摄像机运动情况下的运动对象检测

在监控应用中,由于场景是已知的,因此可以使用背景减去法检测运动对象.当摄像机进行扫描和倾斜运动时,需要使用多个图像帧才能完整地表示监控场景.如何组织和索引这些背景帧属于摄像机跟踪问题.提出一种无需摄像机标定的背景帧索引和访问方法.这一方法需要使用图像配准技术估计图像初始运动参数.提出一种屏蔽外点的图像配准算法,综合利用线性回归和稳健回归快速估计初始运动参数.为了快速计算连续帧之间的运动参数,提出一种基于四参数模型的优化算法.利用非参数背景维护模型抑制虚假运动象素.室内和户外实验结果表明本文方法是有效的.     

基于共面靶标双目立体视觉传感器标定

根据双目立体视觉传感器的特点,提出了一种基于共面靶标的双目立体视觉传感器的标定方法.在不需要外部测量设备的情况下,通过共面靶标在平面上自由移动,获得靶标的标定点图像坐标,利用类似于Dr.Janne Heikkil(a)和Dr.Olli Silven所提出来的摄像机内部参数标定模型,采用线性和非线性结合的方法对双目摄像机进行摄像机内部参数的标定.试验结果表明,标定参数与出厂参数有一定的差异,但和实际情况还是吻合的,并且标定方法操作简便,切实可行.     

一种新的基于散焦图像的摄像机标定方法

提出一种新的用于散焦求深度的摄像机内参数标定算法。该算法依靠改变摄像机镜头光圈指数获取同一场景的任意两幅散焦程度不同的图像,提取两幅图像间模糊程度差异信息,结合分析透镜成像几何标定出摄像机的相应内参数。此算法解除了2006年由Park所提出的标定方法中必须有一幅聚焦图像的限制,并且无须对图像进行复杂的放大率标准化处理。模拟实验与真实实验均验证了算法的有效性和精确性。     

利用CCD摄像机求解飞行器运动参数

针对飞行器的视觉导航应用,提出了利用CCD摄像机来估算飞行器运动参数的实时方案。在利用CCD摄像机所拍摄的序列图像中,首先,自动提取出第1帧图像中比较明显的特征点,并在后续的图像中对所提取的特征点进行追踪。然后,利用多视图几何技术来标定摄像机的外参数。所标定的摄像机的外参数经过转换就可以获得飞行器的运动参数。通过对实际拍摄的图像进行计算,反投影误差的最大值为0.03911,而处理26帧图像需要的时间为0.9194s,说明本方案在计算精度和实时性上是可行的。     

基于二维标定板的摄像机标定

当今摄像机的标定已然成为机器视觉领域研究的重点。在计算机获取图像并转换成数字信号实现三维欧式重建当中,摄像机的标定是非常关键的一步,采用2D共面激光打印标定板,基于Tsai模型两步标定法对摄像机进行标定。基于图像处理算法提取特征点坐标,然后对标定的初值进行多维无约束非线性优化,从而完成整个标定过程,并获得了对摄像机内外参数较为精确的求解。     

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。     

基于空间平行直线束的CCD摄像机内外参数标定

为提高计算机视觉检测中的摄像机标定算法的效率与稳定性,降低对标定设备的要求,提出了一种新的基于主动视觉的摄像机标定算法.在分析了空间平行直线束的中心投影规律的基础上标定摄像机内外参数.与以往方法不同,在标定过程中保持摄像机位置不动,通过控制标定模板沿具有直线边缘的物体作任意量值的平移运动来实现对摄像机内外参数的求解.利用焦线的直线约束对镜头畸变进行修正,有效地提高标定结果的精度.将内外参数标定分为3个独立的阶段分别进行,克服了整体求解过程中未知参数间的相关性影响.该方法原理简单,且不需要知道模板上的任何物理度量.模拟实验和真实图像实验结果表明了该方法的高精度和高稳定性.     

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.     

基于遗传算法的摄像机自标定方法

摄像机标定是计算机视觉领域的关键技术，其中的自标定是只根据图像计算摄像机的内参数，其标定过程简单，适用性强。由于传统的用于摄像机自标定的Kruppa方程不仅需要计算基础矩阵，还要计算图像的极点，而图像的极点又不是固定不变的，且会导致计算结果的不稳定，为此，针对传统摄像机自标定方法的上述不足，利用遗传算法完成了Hartley新的Kruppa方程的摄像机自标定过程，以便将这个过程完全转化为通过代价函数最小化来求得摄像机的内参数，这就排除了极点的不稳定因素。实验结果表明，该方法是简单、有效的，可以作为一种通用的标定工具。     

直线运动摄像机在线动态标定

为实现AS-R智能机器人在运动情况下摄像机在线动态标定,提出一种新的基于粒子滤波的直线运动摄像机标定方法。用状态空间方法描述直线运动摄像机模型,把摄像机内参数和位置运动参数作为状态量,特征点图像坐标作为观测量,根据粒子滤波算法求得摄像机内参数和位置运动参数的最优估计,并用双线程实现整个标定过程。AS-R机器人在直线运动情况下的摄像机在线动态标定实验结果表明：该算法是合理可行的,并且具有很高的标定精度和良好的鲁棒性。该方法适用于各种类型的系统噪声。     

A new easy camera calibration technique based on circular points

Inspired by Zhang's work on flexible calibration technique, a new easy technique for calibrating a camera based on circular points is proposed. The proposed technique only requires the camera to observe a newly designed planar calibration pattern (referred to as the model plane hereinafter) which includes a circle and a pencil of lines passing through the circle's center, at a few (at least three) different unknown orientations, then all the five intrinsic parameters can be determined linearly. The main advantage of our new technique is that it needs to know neither any metric measurement on the model plane, nor the correspondences between points on the model plane and image ones, hence the whole calibration process becomes extremely simple. The proposed technique is particularly useful for those people who are not familiar with computer vision. Experiments with simulated data as well as with real images show that our new technique is robust and accurate.     

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.     

CCD摄像机标定

在基于单目视觉的农业轮式移动机器人自主导航系统中,CCD摄像机标定是农业轮式移动机器人正确和安全导航的前提和关键。摄像机标定确立了地面某点的三维空间坐标与计算机图像二维坐标之间的对应关系,机器人根据该关系计算出车体位姿值自主导航。因此,根据CCD摄像机针孔成像模型,利用大地坐标系中平面模板上已知的各点坐标,建立与计算机图像空间中各对应像素值之间的关系方程组,在Matlab环境下拟合出摄像机各内外参数。实验结果表明：该方法可以正确完成CCD摄像机标定。     

一种基于TOF相机与CCD相机的联合标定算法研究

针对基于Time-of-Flight(TOF)相机的彩色目标三维重建需标定CCD相机与TOF相机联合系统的几何参数,在研究现有的基于彩色图像和TOF深度图像标定算法的基础上,提出了一种基于平面棋盘模板的标定方法。拍摄了固定在平面标定模板上的彩色棋盘图案在不同角度下的彩色图像和振幅图像,改进了Harris角点提取,根据棋盘格上角点与虚拟像点的共轭关系,建立了相机标定系统模型,利用Levenberg-Marquardt算法求解,进行了标定实验。获取了TOF与CCD相机内参数,并利用像平面之间的位姿关系估计两相机坐标系的相对姿态,最后进行联合优化,获取了相机之间的旋转矩阵与平移向量。实验结果表明,提出的算法优化了求解过程,提高了标定效率,能够获得较高的精度。     

Camera array calibration for light field acquisition

Light field cameras are becoming popular in computer vision and graphics, with many research and commercial applications already having been proposed.Various types of cameras have been developed with the camera array being one of the ways of acquiring a 4D light field image usingmultiple cameras. Camera calibration is essential, since each application requires the correct projection and ray geometry of the light field. The calibrated parameters are used in the light field image rectified from the images captured by multiple cameras. Various camera calibration approaches have been proposed for a single camera, multiple cameras, and amoving camera. However, although these approaches can be applied to calibrating camera arrays, they are not effective in terms of accuracy and computational cost. Moreover, less attention has been paid to camera calibration of a light field camera. In this paper, we propose a calibration method for a camera array and a rectification method for generating a light field image from the captured images. We propose a two-step algorithm consisting of closed form initialization and nonlinear refinement, which extends Zhang’swell-known method to the camera array. More importantly, we introduce a rigid camera constraint whereby the array of cameras is rigidly aligned in the camera array and utilize this constraint in our calibration. Using this constraint, we obtained much faster and more accurate calibration results in the experiments.     

基于4目阵列的计算机视觉三维重建算法

目前计算机视觉三维重建方法因需布置和标定摄像机环形拍摄场或者需要结构光而存在应用局限性问题,且算法不稳定。为此提出一种将摄像机阵列和图像配准有机结合的4目阵列重建算法,该算法不需要结构光和现场标定摄像机。经过基于包含光照和阴影的复杂室内仿真图像的实验表明,该方法能稳定有效地进行密集点云重建,且能克服现有重建方法的应用局限性与不稳定等缺陷。     

基于圆形靶标的数码相机外部参数标定算法

数码相机的外部参数标定是应用计算机视觉进行现场测量和图像处理的关键技术。提出一种基于圆形靶标的数码相机外部参数标定算法,首先提出一种用于估计靶标上圆的圆心在该相机像平面的像坐标的迭代学习算法,在此基础上,利用已知的数码相机内部参数和成像几何模型计算出数码相机的外部参数。实验结果表明算法具有较高的精度和良好的稳定性。     

Self-identifying patterns for plane-based camera calibration

Determining camera calibration parameters is a time-consuming task despite the availability of calibration algorithms and software. A set of correspondences between points on the calibration target and the camera image(s) must be found, usually a manual or manually guided process. Most calibration tools assume that the correspondences are already found. We present a system which allows a camera to be calibrated merely by passing it in front of a panel of self-identifying patterns. This calibration scheme uses an array of fiducial markers which are detected with a high degree of confidence, each detected marker provides one or four correspondence points. Experiments were performed calibrating several cameras in a short period of time with no manual intervention. This marker-based calibration system was compared to one using the OpenCV chessboard grid finder which also finds correspondences automatically. We show how our new marker-based system more robustly finds the calibration pattern and how it provides more accurate intrinsic camera parameters.     

A determination method of initial camera parameters for coplanar calibration

Most coplanar calibration algorithms determine the initial camera parameters from a single image under the assumption that the principal point is known in advance. However, the camera orientations, the shifted principal point and the noise corrupted on images have an influence on the estimated initial camera parameters under the above assumption. This paper proposes a useful method to determine the initial camera parameters for coplanar calibration. The proposed method can determine the initial camera parameters from the single image, wherein the principal point is considered as a parameter. In our experiments, both synthetic and real images are used. The experimental results show that the proposed method provides both stable initial camera parameters and noise robustness for changes of camera orientations, noise levels and shifts of principal point.