基于主动视觉系统的摄像机自定标方法研究

提出了一种新的基于主动视觉系统的摄像机自定标方法,其主要特点是可以线性求解 摄像机的所有5个内参数.该方法的基本原理是利用图像中平面场景的信息,通过控制摄像机 作多组平面正交平移运动,由平面场景图像的单应性(homography)矩阵建立摄像机内参数线性 约束方程组,来求解摄像机的内参数,同时给出约束方程组解的唯一性与平移运动组之间的关系.     

未标定单幅结构场景图像的三维重构

提出了一种单视三维重构方法，该方法是利用用户提供图像点及其对应的三维点之间几何信息。由于结构场景是由大量平面构成的，存在大量的平行性、正交性约束，因此该方法主要应用于结构场景的三维重构。首先，相机定标和计算每个平面的度量信息，即先基于3组互相垂直方向的影灭点，对方形像素相机标定，再利用影灭线和圆环点像，对每个平面度量校正；然后考虑每个校正平面的尺度因子和非正交平面间的相对面向，从而将所有校正后的平面缝合起来。采用真实图像进行实验，实验结果表明，该方法简单易用。     

基于单平面模板的摄像机定标研究

提出了一种摄像机定标方法，只需要摄像机从不同方向拍摄平面模板的多幅图像，摄像机与平面模板间可以自由地移动，运动的参数无需已知。对于每个视点获得图像，提取图像上的网格角点；平面模板与图像间的网格角点对应关系，确定了单应性矩阵；对每幅图像，就可确定一个单应性矩阵，这样就能够进行摄像机定标。该算法先有一个线性解法，然后基于极大似然准则对线性结果进行非线性优化求精。该方法同时也考虑了镜头畸变的影响。实验结果表明该算法简单易用。     

基于正交平面的摄像机自定标

提出了一种基于正交平面摄像机定标的新算法。它利用场景中的正交平面,摄像机作五次以上的平移运动,根据每次运动关于平面的单应矩阵建立内参数的线性约束方程组,从而线性地确定内参数。与以往的定标方法相比,文章对摄像机的运动不苛刻,只需控制摄像机作平移运动,这在一般的实验平台上可以很容易地实现,并且线性地确定摄像机所有的五个内参数。模拟实验和真实图象实验表明,文章给出的方法在机器人视觉中具有一定的实用价值。     

基于圆曲线的摄像机自定标

本文给出了一种基于圆曲线的摄像机自定标的新方法.设计了一个含有三个两两不包含的圆(其中圆心和半径大小是未知的)的平面模板,让摄像机在三个以上的不同方位观察该模板,然后利用模板的图像来求解摄像机的内参数.文中,首先介绍了三维摄影空间中绝对二次曲线和圆环点的相关知识,接着推导出摄像机内参数的约束方程,最后又详细讨论了圆环点及其图像的求法.其中关键技术是通过求解像曲线的切线确定圆心图像的像素坐标,然后确定圆环点图像坐标来实现摄像机的定标模拟实验和真实图像实验也表明,本方法具有很好的鲁棒性.由于所用模板中的圆不需要精确定位,因此该模板易于制作.由于该技术对摄像机的运动没有限制,因此,它可以在一般的实验平台上实现.     

一种新的线性摄像机自标定方法

提出了一种新的基于主动视觉系统的线性摄像机自定标方法。所谓基于主动视觉系统,是指摄像机固定在摄像机平台上以平摄像机平台的运动可以精确控制。该方法的主要特点是可以线性求解摄像机的所有5个内参数。据作者所知。文献中现有的方法仅能线性求解摄像机的4个由参数。当摄像机为完全的射影模型时,即当有畸变因子（skew factor）存在时,文献中的线性方法均不再适用。该方法的基本思想是控制摄像机做5组平面正交运动,利用图像中的极点（epipoles）信息来线性标定摄像机。同时,针对摄像机做平移运动时基本矩阵的特殊形式,该文提出了求基本矩阵（fundamental matrix)的2点算法。与8点算法相比较,2点算法大大提高了所求极点的精度和鲁棒性。另外,该文对临近奇异状态（即5组平面正交运动中,有两组或者多组运动平面平行）作了较为详尽的分析,并提出了解决临近奇异状态的策略,从而增强了该文算法的衫性。模拟图像和真实图像实验表明该文的自标定方法具有较高的鲁棒性和准确性。     

基于直线的几何不变性标定摄像机参数

计算机视觉通常采用针孔摄像机模型,但对于存在较大畸变的鱼眼镜头或广角镜头来说,会造成图像中同时存在透视变形和像差畸变。解决此问题的传统方法一般是采用标准网格板来标定摄像机参数,但需要较多的已知信息。为了进行精确的标定,提出了一种新的标定方法,该新方法不需要任何空间3维信息,即可用单幅普通图像来标定摄像机的像差系数及内参数,并可将畸变图像校正到相似变换。为了纠正像差畸变和计算消影点,该方法采用了直线的射影不变性,即共线点的投影仍然共线,平行直线束的投影相交于一点的性质;为了纠正透视变形,还采用了直线的相似不变性,即正交直线的夹角在相似变换中仍然保持正交的性质。用该方法标定的摄像机的参数包括像差系数、焦距、主点和纵横比,同时将图像纠正到了相似变换。用实验室图像和室外图像进行了仿真实验都得到了精确、可靠的结果。     

基于平面相似图形的摄像机标定方法

提出了一种基于平面相似图形的摄像机自标定方法.通过平面相似图形的图像点之间的射影关系,得到该平面的圆环点的图像,然后根据圆环点的图像计算出摄像机内参数.实验证明本文方法是可行的,并有较好的鲁棒性.     

一种基于DLT模型和圆环点改正的摄像机定标方法

提出一种基于DLT(直接线性变换)模型和圆环点改正的摄像机定标方法.运用计算机视觉原理,通过三维DLT平面模板模型平移对摄像机进行定标,并在模板上设计一个圆环,使用圆环上的点进行定标参数改进.实验数据证明,定标方法是有效的.     

基于平面模板的摄像机标定方法

给出了一种基于平面模板的摄像机标定的新算法.所用的标定模板由一个内嵌矩形的圆组成,通过模板图像在像平面上的投影计算圆环点,建立绝对二次曲线对摄像机内参数的约束方程,只需要摄像机在3个(或3个以上)不同方位摄取平面模板中的图像,即可线性求解摄像机的内参数.该方法原理简单,对摄像机运动没有约束,不涉及图像匹配,平面模板容易制作,无须知道物理度量,实验证明本方法可行,并有较好的鲁棒性.     

基于平面镜的摄像机内参数线性标定方法

提出了一种全新的基于平面镜的摄像机标定方法。该方法无需任何标定物,利用平面镜的反射性质和灭点、灭线等理论,通过移动摄像机或平面镜作至少2次平移运动(各次平移间应存在旋转运动)即可线性地标定摄像机的内参数。模拟图像实验和真实图像实验表明所提出的方法能快速、方便地对摄像机进行标定,具有较好的稳定性和精度。     

Using vanishing points for camera calibration

In this article a new method for the calibration of a vision system which consists of two (or more) cameras is presented. The proposed method, which uses simple properties of vanishing points, is divided into two steps. In the first step, the intrinsic parameters of each camera, that is, the focal length and the location of the intersection between the optical axis and the image plane, are recovered from a single image of a cube. In the second step, the extrinsic parameters of a pair of cameras, that is, the rotation matrix and the translation vector which describe the rigid motion between the coordinate systems fixed in the two cameras are estimated from an image stereo pair of a suitable planar pattern. Firstly, by matching the corresponding vanishing points in the two images the rotation matrix can be computed, then the translation vector is estimated by means of a simple triangulation. The robustness of the method against noise is discussed, and the conditions for optimal estimation of the rotation matrix are derived. Extensive experimentation shows that the precision that can be achieved with the proposed method is sufficient to efficiently perform machine vision tasks that require camera calibration, like depth from stereo and motion from image sequence.     

Computing camera parameters using vanishing-line information from a rectangular parallelepiped

A new approach to camera calibration using vanishing line information for three-dimensional computer vision is proposed. Calibrated parameters include the orientation, the position, the focal length, and the image plane center of a camera. A rectangular parallelepiped is employed as the calibration target to generate three principal vanishing points and then three vanishing lines from the projected image of the parallelepiped. Only a monocular image is required for solving these camera parameters. It is shown that the image plane center is the orthocenter of a triangle formed by the three vanishing lines. From the slopes of the vanishing lines the camera orientation parameters can be determined. The focal length can be computed by the area of the triangle. The camera position parameters can then be calibrated by using related geometric projective relationships. The derived results show the geometric meanings of these camera parameters. The calibration formulas are analytic and simple to compute. Experimental results show the feasibility of the proposed approach for a practical application—autonomous land vehicle guidance.This work was supported by National Science Council, Republic of China under Grant NSC-77-0404-E-009-31.     

基于多视定位算法的多摄像机标定

针对多摄像机系统标定,提出一种基于多视定位算法的标定方法,标定过程只需一块可自由移动的平面模板即可,利用约束优化思想,将各摄像机坐标系转换到参考摄像机坐标系下,从而得到摄像机之间相对位置关系。标定操作过程简单,易于实现。实验结果表明,该方法是一种有效的多摄像机标定方法。     

一种基于灭影线的无人直升机位姿估计方法

该文将基于灭影线的摄像机标定原理用于无人直升机的位姿估计,设计了一个包含四组平行线的平面着陆图标,通过机载摄像机摄取的图标图像,估计出直升机相对于该图标的位置和姿态。文章首先简述了灭影线的有关知识,推导了相应的关系方程,接着介绍了使用的图像处理方法和提高计算精度的措施,最后,给出了实验结果并对实验误差做了简要分析。该方法的技术关键是如何精确测定灭影点和灭影线的像平面参数,算法最大的特点为计算结果与焦距变化的无关性。实验结果表明,该方法算法复杂性低,实时性好,精度较高,可以用于无人直升机的位姿估计。     

基于单幅透视图像确定相机参数的几何方法

提出一种求解相机内、外参数的几何方法．在不考虑相机镜头变形的前提下，首先根据空间平面上的单个矩形，从该矩形的两灭点透视图中，根据灭点间的几何关系，补出另外一个灭点的坐标；然后根据透视投影的灭点理论，计算出相机的相对位置；若已知矩形边的实际长度，可以恢复出相机在三维空间中的实际位置和矩形顶点的空间坐标；最后根据透视投影关系计算出相机的有效焦距．大量的模拟和真实实验表明，该方法简单、易于操作，具有标定精度高、鲁棒性强的优点．     

Camera calibration with one-dimensional objects

Camera calibration has been studied extensively in computer vision and photogrammetry and the proposed techniques in the literature include those using 3D apparatus (two or three planes orthogonal to each other or a plane undergoing a pure translation, etc.), 2D objects (planar patterns undergoing unknown motions), and 0D features (self-calibration using unknown scene points). Yet, this paper proposes a new calibration technique using 1D objects (points aligned on a line), thus filling the missing dimension in calibration. In particular, we show that camera calibration is not possible with free-moving 1D objects, but can be solved if one point is fixed. A closed-form solution is developed if six or more observations of such a 1D object are made. For higher accuracy, a nonlinear technique based on the maximum likelihood criterion is then used to refine the estimate. Singularities have also been studied. Besides the theoretical aspect, the proposed technique is also important in practice especially when calibrating multiple cameras mounted apart from each other, where the calibration objects are required to be visible simultaneously.     

Camera calibration using one-dimensional information and its applications in both controlled and uncontrolled environments

Camera model and its calibration are required in many applications for coordinate conversions between the two-dimensional image and the real three-dimensional world. Self-calibration method is usually chosen for camera calibration in uncontrolled environments because the scene geometry could be unknown. However when no reliable feature correspondences can be established or when the camera is static in relation to the majority of the scene, self-calibration method fails to work. On the other hand, object-based calibration methods are more reliable than self-calibration methods due to the existence of the object with known geometry. However, most object-based calibration methods are unable to work in uncontrolled environments because they require the geometric knowledge on calibration objects. Though in the past few years the simplest geometry required for a calibration object has been reduced to a 1D object with at least three points, it is still not easy to find such an object in an uncontrolled environment, not to mention the additional metric/motion requirement in the existing methods. Meanwhile, it is very easy to find a 1D object with two end points in most scenes. Thus, it would be very worthwhile to investigate an object-based method based on such a simple object so that it would still be possible to calibrate a camera when both self-calibration and existing object-based calibration fail to work. We propose a new camera calibration method which requires only an object with two end points, the simplest geometry that can be extracted from many real-life objects. Through observations of such a 1D object at different positions/orientations on a plane which is fixed in relation to the camera, both intrinsic (focal length) and extrinsic (rotation angles and translations) camera parameters can be calibrated using the proposed method. The proposed method has been tested on simulated data and real data from both controlled and uncontrolled environments, including situations where no explicit 1D calibration objects are available, e.g. from a human walking sequence. Very accurate camera calibration results have been achieved using the proposed method.     

基于灭点理论和平面控制场的相机标定方法研究

给出了一种基于灭点理论和平面控制场的相机解析自标定方法。在系统分析平面场景灭点几何的基础上,依据灭点理论并结合2维场景的共线方程严密论证和推导了平面控制场中相机外方位元素初值的实用算法。给出了一种圆形标志点的快速检测及定位方法,提出了一种基于计算可靠性矩阵QVVP的自检校光束法平差中各类观测值权值的确定方法。实验证明了该相机标定方法的正确性和有效性,为相机标定探索了一种可行的方法。