首页> 外文期刊>Journal of the Optical Society of America, A. Optics, image science, and vision >Recovering Euclidean structure from principal-axes paralleled conics: applications to camera calibration
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Recovering Euclidean structure from principal-axes paralleled conics: applications to camera calibration

机译:从主轴平行圆锥体中恢复欧几里得结构:在相机校准中的应用

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We focus on recovering the 2D Euclidean structure further for camera calibration from the projections of N parallel similar conics in this paper. This work demonstrates that the conic dual to the absolute points (CDAP) is the general form of the conic dual to the circular points, so it encodes the 2D Euclidean structure. However, the geometric size of the conic should be known if we utilize the CDAP. Under some special conditions (concentric conics), we proposed the rank-1 and rank-2 constraints. Our work relaxes the problem conditions and gives a more general framework than before. Experiments with simulated and real data are carried out to show the validity of the proposed algorithm.
机译:我们专注于从N个平行相似圆锥的投影中进一步恢复2D欧几里得结构以进行摄像机校准。这项工作证明圆锥对偶绝对点(CDAP)是圆锥对偶圆锥点的一般形式,因此它对2D欧几里得结构进行编码。但是,如果我们使用CDAP,则应该知道圆锥的几何尺寸。在某些特殊条件下(同心圆锥形),我们提出了等级1和等级2约束。我们的工作放宽了问题条件,并提供了一个比以前更通用的框架。通过仿真和真实数据实验证明了该算法的有效性。

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