基于单幅图像的摄像机畸变参数估计方法

摄像机畸变参数估计是摄像机标定的重要步骤.针对已有的摄像机畸变估计方法大多数为首先对无畸变下的摄像机参数进行标定,再进一步估计畸变参数,导致畸变参数估计过程复杂的问题.采用单应矩阵直接估计畸变参数,提出一种基于单幅图像的摄像机畸变参数估计算法.首先利用图像主点附近的图像特征点估计空间平面到图像平面的单应矩阵,然后利用该单应矩阵估计图像畸变误差和畸变参数,最后采用非线性优化算法对单应矩阵与畸变参数进行整体优化.模拟数据与实际图像的实验结果验证了本文算法的有效性;由于该算法仅需要1幅图像即可估计畸变参数,因此有效地提高了摄像机标定方法的灵活性.     

基于粒子群算法的双目立体视觉系统标定

摄像机标定是立体视觉系统研究的重要组成部分,针对双目立体视觉系统中摄像机标定这一多参数、复杂函数的优化问题,建立带有一阶径向畸变的摄像机模型,利用粒子群算法对模型中的参数进行优化处理,并同改进遗传算法的优化结果进行比较分析。实验结果表明该方法具有较高的精度,可满足工业机器视觉的要求。     

基于畸变模型优化的网络摄像机标定*

针对低分辨率网络数码摄像机用于定量量测的标定问题, 提出一种非线性畸变的构建与优化方法。该方法结合多项式模型与传统的畸变模型;利用回归分析原理对模型进行自动精化,以优选显著的畸变参数,从而建立了一种自适应的摄像机畸变模型。运用构建的畸变模型,利用自检校光束法平差方法对同种型号的三个低成本网络摄像机实施标定和畸变纠正。结果表明,该模型有效补偿了通用畸变模型残存的畸变系统差,同时有效的避免了过度参数化的问题,有助于提高摄像机的标定精度和稳健性,使得摄像机的标定精度达到子像素。     

双目视觉测量系统的标定及3维测量

针对结构光形位公差视觉测量系统,提出了一阶径向畸变的摄像机成像模型和标定方法,并对传统的直接线性变换(DLT变换)标定算法做了些改进。即先针对没有畸变的线性模型,利用传统的标定算法,解线性超定方程组求解摄像机的全部参数,再针对引入一阶径向畸变的成像模型,以线性模型的参数为初值,通过非线性迭代优化摄像机的图像中心,等效焦距,倾斜因子,畸变系数等内部参数。实验结果表明,该方法无需预标定,精度适中,是相对简单实用的标定方法。     

一种基于非量测畸变校正的摄像机标定方法

设计一种基于非量测畸变校正的摄像机标定方法.该方法利用单参数除式模型校正镜头畸变,根据直线透视投影保留同素性,通过拉凡格氏法（LM）优化标定出畸变模型系数和摄像机主点坐标,然后校正成像点,使其满足针孔模型映射关系.根据内参数的两个基本方程,线性求解剩余参数.实验表明,该方法在非量测标定过程具有较好的鲁棒性,且对比张正友标定方法,可在单幅标靶图像下进行标定,避免了模型内外参数耦合在一起,提高了标定效率.     

基于分割区间LS-SVM的摄像机标定

利用最小二乘支持向量机（LS-SVM）可以不考虑摄像机具体的内部参数和外部参数实现摄像机的标定。由于镜头的畸变主要由径向畸变引起,根据摄像机畸变特点对畸变区域进行划分,提出一种基于分割区间LS-SVM的摄像机标定法,对不同的畸变区域进行单独处理。该方法与BP神经网络和基本LS-SVM预测结果对比表明,分割区间LS-SVM摄像机标定法误差小、速度快、标定精度高。     

图像几何畸变校正方法

在机器视觉检测中大视场短焦距摄像机镜头一般都存在一定程度的光学畸变,在高精度测量中必须对摄像机镜头畸变进行校正。提出利用光学成像规律和镜头畸变校正模型相结合的畸变校正方法求出初始畸变系数,然后通过优化目标函数求出最优畸变系数,最后采用三次B样条插值对畸变图像进行灰度重建。实验结果表明该方法在不依赖摄像机内部参数的前提下,校正后径向均方根误差为0.45个像素,灰度重建后径向均方根误差为0.36个像素。     

基于单应矩阵的摄像机标定方法及应用

提出了一种基于单应矩阵的摄像机标定方法,并应用标定结果成功完成了移动机器人的视觉伺服任务。该方法首先根据图像平面和标定板平面之间特征点的对应关系,对单应矩阵进行了估计,进而利用旋转矩阵的单位正交性得到了其对摄像机内参数的约束条件。然后把摄像机内参数矩阵分解为有效焦距与主点位置两部分,并利用最小二乘法分别对其进行求解。针对镜头的径向畸变,恰当地选取了一种畸变模型,并由此得到了一种新的目标函数来对摄像机的所有参数进行非线性优化,从而使获得的畸变系数更适合于从二维图像信号中提取三维位姿信息。最后将标定结果成功应用于移动机器人视觉伺服系统之中,实验结果验证了该标定算法具有简单易用、精度较高等优良性能。     

基于数据拟合的摄像机标定和图像的数值校正

采用的摄像机成像模型考虑了透镜的径向畸变和切向畸变。标定过程包括对摄像机的内外部参数的直接线性变换估算和基于内部映射牛顿法的子空间置信域法的非线性优化算法优化。同时提出了对畸变图像校正的数值计算方法。     

3D靶标的摄像机三步标定算法与实现

摄像机标定是计算机视觉中的一个必不可少的重要环节,其标定精度影响着三维重建结果的精度。利用摄像机对一个三维正交的棋盘格拍摄一幅有效靶标图像,提取角点,第一步通过透视变换矩阵算法线性求解各内外参数;第二步引入径向和切向畸变,将第一步求得的内外参数作为初始值,求得畸变系数的解;第三步对内参数进行线性优化,得到更为精确的内参数值;最后求解反投影后图像坐标的绝对误差。实验结果表明,该方法具有较高的精度,且简单有效。     

Simplified Active Calibration

We present a new mathematical formulation to estimate the intrinsic parameters of a camera in active or robotic platforms. We show that the focal lengths can be estimated using only one point correspondence that relates images taken before and after a degenerate rotation of the camera. The estimated focal lengths are then treated as known parameters to obtain a linear set of equations to calculate the principal point. Assuming that the principal point is close to the image center, the accuracy of the linear equations are increased by integrating the image center into the formulation. We extensively evaluate the formulations on a simulated camera, 3D scenes and real-world images. Our error analysis over simulated and real images indicates that the proposed Simplified Active Calibration method estimates the parameters of a camera with low error rates that can be used as an initial guess for further non-linear refinement procedures. Simplified Active Calibration can be employed in real-time environments for automatic calibrations given the proposed closed-form solutions.     

摄像机位姿的高精度快速求解

目的 针对对应点个数大于等于6的摄像机位姿估计问题,提出一种既适用于已标定也适用于未标定摄像机的时间复杂度为 的高精度快速算法。 方法 首先选取四个非共面虚拟控制点,并根据空间点和虚拟控制点的空间关系以及空间点的图像建立线性方程组,以此求解虚拟控制点的图像坐标及摄像机内参,再由POSIT算法根据虚拟控制点及其图像坐标求解旋转矩阵和平移向量。 结果 模拟数据实验和真实图像实验表明该算法时间复杂度和计算精度均优于现有的已标定摄像机位姿的高精度快速求解算法EPnP。 结论 该算法能够同时估计摄像机内外参数,而且比现有算法具有更好的速度和精度。     

Self-calibration with varying focal length from two images obtained by a stereo head

The self-calibration approach based on the absolute conic or its dual, the absolute dual quadric, has the merit of allowing the intrinsic camera parameters to vary in image sequence. In this paper, we show that certain linear equations resulting from the infinity homography can be added to a system of originally undetermined linear equations to find the absolute dual quadric for a stereo head. The special stereo configurations considered here allow one camera to rotate around either one or two of the coordinate axes defined by another camera. Experiments with synthetic images show that satisfactory results can be obtained with only the proposed linear methods. For real images obtained with non-ideal stereo configurations, and possibly with measurement noises, results obtained from the proposed linear methods may serve as reasonable initial guesses for some non-linear optimization procedure.     

稳定精确的摄像机标定方法

在Tsai两步法的基础上提出了一种更加稳定精确的摄像机标定方法。由于Tsai两步法中只考虑了摄像机镜头的径向畸变因素,所以为了提高摄像机的标定精度,在其基础上考虑了镜头的切向畸变情况。在求解摄像机参数的过程中,第一步同Tsai两步法采用最小二乘法求解线性方程得到外部参数,再利用最小二乘法求解关于畸变参数K1,K2,K3,K4的线性方程组,最终求得摄像机的内外所有参数。通过实验对该方法进行了验证。     

基于平面格网的非量测CCD相机自标定

传统的相机标定方法通常需要建立复杂3维标定块或高精度3维控制场,在实际应用中受到了一定的限制。本文采用平面控制格网作为标定块,根据相机的理想模型确定内方位元素,利用2维直接线性变换和共线方程分解出相机的外方位元素初值,采用改进的Hough变换算法检测标定图像中的格网直线并利用最小二乘法拟合出最佳直线,通过求直线的交点得到标定格网点的像坐标。最后利用自检校光线束法平差进行相机的精确标定。实际图像数据实验结果表明,主点和焦距的标定精度分别达到了0.2像素和0.3像素左右。可以满足高精度近景3维量测的要求。     

基于光束法平差的双线阵系统标定技术

针对线阵相机特殊使用场景中所需要的高精度图像,对线阵相机进行高精度标定。提出一种基于光束法平差的双线阵相机标定方法。通过背景差分法获取线阵相机的特征点的像素坐标。再利用已有的直接线性变换方法和非线性优化方法求出相机的内参,外参,畸变参数后,将得到的初始参数与世界坐标作为待优化集合,利用LM法和光束法平差对该集合进行更进一步的优化,使得双线阵系统的重投影误差降到最低。实验表明,该方法与传统的线阵相机标定方法相比重投影误差降低了75.01%。     

一种新的基于Kruppa方程的摄像机自标定方法

主要针对传统的基于Kruppa方程的摄像机自标定算法的欠鲁棒性提出了一种新的二步式标定方法．在新标定方法中，首先利用传统的LM优化算法或遗传算法求解出Kruppa方程中通常需要被消去的比例因子，然后再利用线性方法完成对摄像机的标定．大量的仿真和真实图像实验表明，该方法可以大大提高基于Kruppa方程标定算法的鲁棒性及标定精度．     

A New Linear Method for Camera Self-Calibration with Planar Motion

We consider the self-calibration (affine and metric reconstruction) problem from images acquired with a camera with unchanging internal parameters undergoing planar motion. The general self-calibration methods (modulus constraint, Kruppa equations) are known to fail with this camera motion. In this paper we give two novel linear constraints on the coordinates of the plane at infinity in a projective reconstruction for any camera motion. In the planar case, we show that the two constraints are equivalent and easy to compute, giving us a linear version of the quartic modulus constraint. Using this fact, we present a new linear method to solve the self-calibration problem with planar motion of the camera from three or more images. This work was partly supported by project BFM2003-02914 from the Ministerio de Ciencia y Tecnología (Spain). Ferran Espuny received the MSc in Mathematics in 2002 from the Universitat de Barcelona, Spain. He is currently a PhD student and associate professor in the Departament d’àlgebra i Geometria at Universitat de Barcelona, Spain. His research, supervised by Dr. José Ignacio Burgos Gil, is focussed on self-calibration and critical motions for both pinhole and generic camera models.     

A Least Squares Solution for Camera Distortion Parameters

Most applications in optical metrology need a well calibrated camera. In particular, a calibrated camera includes a distortion mapping, parameters of which are determined in a final non-linear optimization over all camera parameters. In this article we present a closed form solution for the distortion parameters provided that all other camera parameters are known. We show that for radial, tangential, and thin prism distortions the determination of the parameters form a linear least squares problem. Therefore, a part of the camera calibration error function can be minimized by linear methods in closed form: We are able to decouple the calculation of the distortion parameters from the non-linear optimization. The number of parameters in the non-linear minimization are reduced. Several experimental results confirm the benefit of the approach.     

Qualitative estimation of camera motion parameters from the linear composition of optical flow

In this paper, we propose a new method for estimating camera motion parameters based on optical flow models. Camera motion parameters are generated using linear combinations of optical flow models. The proposed method first creates these optical flow models, and then linear decompositions are performed on the input optical flows calculated from adjacent images in the video sequence, which are used to estimate the coefficients of each optical flow model. These coefficients are then applied to the parameters used to create each optical flow model, and the camera motion parameters implied in the adjacent images can be estimated through a linear composition of the weighted parameters.We demonstrated that the proposed method estimates the camera motion parameters accurately and at a low computational cost as well as robust to noise residing in the video sequence being analyzed.