A unified approach to the linear camera calibration problem

The camera calibration process relates camera system measurements (pixels) to known reference points in a three-dimensional world coordinate system. The calibration process is viewed as consisting of two independent phases: the first is removing geometrical camera distortion so that rectangular calibration grids are straightened in the image plane, and the second is using a linear affine transformation as a map between the rectified camera coordinates and the geometrically projected coordinates on the image plane of known reference points. Phase one is camera-dependent, and in some systems may be unnecessary. Phase two is concerned with a generic model that includes 12 extrinsic variables and up to five intrinsic parameters. General methods handling additional constraints on the intrinsic variables in a manner consistent with explicit satisfaction of all six constraints on the orthogonal rotation matrix are presented. The use of coplanar and noncoplanar calibration points is described     

Implicit and explicit camera calibration: theory and experiments

By implicit camera calibration, we mean the process of calibrating a camera without explicitly computing its physical parameters. Implicit calibration can be used for both three-dimensional (3-D) measurement and generation of image coordinates. In this paper, we present a new implicit model based on the generalized projective mappings between the image plane and two calibration planes. The back-projection and projection processes are modelled separately to ease the computation of distorted image coordinates from known world points. A set of constraints of perspectivity is derived to relate the transformation parameters of the two calibration planes. Under the assumption of the radial distortion model, we present a computationally efficient method for explicitly correcting the distortion of image coordinates in frame buffer without involving the computation of camera position and orientation. By combining with any linear calibration techniques, this method makes explicit the camera physical parameters. Extensive experimental comparison of our methods with the classic photogrammetric method and Tsai's (1986) method in the aspects of 3-D measurement (both absolute and relative errors), the prediction of image coordinates, and the effect of the number of calibration points, is made using real images from 15 different depth values     

基于序列图像的摄像机自标定方法

提出了一种新的结合摄影测量和计算机视觉相关理论的摄像机自标定方法。首先通过序列图像的匹配点对,利用计算机视觉理论中的8点法求得摄像机基础矩阵F,通过矩阵F利用Kruppa方程求得矩阵C,对矩阵C进行Cholesky分解得到摄像机的内参数矩阵K,然后将求出的内参数作为初始值,利用摄影测量理论进行相对定向和绝对定向,最小二乘前方交会计算得到匹配点对的三维空间坐标,最后由匹配点对的三维空间坐标及其图像坐标,采用三维直接线性变换和光束法平差方法解算出摄像机内、外参数及畸变系数。该方法不依赖于特定的场景几何约束条件,只要序列图像之间有匹配点对,就可以进行自标定工作,具有广泛的适用性。模拟数据和真实图像的实验结果表明：该方法计算过程简单,标定精度高,是一种值得借鉴的摄像机自标定方法。     

基于图像融合技术的经络实时三维可视化研究

本研究目的是将三维可视化应用于人体经络.通过磁场跟踪仪Wintracker和多通道经络测试仪共同获得经络的三维坐标.利用OpenCV相关函数实现摄像机标定,计算出坐标变换矩阵M,将世界坐标系下的经络三维坐标变换成图像二维坐标系下的经络坐标,最后利用图像融合技术实时地将经络线绘制于现场人体体表图相应位置上,实现经络穴位的...     

采用棋盘格靶标的全自动相机标定方法

为了去除相机标定过程中的人为干预,提出了一种采用改进的棋盘格靶标的全自动相机标定方法。识别出每幅标定图像中的四个标志圆,利用四个标志圆圆心的图像坐标和物理坐标计算二维射影变换矩阵。依据该射影变换矩阵计算出棋盘格角点的初始图像位置,接着提取亚像素精度的角点位置。迭代求解需要标定的相机参数。由实验可知,该全自动相机标定方法的棋盘格角点识别能力和相机标定精度,与Bouguet的相机标定工具箱相当,且可以显著地减少标定时间和工作量。利用20幅分辨率为640×480的靶标图像标定相机仅需16 s。     

Camera-robot transform for vision-guided tracking in a manufacturing work cell

The problem of camera calibration from the perspective of hand-eye integration (henceforth referred to as the Camera-Robot (CR) problem), is addressed in this paper. Mapping results obtained from a least-squares fit using pseudo-inverse technique and a three layer neural network are compared. The calibration matrix is developed to map the image coordinates of an IRI D256 vision processor equipped with a CCD camera directly on to the coordinates for an IBM 7540 SCARA manipulator. One transformation is obtained by performing a least-squares fit using pseudo-inverse technique on a set of one hundred data points which relates two-dimensional (2D) image coordinates to corresponding twodimensional robot coordinates. The CR problem is also approached by using the same data points on a neural network. The results not only demonstrate the ability of neural networks to ‘learn’ the transformation to a reasonable accuracy, but also from the basis for a relatively simple method of adaptive self-calibration of robot-vision systems. In a broader sense, the proposed method can be used to calibrate a variety of robotic sensors that are typically used in a flexible manufacturing environment.     

基于图像匹配的飞行器导航定位算法及仿真

在飞行器导航定位优化控制的研究中,针对无法获得GPS信号时,长航时飞行器捷联惯导系统存在误差,提出了关于图像地标的飞行器精确定位算法,为实现成像系统对捷联惯导系统的误差校正奠定了基础。利用特定地标在机载摄像机像平面上的图像信息,通过图像变换、匹配定位、坐标变换,解算摄像机空间三维坐标和三个姿态角。再根据摄像机坐标系与机体坐标系的变换关系,推算飞行器精确位置。通过计算机仿真对算法进行了验证,结果表明,提出的算法能够满足GPS不可用时长航的飞行器组合导航精度要求,研究结果对长航时飞行器导航具有一定借鉴和参考价值。     

一种鲁棒的基于两个特征点的深度估计方法

This paper presents a novel depth estimation method based on feature points. Two points are selected arbitrarily from an object and their distance in the space is assumed to be known.The proposed technique can estimate simultaneously their depths according to two images taken before and after a camera moves and the motion parameters of the camera may be unknown. In addition, this paper analyzes the ways to enhance the precision of the estimated depths and presents a feature point image coordinates search algorithm to increase the robustness of the proposed method.The search algorithm can find automatically more accurate image coordinates of the feature points based on their detected image coordinates. Experimental results demonstrate the efficiency of the presented method.     

The Effect of Noise on Camera Calibration Parameters

Camera calibration is the estimation of parameters (both intrinsic and extrinsic) associated with a camera being used for imaging. Given the world coordinates of a number of precisely placed points in a 3D space, camera calibration requires the measurement of the 2D projection of those scene points on the image plane. While the coordinates of the points in space can be known precisely, the image coordinates that are determined from the digital image are often inaccurate and hence noisy. In this paper, we look at the statistics of the behavior of the camera calibration parameters, which are important for stereo matching, when the image plane measurements are corrupted by noise. We derive analytically the behavior of the camera calibration matrix under noisy conditions and further show that the elements of the camera calibration matrix have a Gaussian distribution if the noise introduced into the measurement system is Gaussian. Under certain approximations we derive relationships between the camera calibration parameters and the noisy camera calibration matrix and compare it with Monte Carlo simulations.     

基于普通相机的相对定向直接解法

该文介绍了采用双摄像机系统模拟人的双眼以实现计算机视觉的全过程。考虑到照相机所拍摄的左、右影像之间的相关性,可以通过影像匹配推导出这两个相片坐标系之间的相对坐标关系,再以此为基础计算所拍摄场景的立体信息。该方法对摄像工具及摄影条件没有专门的要求,应用范围广,具有很大的实用价值。     

Segment Based Camera Calibration

The basic idea of calibrating a camera system in previous approaches is to determine camera parmeters by using a set of known 3D points as calibration reference.In this paper,we present a method of camera calibration in whih camera parameters are determined by a set of 3D lines.A set of constraints is derived on camea parameters in terms of perspective line mapping.Form these constraints,the same perspective transformation matrix as that for point mapping can be computed linearly.The minimum number of calibration lines is 6.This result generalizes that of Liu,Huang and Faugeras^[12] for camera location determination in which at least 8 line correspondences are required for linear computation of camera location.Since line segments in an image can be located easily and more accurately than points,the use of lines as calibration reference tends to ease the computation in inage preprocessing and to improve calibration accuracy.Experimental results on the calibration along with stereo reconstruction are reported.     

结构光在集装箱顶角加强板焊缝定位中的应用

针对集装箱顶角加强板的焊接特点,设计了一种基于结构光的自动焊缝定位方法。该方法主要有两个特点：一是将摄像机安装在机器人上,使光轴垂直工作平面,通过比较面积大小来获得深度信息,从而将三维信息的获取问题转化为二维信息的提取,并利用仿射变换获得空间信息;二是利用激光器与摄像机相配合的方式进行定位,由于激光束在焊接环境中的特征明显,可降低图像处理的难度。具体实现过程是利用两条线激光打在加强板的一个角上,在加强板和顶板之间的焊缝处出现转折,通过对激光条纹的有效处理,获得4对转折点的图像坐标,然后拟合直线求出加强板的焊接初始位置及方向,再结合事先制作的图像模板将加强板的其他特征点逐一求出。该系统有效地利用了激光器的优势,可靠地解决了加强板焊缝的自动定位问题。     

A New Approach to Pose Detection using a Trinocular Stereovision System

Object location and tracking is a major issue in computer vision. This problem is normally solved through the extraction of representative features of the object, and the two-dimensional coordinates of these image features are used to compute the position of the object. When more than one camera is used, a certain similarity measure between the image features extracted from both stereoscopic images helps to match the correspondences. In this way, three-dimensional measurements can be recovered from the 2D coordinates of the features extracted from different cameras. In this paper the use of a trinocular system is considered to estimate both the position and velocity of known objects by using their apparent area, and with no use of the image-plane coordinates of the object 's features. A high precision low-level image processor has been developed for performing object labeling and noise filtering of the images at video rate. Then, a position measurement tool uses the apparent area captured by every camera to locate the object. This enables us to estimate the position of the object. Finally, a prediction tool refines the estimation in locating the object. We show the performance of the trinocular system with a real implementation. This system has been designed to process the images provided by any conventional of high-speed cameras at video rate.     

基于计算机视觉的目标二维坐标定位实现

提出一种摄像机内部参数未知条件下,由单幅图像实现的目标二维定位方法。推导图像像素坐标系转换至车体坐标系的转换矩阵,并介绍该转换矩阵的求解方法和步骤。实验证明,本单幅图像目标二维坐标定位方法易于实现,定位精度较高（P=1．47mm）,最大误差为2．95mm,能够满足机器人计算机视觉目标定位使用要求．     

A new efficient and direct solution for pose estimation usingquadrangular targets: algorithm and evaluation

Pose estimation is an important operation for many vision tasks. In this paper, the authors propose an algorithm for pose estimation based on the volume measurement of tetrahedra composed of feature-point triplets extracted from an arbitrary quadrangular target and the lens center of the vision system. The inputs to this algorithm are the six distances joining all feature pairs and the image coordinates of the quadrangular target. The outputs of this algorithm are the effective focal length of the vision system, the interior orientation parameters of the target, the exterior orientation parameters of the camera with respect to an arbitrary coordinate system if the target coordinates are known in this frame, and the final pose of the camera. The authors have also developed a shape restoration technique which is applied prior to pose recovery in order to reduce the effects of inaccuracies caused by image projection. An evaluation of the method has shown that this pose estimation technique is accurate and robust. Because it is based on a unique and closed form solution, its speed makes it a potential candidate for solving a variety of landmark-based tracking problems     

基于全维视觉的足球比赛机器人目标定位

本文给出了robocup中型组足球比赛机器人在全维视觉情况下,对足球比赛中目标定位的一种方法。文中推导了一种针对全向摄像机镜面投影的变换关系,该方法简单有效,为全维视觉下的图像处理提供了必要模型。实验结果证明了该种方法的有效性。     

与视点对应的视图插补

ＩｍａｇｅＭｏｒｐｈｉｎｇ是产生图象之间插补变换一类被广泛使用的技术,所有的Ｍｏｒｐｈｉｎｇ方法奢阳基于两幅图象之间的象素位置和颜色的内插。现行的Ｍｏｒｐｈｉｎｇ技术不能保证所生成图象的真实性。本文提出的视图插 补技术,使得所生成的插 补图象对应于某个视点,且不需要知识源图象所对应的摄像朵矩阵,因此计算简单。     

A three-dimensional feature space iterative clustering method for multi-spectral image classification

A practical method of three-dimensional feature space iterative clustering (3D-FSIC) for image classification has been introduced, in which the clustering iteration is performed in three-dimensional feature space rather than scanning the image pixel by pixel. This method permits the cluster size and pixel frequency to be taken into account so that a more advanced decision rule, the optimal multiple point reassignment (OMPR) can be applied. The paper also provides a simple technique for splitting a cluster based on the first principal component without performing principal component transformation. Finally, a classification example using hue images as well as a discussion of the advantages of using hue images in the 3D-FSIC classification is given.     

Projection matrix by orthogonal vanishing points

Calculation of camera projection matrix, also called camera calibration, is an essential task in many computer vision and 3D data processing applications. Calculation of projection matrix using vanishing points and vanishing lines is well suited in the literature; where the intersection of parallel lines (in 3D Euclidean space) when projected on the camera image plane (by a perspective transformation) is called vanishing point and the intersection of two vanishing points (in the image plane) is called vanishing line. The aim of this paper is to propose a new formulation for easily computing the projection matrix based on three orthogonal vanishing points. It can also be used to calculate the intrinsic and extrinsic camera parameters. The proposed method reaches to a closed-form solution by considering only two feasible constraints of zero-skewness in the internal camera matrix and having two corresponding points between the world and the image. A nonlinear optimization procedure is proposed to enhance the computed camera parameters, especially when the measurement error of input parameters or the skew factor are not negligible. The proposed method has been run on real and synthetic data for more precise evaluations. The provided experimental results demonstrate the superiority of the proposed method.     

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.