]Navab N,Faugeras O D.Monocular Pose Determination from Lines:Critical Sets and Maximum Number of Solutions[A].In.IEEE Conf.Computer Vision and Pattern Recognition[C].1993.254-260
[152]Christy S,Hora R.Iterative Pose Computation from Line Correspondences[J].Computer Vision and Image Understanding,1999,73(1):137-144
[153]van den Heuvel,Frank A.Exterior Orientation using Coplanar Parallel Lines[A].In.Proceedings of the 10th Scandinavian Conference on Image Analysis[C].Lappeenranta,1997.71-78
[154]Dhome M,Richetin M.Determination of the Attitude of 3D Objects from a Single Perspective View[J].IEEE Transactions on Patten Analysis and Machine Intelligence,1989,11(12):261-265
[155]Hanek R,Navab N,Appel M.Yet Another Method for Pose Estimation:A Probabilistic Aprroach using Points,Lines and Cylinders[J].1999.
[156]Chesi G,Hashimoto K.Camera Pose Estimation from Less Than Eight Points in Visual Servoing[A].In.Proceedings of the 2004 IEEE International Conference on Robotics and Automation[C].2004.733-738
[157]Huang T S,Liu Y,Faugeras O D.Determination of Camera Location from 2-D To 3-D Line And Point Correspondences[J].IEEE Transactions on Patten Analysis and Machine Intelligence,1990,12(1):28-37
[158]Horaud R,Phong T Q,Yassine A.Object Pose From 2-D To 3-D Point and Line Correspondences[J].International Journal of Computer Vision,1999,15(3):225-243
[159]Wang F Y.A Simple and Analytical Procedure for Calibrating Extrinsic Camera Parameters[J].IEEE Tranctions on robotics and automation,2004,20(1):121-124
[160]Lu Chien-Ping,Gregory D.Fast and Globally Convergent Pose Estimation From Video Images[J].IEEE Transactions on Patten Analysis and Machine Intelligence,2000,22(6):610-622
[161]尚洋.基于视觉的空间目标位置姿态测量方法研究[D].长沙:国防科学技术大学,2006.
[162]何斌,马天予,王运坚等.Visual C++数字图像处理[M].北京:人民邮电出版社,2001.
[163]王润生.图像理解[M].国防科技大学出版社,1995年10月第一版.
[164]章国宝,叶桦,陈维南.基于正交小波变换的多尺度边缘检测[J].中国图像图形学报,1998,3(8):651-653
[165]Keller J M,Chen S R,Crownover M.Texture Description and Segmentation through Fractal Geometry[J].CVGIP,1989,45:150-166
[166]Manjunath B S,Ma W Y.Texture Features for Browsing and Retrieval of Image Data[J].IEEE PAMI,1996,18(8):837-842
[167]Song J,Delp E J.The Analysis of Morphological Filters with Multiple Structuring Elements[J].Computer Vision Graphics and Image Processing,1990,50:308-328
[168]Illingworth J,Kittler J.A survey of the Hough transform.Computer Vision[J].Graphics and Image Processing,1988,44(1):87-116
[169]Nevatia R,Babu K.Linear Feature Extraction and Description[J].Computer Graphics and Image Processing,1980,13(3):257-269
[170]Boldt M,Weiss R,Riseman E.Token-Based extraction of straight lines[J].IEEE Transactions on System,Man Cyber-netics,1989,19(7):1581-1594
[171]Burns J,Hanson A,Riseman E.Extracting Straight Lines[J].IEEE Transactions on Pattern Analysis and Machine Intelligent,1986,8(4):425-455
[172]Kass M,Witkin A,Terzopoulos D.Snakes:Active Contour Models[J].International Journal of Computer Vision,1987:321-331
[173]唐亮.直线Snakes及其在建筑物提取中的应用[J].西安电子科技大学学报(自然科学版)Feb.2005,32(1):60-65
[174]Baker S.Design and Evaluation of Feature Detectors[D].U S A:Columbia University,1998
[175]张小虎,李由等.一种基于梯度方向强度直方图的直线轮廓提取新方法[J].光学技术,2006,32(6):
[176]Castleman K R.数字图像处理.北京:清华大学出版社,1998
[177]Brown G L.A Survey of Image Registration Techniques[J].ACM Computing Survey,1992,24(4):325-376
[178]Barbara Zitova,Jan Flusser.Image Registration Methods:A Survey[J].Image and Vision Computing,2003,24(11):977-1000
[179]Moravec H P.Toward Automatic Visual Obastacle Avoidance[A].In.Proc.5th Int.Joint Conf.Artificial Intelligence[C].Cabbridge,MA,1977.584-596
[180]Harris C G,Stephens M J.A Combined Corner and Edge Detector[A].In.Proceedings Fourth Alvey Vision Conference[C].Manchester,1988 147-151
[181]Scott G,Longuet-Higgins H.An Algorithm for Associating the Features of Two Patterns [A].In.Proceeding of Royal Society[C].London,1991.21-26
[182]Pilu M.A Direct Method for Stereo Correspondence Based on SVD[A].In.Proceeding of CVPR'97[C].1997.261-266
[183]Schmid C,Zisserman A.Automatic Line Matching Across Views[A].In:Procceding of the Conference on Computer Vision and Pattern Recognition[C].Puerlo Rico,UAS,1997,666-671
[184]Noronha S,Nevatia R.Detection and Modeling of Buildings from Multiple Aerial lmges[A].In.IEEE International Conference on Computer Vision and Pattern Recognition [C].San Juan,Puerto 1997.588-594
[185]Cheng Y Q,Riseman E M,Wang X Q,et al.Three-Dimensional Reconstruction of Points And Lines With Unknown Correspondence Across images[J].International Journal of Computer Vision(IJCV),Nov.2001,Vol.45(2):129-156
[186]席学强,工润生.基十直线特征的图像一模型匹配算法[J].国防科技大学学报,2000,22(6):70-74
[187]彭梦,蔡自兴.基十直线段匹配的移动机器人的障碍物榆测[J].图像与多媒体技术,2007.5:66-68
[188]崔巍,强文义,陈兴林.基于直线间结构信息的立体视觉图像动态匹配方法….控制与决策,2003,18(5):633-635
[189]黄良明,彭立中等.利用几何代数进行线段模型匹配和运动估计[J].中国图象图形学报2001,6(3):270-274
[190]季健,昂海松,王旭刚,周新春.序列图像中直线边缘快速提取和匹配[J].南京航空航天大学学报.2005,37(2):227-231
[191]Behzad K P.Line Matching:Solutions and Unsolved Problems[A].In.Proceedings of International Conference on Image Processing[C].Naval Res.Lab,Washington,DC,USA,2001.905-908
[192]Zhang Z.A Robust Technique for Matching Two Uncalibrated Images Through the Recovery of the Unknown Epipolar Geometry[R].May,1994.
[193]周军 徐奕,周源华.基于稀疏-密集匹配算法的视差度量[J].红外与激光工程,2003,32(6):630-634
[194]杨杰,郑元杰.基于单摄像头双日成像系统在计算机视觉中的应用研究[J].红外与激光工程,2004,33(4):392-396
[195]张祖勋,张剑清.数字摄影测量学[M].武汉:武汉测绘科技大学出版社,1996
[196]徐宝昌等.一种改进的最小二乘景像匹配算法[J].北京航空航天大学学报,2005,31(18):848-852
[197]孙即祥 编著.数字图象处理[M].河北:河北教育出版社,1993
[198]齐治昌.数值分析及其应用[M].长沙:国防科学技术大学出版社,2002
[2]Braun C,Stokes G H,Sridharan R,et al.The Space-Based Visible Program[J].Lincoln Laboratory Journal,1998,11(2):205-238
[3]张世杰,曹喜滨,陈闽.非合作航天器间相对位姿的单目视觉确定算法[J].南京理工大学学报(自然科学版),2006,30(05):564-568
[4]林来兴.空间交会对接技术[M].北京:国防工业出版社,1995
[5]Baker T,Davis T,Belchak T,et al.XSS-10 Micro-Satellite Demonstration Program[A].In.SSC03-IV-1,17th AIAA/USU Conference on Small Satellites[C].Logan,UT,2003.
[6]Macke J G,David P M,Michael A S,et al.Deployable Inspector Spacecraft for Distributed Field Measurements[A].In.AIAA ISSSC Conference[C].2004.
[7]Swartwout M.Deployable Inspacecraft:Technology Development and Future Applications with Solar Sails[A].In.Technology Applications Conference[C].Greenbelt,MD,2004.
[8]Wilde D,Kerstein L,Frumkin Y,et al.Inspector[A].In.49th IAF Congress[C].Melbourne,Australia,1998.
[9]Steckling M,Hil W 1.MICROSim[A].In.IAC-02-A.3.02,34th COSPAR Scientific Assembly,The Second World Space Congress[C].Houston,TX,USA,2002.
[10]Guy R,Craig I U,Jerome S.In-orbit Results from the SNAP-1 Nanosatellite and its Future Potential[J].Philosophical Transactions:Mathematical,Physical and Engineering Sciences,2003,361(1802):199-203.
[11]Hashida Y,Steyn W H.In-Orbit Attitude Performance of the 3-Axis Stabilised SNAP-1Nanosatellite[A].In.SSC01-V-1,15th Annual AIAA/USU Conference on Small Satellites [C].Itah State University,Logan Utah USA,2001
[12]百度百科.“月亮女神”号[EB/OL].http://baike.baidu.com/view/1193554.htm。
[13]于清华.微纳米技术推动国际微小卫星研发:皮、纳卫星发展现状[EB/OL].http://www.istis.sh.cn/list/list.asp?id=2561,2006-1-25
[14]王功波,郗晓宁.小卫星在轨安全分离速度设计[J].中国空间科学技术,2007,6:33-38
[15]Suzuki M,Satori S,et al.Nanosatellite Mothership-Daughtership Experiment by Japanese Universities[A].In.Proceedings of IEEE Aerospace Conference[C].Big Sky,MT,USA,2000.109-115
[16]马颂德等.计算机视觉—计算理论与算法基础[M].北京:科学出版社,1998
[17]Faugeras O,Luong Q T,Maybank S.Camera self-calIbration:Theory and experiments[A].In.Proceedings of the 2nd European Conference on Computer Vision[C].Italy,1992.321-334.
[18]Maybank S,Faugeras O.A Theory of a Moving Camera[J].International Jounal of Computer Vision.1992,8(2):123-151.
[19]孟晓桥,胡占义.摄像机自标定方法的研究与进展[J].自动化学报,2003,29(1):110-124
[20]Lourakis M A,Deriche R.Camera self-calibration using the Kruppa equations and the SVD of the fundamental matrix:The case of varying intrinsic parameters[R].Technical Report RR-3911,March 2000
[21]Lei C,Wu F,Hu Z,et al.A New Approach To Solving Kruppa Equations for Camera Self-Calibration[A].In.Int.conf.on Pattern recognition[C].Quebec City,2002.
[22]Pollefeys M,Van Gool L,Oosterlinck A.The Modulus Constraint:A New Constraint for Self-Calibration[A].In.Proc.13th International Conference on Pattern Recognition[C]. IEEE Computer Soc. Press, 1996. 349-353
[23] Pollefeys M, Van Gool L. Self-Calibration from the Absolute Conic on the Plane at Infinity [A]. In.Proceedings Of Computer Analysis Of Images And Patterns, Lecture Notes In Computer Science [C]. Spring-Verlag, 1997. 1296: 1175-1182
[24] Triggs B. Auto-Calibration and the Absolute Quadric [A]. In.Proceedings of Computer Vision and Pattern Recognition [C]. 1997. 609-614
[25] Heyden A, Astrom K. Euclidean Reconstruction from Image Sequences with Varying and Unknown Focal Length and Principal Point [A]. In.Proc. IEEE CVPR [C]. 1997. 438-443
[26] Pollefeys M, Khoch R, Gool L Van. Self-Calibration and Metric Reconstruction in Spite of Varying and Unknown Internal Camera Parameters [A]. In.Proc. 6th Int. Conf. on Computer Vision [C]. Bombay, India, January 1998. 90-95
[27] Pollefeys M, Koch R, Gool L Van. Self-Calibration and Metric Reconstruction in Spite of Varying and Unknown Intrinsic Camera Parameters [J]. Int. Journal of Computer Vision,August 1999, 32(1): 7-25
[28] Heyden A, Astrom K. Minimal Conditions on Intrinsic Parameters for Euclidean Reconstruction [A]. In.Asian Conference on Computer Vision [C]. Hong Kong, 1998.
[29] Hartley R I, Agapito L, Reid I D, et al. Camera calibration and the search for infinity [A].In.proc. 7th Int.Conf. on Computer Vision [C]. kerkyra, Greece, 1999. 510-517
[30] Bougnoux S. From Projective to Euclidean Space under Any Practical Situation, a Criticism of Self-Calibration [A]. In.Proc. 6th Int. Conf. on Computer Vision [C]. India, Jan.1998.790-796
[31] Mendonca P ,Cipolla R. A Simple Technique for Self-Calibration [A]. In.Proc. CVPR [C].Colorado: Fort Collins, June 1999. 500-505
[32] Seo Y, Heyden A. Auto-Calibration from the Orthogonality Constraints [A]. In.Proc. of the Int. Conf. on Pattern Recognition [C]. Barcelona, Spain, 2000. 1067-1072
[33] Hartley R. Estimation of Relative Camera Positions For Uncalibrated Cameras [A]. In: G.Sandini.Proc. 2nd Europ.Conf. on Comp. Vision[C]. Santa Margherita, Italy,Springer-Verlag, May 1992. 579-587
[34] Rousso B, Shilat E. Varying Focal Length Selfcalibration and Pose Estimation from Two Images [A]. In.Proc. of IEEE Conf. on Computer Vision and Pattern Recognition [C]. Santa Barnara, California, June 1998. 469-475
[35] Caprile B, Torrera V. Using Vanishing Points for Camera Calibration [J]. International Journal of Computer Vision, 1990, vol.4(2): 127-140
[36] Hartley R, Zisserman A. Multiple View Geometry in Computer Vision [M]. Cambridge University Press, 2000.
[37] Jelinek D, Taylor C J. Reconstruction of Linearly Parameterized Models from Single Images With A Camera of Unknown Focal Length [J]. IEEE Trans .on PAMI, 2001,vol.23(7): 767-774
[38] Heyden A, Huynh D Q. Auto-calibration via the Absolute Quadric And Scene Constraints [A]. In.16th International Conference On Pattern Recognition [C]. Aug. 2002, 631-634
[39] Triggs W. Auto-calibration from planar scenes [A]. In.proc.5th European Conference on Computer Vision [C]. Freiburg, Germany, 1998. 89-105
[40] Malis E, Cipolla R. Multi-view Constraints between Collineations: Application to Self-Calibration From Unknown Planar Structures [A]. In.Proc. of European Conf on Computer Vision [C]. 2000. 610-624
[41] Malis E, Cipolla R. Camera self-calibration from Unknown Planar Structures Enforcing The Multiview Constraints between Collineations [J]. IEEE Tran. on Pattern Analysis and Machine Intelligence, Sept. 2002, 24(9): 1268-1272
[42] Sturm P, Maybank S. On Plane Based Camera Calibration: A General Algorithm,Singularities, Applications [A]. In.Proc. IEEE Conf. CVPR [C]. 1999. 432-437
[43] Zhang Z. A flexible new technique for camera calibration [J]. IEEE Trans. PAMI, 2000,22(11): 1330-1334
[44]Xu G,Terai J,Shum H.A Linear Algorithm for Camera Self-Calibration,Motion And Structure Recovery For Multi-Planar Scenes From Two Perspective Images[A].In.Proc.of Conf.CVPR[C].Hilton Head Island:SC,June 2000.474-479
[45]Lv F,Zhao T,Nevatia R.Self-calibration of A Camera From Video Of A Walking Human[A].In.Proc.Of The Int.Conf.On Pattern Recognition[C].2002.562-567
[46]Mendelsohn J,Daniilidis K.Constrained selfcalibration[A].In.Proc.IEEE Int.CVPR[C].1999.501-587
[47]于起峰,陆宏伟,刘肖琳.基于图像的精密测量和运动测量[M].北京:科学出版社,2002
[48]Peter S,Triggs B.A Factorization Based Algorithm for Mulit-lmage Projective Structure and Motion[A].In.4th European Conference on Computer Vision[C].Cambridge,Apr.1996,709-720
[49]Quan L,Kanade T.Affine Structure from Line Correspondences with Uncalibrated Affine Cameras[J].In IEEE Trans.on PAMI,1997,volume 19(8):834-845
[50]Carlo T,Takeo K.Shape and Motion from Image Streams Under Orthography:A Factorization Method[J].International Journal of Computer Vision,Nov.1992,9(2):137-154
[51]王之卓.摄影测量学[M].北京:测绘出版社,1982
[52]李德仁等.基础摄影测量学[M].北京,1995
[53]单杰.立体视觉的摄影测量理论[J].武汉测绘科技大学学报,1998,23(4):
[54]Allan J,Philip F.McLauchlan.Image Mosaicing using Sequential Bundle Adjustment[A].In.British Machine Vision Conference[C].University of Bristol,September 2000.11-14
[55]Hartley R I.In Defense of the Eight-Point Algorithm[J].IEEE Trans.on PAMI,1997,19(6):580-593
[56]Faugeras O D.Stratification Of 3-D Vision:Projective,Affine and Metric Representations [J].Journal of Optical Society of America,1995,12(3):465-484
[57]Hartly R.Projective Reconstruction and Invariants From Multiple Images[J].IEEE PAMI,1994,16(10):1036-1041
[58]Sturm P,Triggs.B.A Factorization based Algorithm for Multi-lmage Projective Structure And Motion[A].In.Proceeding of the European Conference on Computer Vision[C].1996.pp.709-720
[59]Tomita F,Ueshiba T.A Factorization Method for Projective and Euclidean Reconstruction from Multiple Perspective Views via Iterative Depth Estimation[A].In.Proceeding of the European Conference on Computer Vision[C].1998.
[60]Hartley R.Euclidean Reconstruction and Invariants from Multiple Images[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1994,Vol.16(10):1036-1041
[61]Heyden A,Astrom K.Euclidean Reconstruction from Constant Intrinsic Parameters[A].In.Proceedings of International Conference of Pattern Recognition[C].Vienna,1996.339-343
[62]Pollefeys M,Van Gool L,Costerlinck A.The Modulus Constraint:A New Constraint for Self-calibration[A].In.Proceedings of International Conference of Pattern Recognition[C].Vienna,1996.349-353
[63]Szeliski R,Torr P.Geometrically Constrained Structure from Motion:Points on Planes[A].In.Proc.Eur.Workshop on 3D Structure from Multiple Images of Large-Scale Environments(SMILE)[C].1998.pp.171-186
[64]Wilczkowiak M,Boyer E,Sturm P.3D Modeling using Geometric Constraints:a Parallelepiped based Approach[A].In.Proc.7th Eur.Conf.Comput.Vision[C].2002.vol.Ⅳ,221-237.
[65]Boufama B,Ohrand R M,Veillon F.Euclidean Constraints for Uncalibrated Reconstruction[A].In.Proc.ICCV'93[C].Berlin,Germany,1993.466-470
[66]Sparr G.Euclidean and Affine Structure/Motion for Uncalibrated Cameras from Affine Shape And Subsidiary Information[A].In.Proc.SMILE Workshop[C].Freiburg,Germany,1998.187-207
[67]百度百科.嫦娥一号[EB/OL].http://baike.baidu.com/view/32160.htm。
[68]Fong C K,Cham W K.3D Object Reconstruction from Single Perspective Distorted Line Drawing Image Using Vanishing Points[A].In.Proceedings of 2005 International Symposium on Intelligent Signal Processing and Communication Systems[C].Hong Kong,December 13-16,2005.53-56
[69]李德仁等.基础摄影测量学[M].北京:测绘出版社,1995
[70]李德仁.摄影测量与遥感的现状及发展趋势[J].武汉测绘科技大学学报,2000,25(1):1-6
[71]Horn.Photogrammetry and Computer Vision[M].Optical Society of America,1987.
[72]Legac A.Videogrammetry or Digital Photogrammetry:General Remarks,Methodology,Applications[A].In.Preceedings of SPIE[C].1994.16-21
[73]龙泽斌.几何变换[M].湖南科学技术出版社,1984年3月第1版
[74]梅向明,刘增贤等.高等几何[M].高等教育出版社,1983年11月第1版
[75]Faugeras O,Toscani G.The Calibration Problem for Stereo[A].In.Proceeding of IEEE Conference of Computer Vision and Pattern Recognition[C].1986.
[76]Weng J,Cohen P,Herniou M.Camera Calibration with Distortion Models and Accuracy Evaluation[J].IEEE Trans.on Pattern Analysis and Machine Intelligence,October 1992,14(10):965-980
[77]Bougnoux S.From Projective to Euclidean Space under Any Practical Situation,A Criticism of Self-Calibration[A].In.Proceedings of the 6th International Conference on Computer Vision[C].Bombay,India,1998.790-796
[78]Huynh D Q,Heyden A.Auto-Calibration via the Absolute Quadric and Scene Constraints [A].In.16th International Conference on Pattern Recognition(ICPR'02)[C].2002.631-634
[79]Guillou E,Meneveaux D,Maisel E,et al.Using Vanishing Point for Camera Calibration and Coarse 3D Reconstruction from a Single Image[J].The Visual Computer,2000,16(7):396-410
[80]Habed A,Boufama B S.Camera Self-Calibration from Two Views[A].In.2002 IEEE International Conference on Systems,Man and Cybernetics[C].Windsor Univ.Ont.,Canada,2002.
[81]Klein F.Elementary Mathematics from an Advanced Standpoint[M].Macmillan,New York,1939
[82]王光辉.基于图像的测量及三维重建研究[D].北京:中国科学院自动化研究所,2004
[83]Riminisi A C.Accurate Visual Metrology from Single and Multiple Uncalibrated Images-Distinguished Dissertations[M].Springer Verlag Press,2001
[84]Liebowitz D,Riminisi A C,Zisserman A.Creating Architectural Models from Images[J].In:Proc.Eurographics,Milan,Italy,Sep,1999:39-50
[85]Lenz R K,Tsai R Y.Calibrating a Cartesian Robot with eye-on-hand Configuration Independent of eye-to-hand Relationship[J].IEEE Trans on PAMI,1989.11:67-75
[86]Quan L,Lan Zh.Linear N-point Camera Pose Determination[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1999,21:774-780
[87]Oberkampf D,DeMenthon D F,Davis L S.Iterative Pose Estimation using Coplanar Feature Points[J].Computer Vision and Image Understanding,1996,63(3):495-511
[88]Aggarwal J K,Wang Y F.Analysis of a Squence of Image Using Point and Line Correspondences[A].In:P.o.I.I.C.o.R.a.Automation[C].1987.1275-1280
[89]Ma S,Wei G,Huang J.Segment Based Camera Calibration[J].Journal of Computer Science and Technology,1993,8(1):11-12
[90]高满屯.基于长度和角度约束的线图解释[J].模式识别与人工智能,1993,6(3):267-272
[91]Shang Yang,Yu Qifeng,Zhang Xiaohu.A linear and Analytical Method for Camera Calibration from a Single Image with Four Coplanar Control Lines[J].Applied Optics,2004,43(28):5364-5369
[92]Kanatani K.Statistical Analysis of Focul-length Calibration Using Vanishing Points[J].IEEE Trans.on RA,Dec.1992,vol.8(6):767-775
[93]Ilkka Niini.On the Algorithmic Aspects of the Projective Block Adjustment[A].In.International Archieves of Photogrammetry and Remote Sensing[C].Columbus,Ohio,USA,1998.81-86
[94]邱志强.基于仿射近似从航空图象重建目标三维结构[D].长沙:国防科学技术大学,2004.
[95]Grossmann E.Maximum Likelihood 3D Reconstruction from One or More Uncalibrated Views under Geometric Constraints[D].Potugal:universidade Tecnica de Kisboa,2002.
[96]Criminisi A,Reid I,Zisserman A.Single view metrology[A].In:The Proceedings of the Seventh IEEE International Conference on Computer Vision[C].1999,1:434-441
[97]黄凤荣.三维重建问题研究[D].北京:中国科学院自动化研究所,2004.
[98]Liebowitz D,Zisserman A.Metric Rectificationfor Perspective Images of Planes[J].IEEE Computer Society Conference on Computer Vision and Pattern Recognition,1998,482-488
[99]Shah S,Aggarwal J K.Intrinsic Parameter Calibration Procedure for A(High-Distortion)Fish-Eys Lens Camera with Distortion Model and Accuracy Estimation[J].Pattern Recognition,1996,29(11):1175-1788
[100]Becker S,Bove V.Semi-automatic 3D Model Extraction from Uncalibrated 2D Camera Views[A].In.SPIE Visual Data Exploration and Analysis Ⅱ[C].1995.447-461
[101]Penna M.Camera Calibration:A Quick and Easy Way To Determine The Scale Factor[J].IEEE Trans.Pattern Anal.Mach.Intell,1991,13(10):1240-1245
[102]Zhang Z.Determining the Epipolar Geometry and Its Uncertainty:A Review[R].Technical Report RR-29 27,INRIA,1996.
[103]Ahmed M,Farag A.Nonmetric Calibration of Camera Lens Distortion:Differential Methods and Robust Estimation[[J].IEEE Transactions On Image Processing,August 2005,14(8):1215-1230
[104]徐士良.C常用算法程序集[M].北京,2004
[105]Mundy J L,Zisserman A.Geometric Invariance in Computer Vision[M].MIT Press,Cambridge,MA,1992
[106]Robert L,Faugeras O.Relative 3D Positioning And 3D Convex Hull Computation From A Weakly Calibrated Stereo Pair[R].In.Technical Report RR-2349.INRIA,1994.
[107]于洪川.视觉基础矩阵的算法及其应用研究[D].北京:中国科学院等离子体物理研究所,2000
[108]Longuet-Higgins H C.A Computer Algorithm for Reconstructing A Scene from Two Projections[J].Nature,1981,293:133-135
[109]Quan L,Deriche R.On Determining the Fundamental Matrix:Analysis of Different Methods and Experimental Results[R].Technical Report RR-1894,INRIA,1993.
[110]Hartley R I.In Defence of the 8-point Algorithm[C].Cambridge(MA):Proceeding 5th International Conference on Computer Vision,1995,1064-1070
[111]Kruppa E.Zur Ermittlung eines Objektes aus zwei Perspektiven mit innerer Orientierung.Akad.Wiss.Wien.Math.-Naturwiss.K1.Sitzungsber[J].1913,22:1939-1948
[112]Horn.Photogrammetry and Computer Vision[J].J.Optical Soc.Am.A,1987,4:629-642
[113]Ullman S.The Interpretation of Visual Motion[M].M.I.T.Press,Cambridge,MA 1979.
[114]Kontsevich L L.Pairwise Comparison Technique:a Simple Solution for Depth Reconstruction[J].J.Opt.Soc.Am.A,1993,10(6):1129-1135
[115]Zhang Z.Estimating Motion and Structure from Correspondences of Line Segments between Two Perspective images[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1995:1129-1139
[116]Hartley R.Extracting of Focal Lengths from The Fundamental Matrix[A].In.GE internal report[C].CRD,Schenectady,NY,1993.
[117]Pollefeys M.Self-calibration and Metric 3D Reconstruction from Uncalibrated Image Sequences[D].Katholieke Universiteit Leuven,1999
[118]Foroosh H,Xiaochun Cao,Balci M.Metrology in Uncalibrated Images Given One Vanishing Point[A].In.ICIP Genoa[C].Italy,2005.361-364
[119]Quan L,Kanade T.Affine Structure from Line Correspondences with Uncalibrated Affine Cameras[J].In IEEE Trans on PAMI,1997,volume 19(8):834-845
[120]Kahi F,Heyden A.Affine Structure and Motion from Points,Lines and Conics[J].In IJC,1999,33(3):163-180
[121]Hu M L,Zhang Q B,Wei S.Projective Reconstruction from Line Based on SVD[A].In.Proceedings of International Conference on Image and Gaphics[C].2002.896-902
[122]Heuvel E A.A Line-photogrammetry Mathematical Model for The Reconstruction of Polyhedral Objects[J].Proceedings of SPIE,1999,3641:60-71
[123]Paul E,Debevec.Modelling and Rendering Architecture from Photographs:A hybrid Geometry-and Image-based Approach[J].SIGGRAPH,1996.
[124]张永军.基于序列图像的工业钣金件三维重建与视觉检测[D].武汉:武汉大学博士论文,2002.
[125]雷成,吴福朝,胡占义.Kruppa方程与摄像机自标定[J].自动化学报,2001,27(5):621-630
[126]Hartley R,Gupta R.Stereo from Uncalibrated Cameras[J].Computer Vision and Pattern Recognition,1992:761-764
[127]Hartley R I.Computer Society Conference on Projective Reconstruction from Line Correspondences[J].Computer Vision and Pattern Recognition,1994:903-907
[128]Hartley R I.A Linear Method for Reconstruction from Lines and Points[A].In.Fifth International Conference on Computer Vision[C].1995.882-887
[129]Quan L,Kanade T.Affine Structure from Line Correspondences with Uncalibrated Affine Cameras[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1997:834-845
[130]Quan L,Kanade T.A factorization Method for Affine Structure from Line Correspondences[A].In.IEEE Computer Society Conference on Computer Vision and Pattern Recognition[C].1996.803-808
[131]Chen S,Tsai W.A Systematic Approach to Analytic Determination of Camera Parameters by Line Features[J].pattern Recongnition,1990,23(8):859-877
[132]Frank A,van den Heuvel.Estimation of Interior Oriertation Parameters from Constraints on Line Measurements in A Single Image[J].International Archives of photogrammetry and Remote sensing,1999,32(t5):81-88
[133]Chen T,Shibasaki R S.Determination of Camera's Orientation Parameters Based on Line Features[J].International Archives of Photogrammetry and Remote sensing,Hakodate 1998.
[134]Cipolla R,Robertson D,Edmend boyer.Photobuilder-3D Models of Architectural Scenes from Uncalibrated Images[A].In.Proc.IEEE International Conference on Multimedia Computing and Systems[C].Firenze,1999.1:25-31
[135]Mass H G.Image Sequence Based Automatic Multi-Camera System Calibration Technique[EB/OL].http://www.geo.tudelft.nl/frs/papers/1998/Masslmage.pdf.
[136]Heyden A,Huynh D Q.Auto-calibration via the Absolute Quadric and Scene Constraints [A].In.16th International Conference on Pattern Recognition[C].11-15 Aug.2002.
[137]Ha J E,Kweon I S.3D Structure Recovery And Calibration under Varying Intrinsic Parameters using Known Angles[J].Pattern Recognition,2001,34:351-359
[138]Pollefeys M,Van Gool L,Oosterlinck A.The Modulus Constraint:A New Constraint for Self-Calibration[A].In.Proc.13th International Conference on Pattern Recognition[C].IEEE Computer Soc.Press,1996.349-353
[139]Ma S D.A Self-CalibratiOn Technique for Active Vision System.IEEE Transactions on Robot Automation,1996,12(1):114-120
[140]孟云鹤.近地轨道航天器编队飞行控制与应用研究[D].长沙:国防科学技术大学,2006.
[141]Homer H Chen.Pose Determination from Line-To-Plane Correspondences:Existence Condition and Closed-Form Solutions[J].IEEE Transactions on Patten Analysis and Machine Intelligence,1991,13(6):530-541
[142]Hyonam Joo Robert,Haralick M,Chung-Nanlee.Pose Estimation from Corresponding Point Data[J].IEEE Transactions on Systems,Man.and Cybernetics,1989,19(6):1426-1446
[143]David P,Dementhon D,Duraiswami R.,et al.Simultaneous Pose and Correspondence Determination Using Line Feature[A].In.Proceedings of IEEE Computer Vision and Pattern Recognition[C].2003.424-431
[144]Dornaika F,Garcia C.Pose Estimation Using Point and Line Correspondences[J].Journal of Real-Time Imaging,1999,5(3):217-232
[145]Cropp A,Palmer P L.Estimation the Pose of a Known Target Satellite[J].IEE Electronic letters,2000,36(5):1331-1332
[146]Cropp A,Palmer P L,Underwood C I.Pose Estimation of the Target Satellite for Proximity Operations[A].In.14th Annual/USU Conference on Small Statellite[C].Utah,logan,USA,2000.
[147]高满屯.利用直线对应计算纯旋转运动参数的一种线性方法[J].西北工业大学学报,1992,14(3):24-27
[148]Chang C,Tsai W.Reliable Determination of Object Pose from Line Features by Hypothesis Testing[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1999,21(12):1235-1241
[149]Holt J R,Netravali A N.Uniqueness of Solutions to Structure and Motion from Combinations of Point and Line Correspondences[J].Journal of Visual Communication and Image Representation,1996,7(2):126-136
[150]Phong T Q,Horaud R,Yassine A.Object Pose from 2-D to 3-D Point and Line Correspondences[J].International Journal of Computer Vision,1999,15(3):225-243
[151]Navab N,Faugeras O D.Monocular Pose Determination from Lines:Critical Sets and Maximum Number of Solutions[A].In.IEEE Conf.Computer Vision and Pattern Recognition[C].1993.254-260
[152]Christy S,Hora R.Iterative Pose Computation from Line Correspondences[J].Computer Vision and Image Understanding,1999,73(1):137-144
[153]van den Heuvel,Frank A.Exterior Orientation using Coplanar Parallel Lines[A].In.Proceedings of the 10th Scandinavian Conference on Image Analysis[C].Lappeenranta,1997.71-78
[154]Dhome M,Richetin M.Determination of the Attitude of 3D Objects from a Single Perspective View[J].IEEE Transactions on Patten Analysis and Machine Intelligence,1989,11(12):261-265
[155]Hanek R,Navab N,Appel M.Yet Another Method for Pose Estimation:A Probabilistic Aprroach using Points,Lines and Cylinders[J].1999.
[156]Chesi G,Hashimoto K.Camera Pose Estimation from Less Than Eight Points in Visual Servoing[A].In.Proceedings of the 2004 IEEE International Conference on Robotics and Automation[C].2004.733-738
[157]Huang T S,Liu Y,Faugeras O D.Determination of Camera Location from 2-D To 3-D Line And Point Correspondences[J].IEEE Transactions on Patten Analysis and Machine Intelligence,1990,12(1):28-37
[158]Horaud R,Phong T Q,Yassine A.Object Pose From 2-D To 3-D Point and Line Correspondences[J].International Journal of Computer Vision,1999,15(3):225-243
[159]Wang F Y.A Simple and Analytical Procedure for Calibrating Extrinsic Camera Parameters[J].IEEE Tranctions on robotics and automation,2004,20(1):121-124
[160]Lu Chien-Ping,Gregory D.Fast and Globally Convergent Pose Estimation From Video Images[J].IEEE Transactions on Patten Analysis and Machine Intelligence,2000,22(6):610-622
[161]尚洋.基于视觉的空间目标位置姿态测量方法研究[D].长沙:国防科学技术大学,2006.
[162]何斌,马天予,王运坚等.Visual C++数字图像处理[M].北京:人民邮电出版社,2001.
[163]王润生.图像理解[M].国防科技大学出版社,1995年10月第一版.
[164]章国宝,叶桦,陈维南.基于正交小波变换的多尺度边缘检测[J].中国图像图形学报,1998,3(8):651-653
[165]Keller J M,Chen S R,Crownover M.Texture Description and Segmentation through Fractal Geometry[J].CVGIP,1989,45:150-166
[166]Manjunath B S,Ma W Y.Texture Features for Browsing and Retrieval of Image Data[J].IEEE PAMI,1996,18(8):837-842
[167]Song J,Delp E J.The Analysis of Morphological Filters with Multiple Structuring Elements[J].Computer Vision Graphics and Image Processing,1990,50:308-328
[168]Illingworth J,Kittler J.A survey of the Hough transform.Computer Vision[J].Graphics and Image Processing,1988,44(1):87-116
[169]Nevatia R,Babu K.Linear Feature Extraction and Description[J].Computer Graphics and Image Processing,1980,13(3):257-269
[170]Boldt M,Weiss R,Riseman E.Token-Based extraction of straight lines[J].IEEE Transactions on System,Man Cyber-netics,1989,19(7):1581-1594
[171]Burns J,Hanson A,Riseman E.Extracting Straight Lines[J].IEEE Transactions on Pattern Analysis and Machine Intelligent,1986,8(4):425-455
[172]Kass M,Witkin A,Terzopoulos D.Snakes:Active Contour Models[J].International Journal of Computer Vision,1987:321-331
[173]唐亮.直线Snakes及其在建筑物提取中的应用[J].西安电子科技大学学报(自然科学版)Feb.2005,32(1):60-65
[174]Baker S.Design and Evaluation of Feature Detectors[D].U S A:Columbia University,1998
[175]张小虎,李由等.一种基于梯度方向强度直方图的直线轮廓提取新方法[J].光学技术,2006,32(6):
[176]Castleman K R.数字图像处理.北京:清华大学出版社,1998
[177]Brown G L.A Survey of Image Registration Techniques[J].ACM Computing Survey,1992,24(4):325-376
[178]Barbara Zitova,Jan Flusser.Image Registration Methods:A Survey[J].Image and Vision Computing,2003,24(11):977-1000
[179]Moravec H P.Toward Automatic Visual Obastacle Avoidance[A].In.Proc.5th Int.Joint Conf.Artificial Intelligence[C].Cabbridge,MA,1977.584-596
[180]Harris C G,Stephens M J.A Combined Corner and Edge Detector[A].In.Proceedings Fourth Alvey Vision Conference[C].Manchester,1988 147-151
[181]Scott G,Longuet-Higgins H.An Algorithm for Associating the Features of Two Patterns [A].In.Proceeding of Royal Society[C].London,1991.21-26
[182]Pilu M.A Direct Method for Stereo Correspondence Based on SVD[A].In.Proceeding of CVPR'97[C].1997.261-266
[183]Schmid C,Zisserman A.Automatic Line Matching Across Views[A].In:Procceding of the Conference on Computer Vision and Pattern Recognition[C].Puerlo Rico,UAS,1997,666-671
[184]Noronha S,Nevatia R.Detection and Modeling of Buildings from Multiple Aerial lmges[A].In.IEEE International Conference on Computer Vision and Pattern Recognition [C].San Juan,Puerto 1997.588-594
[185]Cheng Y Q,Riseman E M,Wang X Q,et al.Three-Dimensional Reconstruction of Points And Lines With Unknown Correspondence Across images[J].International Journal of Computer Vision(IJCV),Nov.2001,Vol.45(2):129-156
[186]席学强,工润生.基十直线特征的图像一模型匹配算法[J].国防科技大学学报,2000,22(6):70-74
[187]彭梦,蔡自兴.基十直线段匹配的移动机器人的障碍物榆测[J].图像与多媒体技术,2007.5:66-68
[188]崔巍,强文义,陈兴林.基于直线间结构信息的立体视觉图像动态匹配方法….控制与决策,2003,18(5):633-635
[189]黄良明,彭立中等.利用几何代数进行线段模型匹配和运动估计[J].中国图象图形学报2001,6(3):270-274
[190]季健,昂海松,王旭刚,周新春.序列图像中直线边缘快速提取和匹配[J].南京航空航天大学学报.2005,37(2):227-231
[191]Behzad K P.Line Matching:Solutions and Unsolved Problems[A].In.Proceedings of International Conference on Image Processing[C].Naval Res.Lab,Washington,DC,USA,2001.905-908
[192]Zhang Z.A Robust Technique for Matching Two Uncalibrated Images Through the Recovery of the Unknown Epipolar Geometry[R].May,1994.
[193]周军 徐奕,周源华.基于稀疏-密集匹配算法的视差度量[J].红外与激光工程,2003,32(6):630-634
[194]杨杰,郑元杰.基于单摄像头双日成像系统在计算机视觉中的应用研究[J].红外与激光工程,2004,33(4):392-396
[195]张祖勋,张剑清.数字摄影测量学[M].武汉:武汉测绘科技大学出版社,1996
[196]徐宝昌等.一种改进的最小二乘景像匹配算法[J].北京航空航天大学学报,2005,31(18):848-852
[197]孙即祥 编著.数字图象处理[M].河北:河北教育出版社,1993
[198]齐治昌.数值分析及其应用[M].长沙:国防科学技术大学出版社,2002
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |