基于OpenCV改进的摄像机标定法

针对计算机视觉领域的摄像机标定问题,考虑到畸变对标定精度的影响,介绍了开放计算机视觉函数库OpenCV和摄像机模型,给出了基于OpenCV的摄像机标定算法;该算法充分运用了OpenCV的函数库功能,具有很高的标定精度和计算效率,可以满足立体视觉系统的需要.     

基于一维标定物的多摄像机标定

一维标定物是由三个或三个以上彼此距离已知的共线点构成的. 现有文献指出只有当一维标定物做平面运动或者绕固定点转动时，才能实现摄像机的标定. 本文的研究结果表明，当多个摄像机同时观察作任意刚体运动的一维标定物时，则该摄像机组能被线性地标定. 本文给出一种线性标定算法，并使用最大似然准则对线性算法结果进行精化. 模拟实验和真实图像实验都表明本文的算法是有效可行的.     

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。     

双目视觉的立体标定方法

为实现双目视觉系统的立体标定,分析了摄像机成像模型,并充分考虑了透镜的径向畸变和切向畸变,提出了一种新的立体标定算法。该算法利用张正友的灵活标定算法,初步求取摄像机的内参数,结合Brown算法并提取图像中角点的子像素级坐标,精确求取摄像机内参数和畸变向量。为方便后续的图像校正,基于前面的单个摄像机标定,通过计算空间中的景物点在左右摄像机成像平面上的位置关系,计算出双目视觉系统中两个摄像机之间的旋转矩阵R和平移向量T,从而实现了立体标定。实验结果表明,该算法能取得较高的精度,可以应用于双目视觉系统。     

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。     

基于OpenCV的摄像机标定

以增强现实系统中摄像机标定技术为研究对象,分析了开放计算机视觉函数库OpenCV中的摄像机模型,特别充分考虑了透镜的径向畸变和切向畸变影响及求解方法,给出了基于OpenCV的摄像机标定算法.该算法充分发挥了OpenCV的函数库功能,提高了标定精度和计算效率,具有良好的跨平台移植性,可以满足增强现实和其它计算机视觉系统的需要.     

基于参考像面法的CCD摄像机标定新技术

摄像机标定是计算机视觉检测中必不可少的步骤.在现有的摄像机参数标定算法中,纯线性算法标定速度快,但标定精度不是很高;相反如采用非线性搜索算法,标定精度高,但存在标定速度慢以及会出现不收敛现象.通过对现有线性标定算法的研究,提出一种既考虑摄像机的径向和切向畸变,又能实现全线性化求解的全新标定算法--参考像面法.经验证该方法标定速度快、精度较高、算法健壮,适用于大型视觉系统多摄像机的快速标定.     

一种基于混沌天牛须搜索算法的摄像机标定方法

为了较好地解决传统智能优化方法在摄像机标定中存在标定精度低、效率和鲁棒性差的问题，提出一种基于混沌天牛须搜索算法的摄像机标定方法。该方法使用MATLAB标定工具箱对摄像机非线性成像模型进行预标定，预标定结果作为混沌天牛须搜索算法的初始值；构造平均重投影误差适应度函数，建立混沌天牛须搜索算法优化模型对标定参数进行优化；与基于传统智能优化方法的摄像机标定方法进行实验对比。实验结果表明，该方法得到的平均重投影误差为0.005 72像素，算法总的运行时间为46.15 s,可以有效提高摄像机标定的精度、鲁棒性与效率。     

基于分块的二维视觉测量系统标定

为了满足高速动态拉伸试验机的的应变测量系统对于高精度的要求,研究了一种基于计算机视觉测量系统的标定技术。应用二次多项式曲面拟合技术,采用了一种基于分块的摄像机模型参数标定方法,不仅能够对摄像机的镜头的畸变进行校正,并且能够获得较高的测量精度。通过对试件的拉伸试验表明:采用基于分块的摄像机模型参数标定方法能取得较高的图像测量精度。     

基于自由运动一维标定物的多摄像机参数标定方法与实验

一维标定方法易于实现且标定效率高,为了克服现有一维标定方法的一些不足,本文提出一种用一维标定物标定多摄像机内外参数的方法,首先进行两两标定,在此过程中,假定主点坐标近似已知而仅考虑畸变、焦距、旋转和平移等参数,接着利用基本矩阵及一维标定物上特征点之间的几何约束,估计两摄像机的内外参数,两两标定完成后,采用Dijkstra最短路径法和捆绑调整对多摄像机系统进行全局标定(含主点坐标),仿真和真实实验表明本文的方法是切实有效的.     

改进基本矩阵计算和优化的多摄像机并行标定算法

多摄像机系统具有摄像机数目多、空间位置分布复杂特点,导致多摄像机标定效率低。基本矩阵计算和非线性优化是摄像机标定算法的关键步骤。针对标定物空间位置相互独立性,改进随机抽样一致性(RANSAC)的基本矩阵计算和简化非线性优化的增量方程,提出多摄像机系统的并行标定算法。该算法挖掘多摄像机标定过程的内在并行化,从而提高了标定的时间效率。相比于传统的多摄像机标定算法,并行算法的时间复杂度从O(n3)降为O(n)。实验结果表明：使用多摄像机系统并行标定算法在不损失精度的同时能够减少标定时间,实现多摄像机系统的快速标定。     

Camera calibration based on receptive fields

Camera calibration is to identify a model that infers 3-D space measurements from 2-D image observations. In this paper, the nonlinear mapping model of the camera is approximated by a series of linear input-output models defined on a set of local regions called receptive fields. Camera calibration is thus a learning procedure to evolve the size and shape of every receptive field as well as parameters of the associated linear model. Since the learning procedure can also provide an approximation extent measurement for the linear model on each of the receptive fields, calibration model is consequently obtained from a fusion framework integrated with all linear models weighted by their corresponding approximation measurements. Since each camera model is composed of several receptive fields, it is feasible to unitedly calibrate multiple cameras simultaneously. The 3-D measurements of a multi-camera vision system are produced from a weighted regression fusion on all receptive fields of cameras. Thanks to the fusion strategy, the resultant calibration model of a multi-camera system is expected to have higher accuracy than either of them. Moreover, the calibration model is very efficient to be updated whenever one or more cameras in the multi-camera vision system need to be activated or deactivated to adapt to variable sensing requirements at different stages of task fulfillment. Simulation and experiment results illustrate effectiveness and properties of the proposed method. Comparisons with neural network-based calibration method and Tsai's method are also provided to exhibit advantages of the method.     

一种基于投影不变量的目标交接改进方法

在多摄像机视频监控系统中,图像之间的视点对应以及目标的交接是重要的研究内容。不需要标定摄像机的参数,该文提出了一种利用尺度不变特征变换（SIFT：scale-invariant features transform）及融合颜色信息的投影不变量实现目标交接的方法。利用SIFT方法自动生成图像间匹配的特征点对,并由此生成视野分界线,然后利用融合颜色信息的投影不变量方法完成对多摄像机之间目标身份的确认。     

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。     

Multi-camera calibration based on an invariant pattern

In this paper we present a method for the calibration of multiple cameras based on the extraction and use of the physical characteristics of a one-dimensional invariant pattern which is defined by four collinear markers. The advantages of this kind of pattern stand out in two key steps of the calibration process. In the initial step of camera calibration methods, related to sample points capture, the proposed method takes advantage of using a new technique for the capture and recognition of a robust sample of projective invariant patterns, which allows to capture simultaneously more than one invariant pattern in the tracking area and recognize each pattern individually as well as each marker that composes them. This process is executed in real time while capturing our sample of calibration points in the cameras of our system. This new feature allows to capture a more numerous and robust set of sample points than other patterns used for multi-camera calibration methods. In the last step of the calibration process, related to camera parameters' optimization, we explore the collinearity feature of the invariant pattern and add this feature in the camera parameters optimization model. This approach obtains better results in the computation of camera parameters. We present the results obtained with the calibration of two multi-camera systems using the proposed method and compare them with other methods from the literature.     

一种非重叠多摄像头的实时监控系统

大范围场景的监控需要使用多个摄像头。论文利用运动目标的颜色信息和路径特征,设计了一种非重叠多摄像头的实时监控系统。系统采用分布式多层次结构,在进行单摄像头层的处理时,根据像素点亮度变化检测和跟踪运动目标,同时获取运动目标的外形信息和路径特征;在进行多摄像头层的处理时,使用估计目标外形变化和建立路径模型方法融合多个摄像头信息,实现目标在非重叠多摄像头的跟踪。该系统不要求校准摄像头,也不要求建立完整的场景模型,即便在有亮度变化的环境中,仍能立即准确跟踪目标。实验证明提出的方法有好的跟踪效果。     

Calibration of 3D kinematic systems using orthogonality constraints

Processing images acquired by multi-camera systems is nowadays an effective and convenient way of performing 3D reconstruction. The basic output, i.e. the 3D location of points, can easily be further processed to also acquire information about additional kinematic data: velocity and acceleration. Hence, many such reconstruction systems are referred to as 3D kinematic systems and are very broadly used, among other tasks, for human motion analysis. A prerequisite for the actual reconstruction of the unknown points is the calibration of the multi-camera system. At present, many popular 3D kinematic systems offer so-called wand calibration, using a rigid bar with attached markers, which is from the end user’s point of view preferred over many traditional methods. During this work a brief criticism on different calibration strategies is given and typical calibration approaches for 3D kinematic systems are explained. In addition, alternative ways of calibration are proposed, especially for the initialization stage. More specifically, the proposed methods rely not only on the enforcement of known distances between markers, but also on the orthogonality of two or three rigidly linked wands. Besides, the proposed ideas utilize common present calibration tools and shorten the typical calibration procedure. The obtained reconstruction accuracy is quite comparable with that obtained by commercial 3D kinematic systems.     

Euclidean upgrading from segment lengths: DLT-like algorithm and its variants

In this paper, how to calibrate a fixed multi-camera system and simultaneously achieve a Euclidean reconstruction from a set of segments is addressed. It is well known that only a projective reconstruction could be achieved without any prior information. Here, the known segment lengths are exploited to upgrade the projective reconstruction to a Euclidean reconstruction and simultaneously calibrate the intrinsic and extrinsic camera parameters. At first, a DLT(Direct Linear Transformation)-like algorithm for the Euclidean upgrading from segment lengths is derived in a very simple way. Although the intermediate results in the DLT-like algorithm are essentially equivalent to the quadric of segments (QoS), the DLT-like algorithm is of higher accuracy than the existing linear algorithms derived from the QoS because of a more accurate way to extract the plane at infinity from the intermediate results. Then, to further improve the accuracy of Euclidean upgrading, two weighted DLT-like algorithms are presented by weighting the linear constraint equations in the original DLT-like algorithm. Finally, using the results of these linear algorithms as the initial values, a new weighted nonlinear algorithm for Euclidean upgrading is explored to recover the Euclidean structure more accurately. Extensive experimental results on both the synthetic data and the real image data demonstrate the effectiveness of our proposed algorithms in Euclidean upgrading and multi-camera calibration.     

结合PCA与HEIV的椭圆目标检测算法

提出结合主元变换与异方差变量含误差模型的椭圆识别与定位方法。根据椭圆长轴对应于椭圆主元方向的特点,利用主元变换法将目标边缘数据变换到主元坐标系,给出新的椭圆轮廓度误差评定方法,将变换后数据点集的椭圆轮廓度误差作为椭圆识别的依据,采用基于异方差变量含误差模型的拟合算法获取椭圆的中心坐标。该方法将任意椭圆转化为标准型椭圆,简化了识别过程,考虑到椭圆数据点的异方差特性,提高了椭圆的定位精度,在噪声方差为0.05情况下,定位精度小于0.04 pixel。