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基于单幅全向图像的空间水平直线定位新方法 被引量：1

Novel method of space horizontal line localization based on single catadioptric omnidirectional image
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摘要 研究了在没有先验知识的前提下,基于单幅全向图像定位空间水平直线的问题。在已有算法的基础上,分析和推导了空间水平直线在全向成像系统中的成像特点,指出基于直线在全向图中的两个像点即可重建该水平直线,并相应地提出了一种基于"主像点/非主像点"的空间水平直线定位算法。试验表明,在不同的像点提取精度下,对于不同空间水平直线,本方法均能取得较好结果。 Space horizontal line localization without prior knowledge from a single omnidirectional image was exploited. Based on the existing approaches of straight line localization, the authors demonstrated that, for symmetric non-central eatadioptric systems, the equation of a 3D horizontal line could be estimated using only two points extracted from a single image of the line by exploiting the characteristics of horizontal line image in catadioptric system, and meanwhile a horizontal line reconstruction algorithm based on main-point image and non-main-point image was proposed. The experimental results justify that, under different precision of image point extraction, compared to present approaches of line reconstruction, the proposed method is constantly more efficient.
作者 娄静涛 王炜 张茂军 陈旺
机构地区 国防科学技术大学信息系统与管理学院
出处 《计算机应用》 CSCD 北大核心 2009年第9期2593-2595,共3页 journal of Computer Applications
基金 国家自然科学基金资助项目(60705013 60872150)
关键词 单幅图像 直线定位 三维重建 折反射全向成像 single image horizontal line localization 3D reconstruction catadioptric omni-directional imaging
分类号 TP391 [自动化与计算机技术—计算机应用技术]
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参考文献10

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同被引文献8

  • 1CHAM T-J, CIPTADI A, TAN W-C et al. Estimating camera pose from a single urban ground-view omnidirectional image and a 2D building outline map[ C]//CVPR 2010: IEEE International Conference on Computer Vision and Pattern Recognition. Washington, DC: IEEE Computer Society, 2010:366 -373.
  • 2CHEN L-C, TEO T-A, HSIEH C-H, et al. Reconstruction of building models with curvilinear boundaries from laser scanner and aerial imagery [ C]// PSIVT: Pacific-Rim Symposium on Image and Video Technology, LNCS4319. Berlin: Springer-Verlag, 2006:24-33.
  • 3LEE S C, NEVATIA R. Automatic integration of facade textures into 3D building models with a projective geometry based line clustering [J]. Computer Graphics Forum, 2002,21(3) :511 -519.
  • 4WANG L, YOU S, NEUMANN U. Semiautomatic registration be- tween ground-level panoramas and an orthorectified aerial image for building modeling[ C]// IEEE 11 th International Conference on Computer Vision. Piscataway, NJ: IEEE, 2007:1-8.
  • 5FRUEH C, ZAKHOR A. 3D model generation for cities using aerial photographs and ground level laser scans[ C]// CVPR 2001 : Computer Vision and Pattern Recognition Conference. Washington, DC: IEEE Computer Society, 2001, 2:31 - 38.
  • 6ROBERTSONAND D P, CIPOLLA R. Building architectural models from many views using map constraints[ C]//ECCV 2002: European Conference on Computer Vision, LNCS 2351. Berlin: Springer- Verlag, 2002:155 - 169.
  • 7FIALA M, BASU A. Panoramic stereo reconstruction using non-SVP optics [ J]. Computer Vision and Image Understanding, 2005, 98 (3) : 363 -397.
  • 8FIALA M, BASU A. Hough transform for feature detection in panoramic images [ J]. Pattern Recognition Letters, 2002, 23( 14): 1863 - 1874.

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计算机应用
2009年 第9期

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