基于近似分解法的全向摄像机标定

Haijiang Zhu, Xuejing Wang, C. Yi
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引用次数: 0

摘要

标准透视图像可以用Micusik[12]的全向图像的部分中心区域来表示,我们能否利用全向图像中心区域的部分信息将几种摄像机标定方法从透视图像推广到全向图像?提出了一种基于多幅图像近似分解的全向摄像机标定方法。该方法首先在全向图像的中心区域利用部分比其他特征点畸变较小的图像点进行近似投影分解算法。然后,假设全向图像的中心为零偏移,主点在中心,计算出相机固有参数的初始值。最后,以目标函数最小化为原则,对畸变的内在参数进行了细化。我们用合成数据和真实鱼眼图像进行了实验验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Omnidirectional camera calibration based on an approximate factorization method
A standard perspective image can be represented with part center area of an omnidirectional image from Micusik [12], can we generalize a few camera calibration methods from the perspective image to the omnidirectional image using part information in the center area of the omnidirectional image? In this paper, a novel omnidirectional camera calibration based on the approximate factorization method from multiple images is presented. This method first performs the approximate projective factorization algorithm using part of image points, which are less distorted than other feature points, in the center area of the omnidirectional image. Then, assuming zero skew and the principal point at the center of omnidirectional image, the initial value of camera intrinsic parameters can be computed. Last, the distortion intrinsic parameters are refined on minimizing the objective function. We demonstrate this method in experiment with synthetic data and real fisheye images.
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