基于光电图像目标空间位置及轨迹确定方法的研究
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摘要
本文主要研究的是基于单站和多站目标空间位置及轨迹的确定方法,它是计算机视觉技术的一种应用。
基于单站目标空间位置的确定方法是运用两个摄像机对同一目标从不同位置成像,实现对三维空间目标的定位。首先,对拍摄目标进行处理,得到其二维坐标。进而由双目体视成像原理,从两幅二维图像得到目标的三维空间坐标。
为了提高目标的定位精度,引入基于多站目标空间位置及轨迹的确定方法。本文采用加权最小二乘法、极小化误差法、基于概率的多站空间定位方法,就目标位置、测向误差和站址位置测量误差等因素对定位精度的影响进行了比较和仿真,最终给出了目标的运动轨迹图和定位精度分布图。
In this paper, the methods which determine the 3-D coordinates and trajectories of objects are studied based on single station location and multi-ground stations. It is an application of computer vision.
The methods are introduced based on single station. In the methods, binocular stereo vision is applied to acquire the 3-D coordinates of the objects we are interested in. First, the images are processed with image processing algorithms and the 2-D coordinates of the objects are obtained. Then, according to the principle of binocular stereo vision, the 3-D coordinates of the objects can be reconstructed from the two 2-D images.
In order to improve the location accuracy, the methods based on multi-ground stations are introduced. In this paper, several methods such as those based on weighted least square, minimum error and probability are studied. And the effects of the location of objects, the direction measurement error and the receivers’position measurement errors on the location accuracy are simulated and compared. At last, the track and the location accuracy of objects can be obtained.
基于单站目标空间位置的确定方法是运用两个摄像机对同一目标从不同位置成像,实现对三维空间目标的定位。首先,对拍摄目标进行处理,得到其二维坐标。进而由双目体视成像原理,从两幅二维图像得到目标的三维空间坐标。
为了提高目标的定位精度,引入基于多站目标空间位置及轨迹的确定方法。本文采用加权最小二乘法、极小化误差法、基于概率的多站空间定位方法,就目标位置、测向误差和站址位置测量误差等因素对定位精度的影响进行了比较和仿真,最终给出了目标的运动轨迹图和定位精度分布图。
In this paper, the methods which determine the 3-D coordinates and trajectories of objects are studied based on single station location and multi-ground stations. It is an application of computer vision.
The methods are introduced based on single station. In the methods, binocular stereo vision is applied to acquire the 3-D coordinates of the objects we are interested in. First, the images are processed with image processing algorithms and the 2-D coordinates of the objects are obtained. Then, according to the principle of binocular stereo vision, the 3-D coordinates of the objects can be reconstructed from the two 2-D images.
In order to improve the location accuracy, the methods based on multi-ground stations are introduced. In this paper, several methods such as those based on weighted least square, minimum error and probability are studied. And the effects of the location of objects, the direction measurement error and the receivers’position measurement errors on the location accuracy are simulated and compared. At last, the track and the location accuracy of objects can be obtained.
引文
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[2] O.Faugeras. What can be seen in three dimensions with an uncalibrated rig. Proc. of 2nd ECCV. Santa Marghertia Ligure , Italy. May 1992:563～578
[3] Hartley R. Invariants of points seen in multiple images. Tech.Rep, G.E-CRD, Schenectady .1992:26～30
[4] Hartley R, Zisserman A. Multiple View Geometry in Computer Vision. First Published, Cambridge University Press, UK. 2000
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[6] Loung Q-T. On the direct determination of epipoles: a case study in algebraic methods for geometric problems. In proceedings of CVPR 94.1994:243～247
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[12] 王天珍. 计算机视觉研究进展. 武汉汽车工业大学学报. 1998, 20(1):23～26
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[14] Rafael C. Gonzalez, Richard E. Woods. Digital Image Processing.北京:电子工业出版社, 2003:93～98.
[15] 韩思奇, 王蕾. 图像分割的阈值法综述. 系统工程与电子技术. 2002, 24(6):91～95
[16] 刘立波. 图像分割方法探讨. 宁夏农学院学报. 2001, 22(4):51～56
[17] Cinque L, Levialdi S, Rosenfeid A. Fast pyramidal algorithms for image thresholding. Pattern Recognition, 1995, 28(6):901～906
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[19] 佟国旭, 刘敬海, 林幼娜等. 亚成像系统中的实时图像分割与识别. 红外与激光工程. 1998, 27(4):26～29
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[21] 陈果, 左洪福. 图像分割的二维最大熵遗传算法. 计算机辅助设计与图形学学报. 2002, 14(6):530～534
[22] Kapur JN, Wong AKC.A new method for gray-level picture thresholding using the entropy of the histogram. Computer Vision, Graphics and Image Processing,1985,29(2):273～285
[23] 崔屹. 数学形态学方法及应用. 北京: 科学出版社, 2000:98～113
[24] L. G. Roberts .Machine Perception of Three-Dimensional Solids. In J. T. Tippett et al(editors), Optical and Electro-optical Information Processing. MIT Press.1995:159～197
[25] Jimenez A R, Ceres R, Pons J L.A Survey of Computer Vision Methods for Locating Fruit on Trees. Transaction of the ASAE. 2000, 43(6):1911～1920
[26] 张艳珍. 微机视觉系统相关理论及技术研究. 大连理工大学机械工程学院博士学位论文. 2001, 4
[27] 杨枝灵, 王开等. Visual C++ 数字图像获取处理及实践应用. 北京:人民邮电出版社,2003:574～576
[28] 邱茂林, 马颂德, 李毅. 计算机视觉中定标综述. 自动化报. 2000,26(1):43～55
[29] 隋婧, 金伟其. 双目立体视觉技术的实现及其进展. 电子技术应用. 2004, 10:4～6
[30] Vallerand Steve, Kanbara Masayuki, Yokoya Naokazu. Binocular Vision-Based Augmented Reality System With An Increased Registration Depth Using Dynamic Correction Of Feature Positions. Proceedings Of the 2003 IEEE, Virtual Reality. March 2003,Vol.2:22～26
[31] 谭跃钢, 吴正平. 一种新的基于双目视模型的三维重建方法. 仪器仪表学报. 2001, 22(3): 219～220
[32] 张毓晋. 图像理解与计算机视觉. 北京:清华大学出版社, 2000, 8:25～46,81～87
[33] 李明智. 红外告警系统评价技术研究. 红外技术, 2000, 22(2):27～31
[34] 桑炜森等. 综合电子战新技术新方法. 北京:国防工业出版社, 1996:155～159
[35] 张正明等. 平面时差定位精度分析. 西安电子科技大学学报, 2000,27(1):13～16
[36] 茅玉龙等. 无源雷达协同定位精度仿真研究. 雷达与对抗. 2000, (4):22～26
[37] Y.Oshman , P.Davidson .Optimization of Observer Trajectories for Bearing-Only Target Localization. IEEE Trans on AES, 1999, 35(3):892～902
[38] 胡来招. 无源定位 (Passive Location). 北京:国防工业出版社, 2004, 1:41～47
[39] 韦毅, 杨万海, 李艳红. 红外三维定位精度分析. 红外月刊. 2002, 2:11～12
[40] 钟镇权. 对矩阵的迹的性质研究.柳州师专学报. 1997,12(3):54～60
[41] 张健, 张建奇, 邵晓鹏. 基于Vega Prime 的红外场景生成技术在成像制导仿真系统中的应用. 红外技术. 2005, 27(2):124～128
[2] O.Faugeras. What can be seen in three dimensions with an uncalibrated rig. Proc. of 2nd ECCV. Santa Marghertia Ligure , Italy. May 1992:563～578
[3] Hartley R. Invariants of points seen in multiple images. Tech.Rep, G.E-CRD, Schenectady .1992:26～30
[4] Hartley R, Zisserman A. Multiple View Geometry in Computer Vision. First Published, Cambridge University Press, UK. 2000
[5] Hartley R. In defense of the 8-point algorithm. In Proceedings of the 5-th ICCV. Cambridge, USA:IEEE Computer Society Pree,1995:1064～1070
[6] Loung Q-T. On the direct determination of epipoles: a case study in algebraic methods for geometric problems. In proceedings of CVPR 94.1994:243～247
[7] Heyden Anders, Astrom Kalle. Euclidean reconstruction from constant intrinsic parameters .In Proc. International Conference on Pattern Recognition .1996, Vienna, Austria, 339～343
[8] Hartley Richard I. Euclidean reconstruction from uncalibrated views. Applications of Invariance in Computer Vision.1994, Lecture notes in Computer Science, 825:237～256
[9] P.Narayannan, P.Rander, T.Kanade. Constructing virtual worlds using dense stereo. Pro. International Conference on Computer Vision.1998
[10] S.Vedula, P.Rander, H.saito. Modeling, Combing, and Rendering Dynamic Real-World Events from Image Sequences. Proc.Virtual Systems and Multimedia, 1998:323～344
[11] 刘虹. 计算机视觉系统的发展和应用综述. 云南广播电视大学学报. 1999, 4:59～60
[12] 王天珍. 计算机视觉研究进展. 武汉汽车工业大学学报. 1998, 20(1):23～26
[13] 张毓晋. 图像处理和分析. 北京: 清华大学出版社, 1999:84～85
[14] Rafael C. Gonzalez, Richard E. Woods. Digital Image Processing.北京:电子工业出版社, 2003:93～98.
[15] 韩思奇, 王蕾. 图像分割的阈值法综述. 系统工程与电子技术. 2002, 24(6):91～95
[16] 刘立波. 图像分割方法探讨. 宁夏农学院学报. 2001, 22(4):51～56
[17] Cinque L, Levialdi S, Rosenfeid A. Fast pyramidal algorithms for image thresholding. Pattern Recognition, 1995, 28(6):901～906
[18] Ramac L C, Varshney P K. Image thresholding based on Ali-Silvery distance measures. Pattern Recognition, 1997, 30(7):1161～1174
[19] 佟国旭, 刘敬海, 林幼娜等. 亚成像系统中的实时图像分割与识别. 红外与激光工程. 1998, 27(4):26～29
[20] Hojjatoleslami S A, Kittler J. Region growing: A new approach. IEEE Transaction on Image Processing.1998,7(7):1079～1084
[21] 陈果, 左洪福. 图像分割的二维最大熵遗传算法. 计算机辅助设计与图形学学报. 2002, 14(6):530～534
[22] Kapur JN, Wong AKC.A new method for gray-level picture thresholding using the entropy of the histogram. Computer Vision, Graphics and Image Processing,1985,29(2):273～285
[23] 崔屹. 数学形态学方法及应用. 北京: 科学出版社, 2000:98～113
[24] L. G. Roberts .Machine Perception of Three-Dimensional Solids. In J. T. Tippett et al(editors), Optical and Electro-optical Information Processing. MIT Press.1995:159～197
[25] Jimenez A R, Ceres R, Pons J L.A Survey of Computer Vision Methods for Locating Fruit on Trees. Transaction of the ASAE. 2000, 43(6):1911～1920
[26] 张艳珍. 微机视觉系统相关理论及技术研究. 大连理工大学机械工程学院博士学位论文. 2001, 4
[27] 杨枝灵, 王开等. Visual C++ 数字图像获取处理及实践应用. 北京:人民邮电出版社,2003:574～576
[28] 邱茂林, 马颂德, 李毅. 计算机视觉中定标综述. 自动化报. 2000,26(1):43～55
[29] 隋婧, 金伟其. 双目立体视觉技术的实现及其进展. 电子技术应用. 2004, 10:4～6
[30] Vallerand Steve, Kanbara Masayuki, Yokoya Naokazu. Binocular Vision-Based Augmented Reality System With An Increased Registration Depth Using Dynamic Correction Of Feature Positions. Proceedings Of the 2003 IEEE, Virtual Reality. March 2003,Vol.2:22～26
[31] 谭跃钢, 吴正平. 一种新的基于双目视模型的三维重建方法. 仪器仪表学报. 2001, 22(3): 219～220
[32] 张毓晋. 图像理解与计算机视觉. 北京:清华大学出版社, 2000, 8:25～46,81～87
[33] 李明智. 红外告警系统评价技术研究. 红外技术, 2000, 22(2):27～31
[34] 桑炜森等. 综合电子战新技术新方法. 北京:国防工业出版社, 1996:155～159
[35] 张正明等. 平面时差定位精度分析. 西安电子科技大学学报, 2000,27(1):13～16
[36] 茅玉龙等. 无源雷达协同定位精度仿真研究. 雷达与对抗. 2000, (4):22～26
[37] Y.Oshman , P.Davidson .Optimization of Observer Trajectories for Bearing-Only Target Localization. IEEE Trans on AES, 1999, 35(3):892～902
[38] 胡来招. 无源定位 (Passive Location). 北京:国防工业出版社, 2004, 1:41～47
[39] 韦毅, 杨万海, 李艳红. 红外三维定位精度分析. 红外月刊. 2002, 2:11～12
[40] 钟镇权. 对矩阵的迹的性质研究.柳州师专学报. 1997,12(3):54～60
[41] 张健, 张建奇, 邵晓鹏. 基于Vega Prime 的红外场景生成技术在成像制导仿真系统中的应用. 红外技术. 2005, 27(2):124～128