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基于极少信息的任意照片目标点定位算法 被引量:3

Target points positioning algorithm based on rare information in uncalibrated camera's photos
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摘要 为了达到通过任意摄像头拍摄照片就可对目标点进行定位的目的,针对如何用最少信息得到空间中目标点坐标问题,基于摄像机的成像模型、空间中固定点之间的几何约束,以及坐标系变换的基本原理,推导出一种类似于P4P的无标定照片的目标点定位方法。该方法可由观测到的两幅图像中已知世界坐标系中位置坐标的任意四点在图像中的位置,通过计算得到图像中目标点的三维坐标。此方法需要的已知信息极少,对于拍摄图像以及拍摄所用相机没有要求,并通过实验验证可行,精度相比传统标定方法没有明显损失,且较其他自标定定位方法更高,实用性较强。 In order to achieve the purpose of positioning the target point from any camera photos, for information on how to use the least get the coordinates of the target point in space, based on the the camera imaging model, geometric constraints of the fixed point in space, and the basic principles of the coordinate system transformation, this paper obtained a target point positio- ning method through uncalibrated photographs similar to P4P. This method could calculate the three-dimensional world coordi- nates of the target point by observing two images' position coordinates of four points whose world coordinate was already known. Very minimal information was needed in this method, and it could be used with any cameras of photos. Experiment has been made to verify the feasibility of the algorithm. The accuracy compared to traditional calibration methods has no signifi- cant losses, and is higher than other self-calibration positioning methods.
作者 刘吟啸 韦巍
出处 《计算机应用研究》 CSCD 北大核心 2015年第1期272-275,共4页 Application Research of Computers
基金 国家"863"计划重大项目(2011AA050204) 中央高校基本科研业务费专项资助项目(2013QNA4021) 杭州市重大科技创新产业链项目
关键词 机器视觉 无标定定位 P4P 双目视觉 几何约束 坐标系转换 machine vision uncalihrated positioning P4P binocular vision geometric constraints coordinate conversion
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参考文献11

  • 1胡占义,雷成,吴福朝.关于P4P问题的一点讨论[J].自动化学报,2001,27(6):770-776. 被引量:49
  • 2FISCHLER M A, BOLLES R C. Random sample consensus:a para- digm for model fitting with applications to image analysis and automa- ted cartography [ J ]. Communication of the ACM, 1981,24 ( 6 ) : 381-395.
  • 3HARALLICK R M. Determining camera parameters from the perspec- tive projection of 8 rectangle[J]. Patterns Recognition, 1989,22 (3) :225- 230.
  • 4HARALLICK R M. Determining camera parameters from the perspec- tive projection of a quadrilateral[ ,I]. Patterns Recognition, 1991,24 (6) :539-541.
  • 5ABIDI M A, CHANDRA T. A new efficient and direct solution for pose estimation using quadrangular targets: algorithm and evaluation [ J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 1995,17(5) :534-538.
  • 6傅丹,冯卫东,于起峰,徐一丹.一种摄像机自标定的线性方法[J].光电工程,2008,35(1):71-75. 被引量:12
  • 7翟优,曾峦,熊伟.基于不变特征描述符实现星点匹配[J].光学精密工程,2012,20(11):2531-2539. 被引量:19
  • 8霍炬,杨卫,杨明.基于消隐点几何特性的摄像机自标定方法[J].光学学报,2010,30(2):465-472. 被引量:52
  • 9RAO Xing-tang, SONG Tao, GAO Jun-kuo. A highly sensitive mixed lanthanide metal-organic framework seE-calibrated luminescent ther- mometer[ J]. American Chemical Society,2013,135 (41) : 15559- 1.5564.
  • 10GEORGE V, CARLOS H. Self-calibrated, multi-spectral photometric stereo for 3D face capture[ J]. International Journal of Computer Vision,2012,97 ( 1 ) :91-103.

二级参考文献44

  • 1刘宏建,罗毅,刘允才.可变精度的神经网络摄像机标定法[J].光学精密工程,2004,12(4):443-448. 被引量:13
  • 2刘朝山,马瑞萍,肖称贵,刘光斌.星图匹配制导中的关键技术[J].宇航学报,2006,27(1):31-35. 被引量:14
  • 3李为民,俞巧云,刘超.采用分离式差分标定靶的单摄像机标定方法[J].光学学报,2006,26(5):697-701. 被引量:26
  • 4Roger Y. Tsai. A versatile camera calibration technique for high- accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses[J]. IEEE J. Robot. Autom. , 1987, 3(4): 323-344.
  • 5O. D. Faugeras, Q. T. Luong, S. J. Maybank. Camera sell calibration: theory and experiments[C]. Proc. the 2nd European Conference on Computer Vision Berlin: Springer, 1992, 588:321-334.
  • 6S. Maybank, O. D. Faugeras. A theory of self-calibration of a moving camera[J]. Int. J. Comput. Vision, 1992, 8(2): 123-151.
  • 7R. Hartley. Self-calibration of stationary cameras[J]. Int. J. Cornput. Vision, 1997, 22(1): 5-23.
  • 8Songde Ma. A self-calibration technique for active vision systems [J]. IEEE T. Robot. Aurora. , 1996, 12(1): 114-120.
  • 9X. Q. Meng, H. Li, Z. Y. Hu. A new easy camera calibration technique based on circular ooints[C]. Proc. the British Machine Vision Conference, Bristol: ILES Central Press, 2000, 496- 501.
  • 10R. Hartley. Multiple View Geometry in Computer Vision[M]. London: Cambridge University Press, 2003.

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