Weighted Similarity-Invariant Linear Algorithm for Camera Calibration With Rotating 1-D Objects
Weighted Similarity-Invariant Linear Algorithm for Camera Calibration With Rotating 1-D Objects
复制标题
DOI：
10.1109/tip.2012.2195013
复制
发表时间：
2012-08
影响因子：
10.6
通讯作者：
Kunfeng Shi;Qiulei Dong;Fuchao Wu
中科院分区：
文献类型：
--
作者：
Kunfeng Shi;Qiulei Dong;Fuchao Wu
提出了一种用于旋转一维物体摄像机标定的加权相似不变线性算法。首先，我们提出了一种计算一维物体上自由端点相对深度的新方法，并与以往文献中使用的方法相比，证明了该方法对噪声的稳健性。引入的估计器对图像的相似性变换具有不变性，从而得到了一种相似性不变的线性校正算法，其精度略高于著名的归一化线性校正算法。然后，利用不同图像估计的相对深度的标准差的倒数作为相似不变线性校正算法约束方程上的权值，提出了一种具有更高精度的加权相似不变线性校正算法。在合成数据和真实图像数据上的实验结果表明了该算法的有效性。
In this paper, a weighted similarity-invariant linear algorithm for camera calibration with rotating 1-D objects is proposed. First, we propose a new estimation method for computing the relative depth of the free endpoint on the 1-D object and prove its robustness against noise compared with those used in previous literature. The introduced estimator is invariant to image similarity transforms, resulting in a similarity-invariant linear calibration algorithm which is slightly more accurate than the well-known normalized linear algorithm. Then, we use the reciprocals of the standard deviations of the estimated relative depths from different images as the weights on the constraint equations of the similarity-invariant linear calibration algorithm, and propose a weighted similarity-invariant linear calibration algorithm with higher accuracy. Experimental results on synthetic data as well as on real image data show the effectiveness of our proposed algorithm.