多场景下基于快速相机标定的柱面图像拼接方法

针对目前利用相机标定参数进行图像拼接的方法存在受场景限制大、标定过程复杂而耗时长的问题,提出一种多场景下基于快速相机标定的柱面图像拼接方法。首先,利用棋盘格标定板角点特征提取精度高的特点,使其分别位于两两邻接图像的重叠视场中,对该图像序列依次进行角点提取、精确化和匹配等预处理,以准确快速求解出待拼接图像间的配准参数;然后利用标定得到的配准参数快速拼接图像,通过柱面投影以保持图像的视觉一致性,并采用多频段融合以保留图像的细节信息;最后,将整个系统搭建在低功耗嵌入式平台,实现可在多场景下完成快速标定及基于标定参数的拼接过程。实验结果表明,该文方法在室内及隧道等场景下可准确快速完成相机标定,图像拼接过程耗时短,同时可保证较高的拼接精度和较好的成像效果,具有较强的鲁棒性。     

一种立体视觉测量高精度标定新方法

提出一种实现通用摄像机标定和现场高精度立体视觉传感器标定的新方法。该方法无需预先给定初始参数,而是根据投影矩阵计算摄像机参数的初始值,结合镜头畸变的标定数学模型,实现通用摄像机标定;在立体视觉传感器三维测量模型基础上,引入目标距离约束建立结构参数标定优化目标函数,从而得到使空间距离偏差最小的最优结构参数,实现传感器现场高精度标定。实验结果表明,上述方法标定精度较高,已标定传感器空间距离测量相对误差小于0.45%。     

An efficient camera calibration and optimisation method based on orthogonal vanishing points

A more efficient camera calibration and optimisation method based on orthogonal vanishing points is proposed. After extracting corner points on the calibration images by using the Harris algorithm, some parallel lines are fitted via the least square method. The Levenberg–Marquard (L–M) algorithm is used to find out the optimum vanishing points. The intrinsic parameters are solved on the basis of the orthogonal vanishing point characters. The enhanced Tsai’s method is designed to solve the extrinsic parameters. The minimum residue optimisation function is introduced to calculate the distortion coefficients and optimise all parameters globally. The feasibility and efficiency of the proposed method is illustrated by an experimental study. The method can be used to obtain high precision calibration parameters without the need of sophisticated mechanical devices. It has great potential to calibration cameras in the field of computer vision, 3D measurement, etc.     

三线阵CCD相机亚像元精度几何标定方法研究

为了解决三线阵CCD立体测绘相机内方位元素的几何标定问题,提出了一种实现相机亚像元精度内方位元素和交会角标定的方法。该方法利用光学高精度角度发生器和CCD细分测量相结合的检测方法,由最小二乘回归分析,实现相机内方位元素的标定,并通过扫描星点图像标定交会角。校飞实验的数据处理结果验证了该方法的有效性。     

Three dimension reconstruction through measure-based image selection

Three dimension (3D) reconstruction is one of the research focus of computer vision and widely applied in various fields. The main steps of 3D reconstruction include image acquisition, feature point extraction and matching, camera calibration and production of dense 3D scene models. Generally, not all the input images are useful for camera calibration because some images contain similar and redundant visual information. These images can even reduce the calibration accuracy. In this paper, we propose an effective image selection method to improve the accuracy of camera calibration. Then a new 3D reconstruction algorithm is proposed by adding the image selection step to 3D reconstruction. The image selection method uses structure-from-motion algorithm to estimate the position and attitude of each camera, first. Then the contributed value to 3D reconstruction of each image is calculated. Finally, images are selected according to the contributed value of each image and their effects on the contributed values of other images. Experimental results show that our image selection algorithm can improve the accuracy of camera calibration and the 3D reconstruction algorithm proposed in this paper can get better dense 3D models than the normal algorithm without image selection.     

The effects of a camera's intrinsic parameters on the determination of a coordinate frame

Abstract

In this paper, two methods, the Individual Calibration Method (ICM) and the Simultaneous Calibration Method (SCM), are used to calibrate a vision system with two cameras. The first approach calibrates a camera individually. For the second approach, two or more sets of camera intrinsic and extrinsic parameters are determined simultaneously such that the position errors for the 3‐D points are minimized. We show, theoretically and experimentally, the following results: (a) the offsets of the image centers do not affect significantly the position and orientation of a coordinate frame with respect to a stereo vision system with two cameras; (b) the lens distortion will not change dramatically the position and orientation of a coordinate frame with respect to a stereo vision system. We also find that (a) there is a difference in the determination of an object's location for the simultaneous and individual calibration methods; (b) the approach of calibrating two cameras simultaneously has the advantage of better accuracy over the method of individual calibration.     

基于卡尔曼滤波的摄像机标定方法

本文提出了一种基于迭代扩展卡尔曼滤波的摄像机标定方法.将二维平面靶标图像上的特征点看作是匀速运动的点,以观测到的特征点图像坐标和对应世界坐标作为滤波器的输入,摄像机内外参数的估计值作为滤波器的输出,根据迭代扩展卡尔曼滤波算法得到摄像机内外参数的最优估计.通过仿真和真实实验,结果表明,对于有限数量的平面靶标标定图像数据,该算法具有较高的标定精度和较好的鲁棒性.     

基于LS-SVM的立体视觉摄像机标定

利用最小二乘支持向量机来直接学习图像信息与三维信息之间的关系,不需确定摄像机具体的内部参数和外部参数.在双目视觉的情况下,两摄像机的位置关系不需具体求出,而是隐含在映射关系中.根据最小二乘支持向量机与摄像机标定的特点,提出了基于最小二乘支持向量机的双目立体摄像机标定方法.将摄像头采集到的图像的像素坐标作为输入,将世界坐标作为输出,用最小二乘支持向量机使网络实现给定的输入输出映射关系.该方法同BP神经网络预测结果对比表明:基于最小二乘支持向量机的双目视觉标定方法速度快,实时性好,能有效提高标定精度.     

立体视觉曲面重建与系统误差分析

采用立体视觉空间曲面重建技术对三维曲面表面成像进行边缘提取、图像匹配、匹配点空间位置计算等步骤,得到三维曲面表面点的空间位置,利用空间点信息对三维曲面形状进行重建,恢复曲面三维形状;并讨论了立体视觉系统的摄像机分辨率、测量范围和摄像机间距等参数之间的关系.利用该方法对堆积物表面形状及体积进行测量实验结果表明,该方法能准确、快速、方便地给出三维曲面的形状.     

面向建筑物重建的相机标定方法研究

由于灭点具有很多独特的几何属性且大量地存在于建筑物场景中,因此针对建筑物重建,提出了一种基于灭点的相机标定方法。首先通过构造各种几何约束关系,如灭点与相机矩阵间的关系、世界坐标原点和相机矩阵间的关系,逐步实现相机矩阵的度量重建和欧氏重建,获得相机矩阵的值,然后通过分解相机矩阵得到相机的内外部参数。该方法在标定过程中无须借助任何标定物,求解过程简单,标定速度快。实验结果表明,该方法可以满足虚拟现实中建筑物场景重建的需要。     

基于单特征点的手眼系统摄像机标定

基于机器人的相对运动,提出了一种基于单特征点的手一眼系统摄像机内、外参数的标定方法。在摄像机视场中任意选择一个特征点,使机器人末端在保持姿态不变的条件下运动,相当于机器人不动但具有多个特征点。利用这些点可计算出摄像机的内参数和相对于特征点的外参数。大幅度改变机器人的姿态,可标定出特征点在机器人基坐标系下的位置,进而得到相对于机器人末端的外参数。实验结果验证了该方法的有效性。     

基于标准球的显微图像测量系统标定方法

提出一种利用标准球对显微图像测量系统进行标定的方法．该方法利用显微系统小视场小景深的成像特点简化计算模型,减少标定参数．通过标准球图像在水平和垂直方向上的直径比计算比例因子;利用标准球边缘图像的边缘点集．运用优化的方法来计算成像系统的畸变系数和主点位置．系统的放大倍数由标准球的实际直径来标定得出．利用标准球在多摄像机公共视场内其轮廓在任何位置均可见这一特性,可同时对显微图像测量系统中的多个摄像机进行标定,简化标定过程．实验结果表明,该方法标定精度较高,标定后的测量系统的极限误差3σ为2．4μm．     

双目视觉摄像机神经网络标定方法

摄像机标定是精密视觉测量的基础。为了描述双目视觉中三维空间物点坐标和两个摄像机像面像点坐标间的非线性关系,传统的标定方法需要建立复杂的数学模型。而神经网络可以有效地处理非线性映射问题,笔者介绍了一种BP(ErrorBackPropagation)神经网络,并且为了提高网络的学习能力引入了动态因子。用相同的参考数据,将神经网络标定方法与线性标定方法比较,实验结果表明基于神经网络的双目视觉标定方法能获得较高的标定精度。     

Research on the application of single camera stereo vision sensor in three-dimensional point measurement

A single camera stereo vision sensor model based on planar mirror imaging is proposed for measuring a three-dimensional point. The model consists of a CCD camera and a planar mirror. Using planar mirror reflections of a scene, a picture with parallax is obtained by shooting the target object and its virtual image. This is equivalent to shooting the target object from different angles with the camera and the virtual camera in the planar mirror, so it has the function of binocular stereo vision. In addition, the measurement theory of the three-dimensional point is discussed. The mathematical model of a single camera stereo vision sensor is established, the intrinsic and extrinsic parameters are calibrated, and the corresponding experiment has been done. The experimental results show that the measuring method is convenient and effective; it also has the advantages of simple structure, convenient adjustment, and is especially suitable for short-distance measurement with high precision.     

双目立体视觉中特征点三维坐标重构校准研究

双目立体视觉是一种商业化较成熟的三维测量技术,左右摄像机内外参数的精确标定是实现三维重构的基础和关键。针对棋盘格和圆点两种标定板图案研究了相应的图像处理技术,实现了标定点的亚像素精度定位及其有效排序。采用基于单映性约束和非线性优化的多视角平面标定算法实现了摄像机光学及空间位置参数求解。用极线约束残差法衡量标定结果的准确度。基于桥式三坐标标准实现标定点三维重构平面度以及多平面空间夹角测量结果的校准,基于光学三坐标标准实现了标定点三维重构空间距离的校准,并分析了校准结果的不确定度。圆标志点三维重构空间距离示值误差为0.029 mm,不确定度U=24 μm（k=2）     

光栅投影式三维摄影测量仪的几何标定方法

光栅投影式三维摄影测量仪利用了时域结构光投影技术和立体视觉测量原理获得三维点坐标。针对传统标定方法易受镜头畸变影响和标定约束方程少导致精度下降的问题,采用了非线性的摄像机和投影机模型,并提出了二维的投影机模型;使用多平面法标定了系统测量所需的摄像机和投影机几何参数;为进一步提高参数精度,采用Levenberg-Marquardt算法优化了摄像机和投影机模型。实验结果表明,该方法操作简单,无需精确的位置和姿态调整,标定的绝对精度为0.2pixel,相对精度为1/5000。     

基于相位测量的摄像机标定方法

提出一种新的摄像机标定方法,该方法基于2D共面参照物摄像机标定方法和相移技术.首先由摄像机记录已知相位分布的平面,通过相移计算出相位分布.对任意标定平面,两个正交相位分布与平面坐标的对应关系已知,由此可以建立像素坐标与2D平面坐标的一一对应关系.将该已知相位分布的平面在空间中放置不同的位置,分别完成上述测量,即可根据2D共面参照物摄像机标定方法完成摄像机的高精度标定.该方法避开了复杂的标定特征点提取,大大增加了2D标定数据量,提高了标定精度.实验和计算机模拟表明,该方法简单、可靠和精度高.     

双视角三维测量系统同时标定方法

针对现有标定方法在相机无公共视场情况下的局限性,本文提出使用双平面标定板对双相机进行同时标定的方法.通过推导两个相机与两个标定板间的坐标变换,将待标定相机与参考相机的相对位姿关系的求解转换为较为成熟的手眼标定方程求解.通过实验验证:该方法可实现双相机的同时标定,且方法的绝对误差不超过0.089 mm,较为可靠;在双视角...     

AHSPP Annual Conference

Abstract

A calibration and three-dimensional (3D) reconstruction method is presented based on images reflected from planar mirrors and acquired with one camera. The geometric model of the camera–mirror set and a method of calibrating it are described. The calibration technique computes the model parameters using linear equations, and it is proved that the calibration is possible with the knowledge of only six 3D points. The reconstruction method is based on a volumetric representation. The 3D reconstruction is based on a space carving algorithm and the calibration method described in the paper. The results of the calibration and reconstruction method show the efficiency of both techniques. This set-up enables a simple and inexpensive multi-ocular system to be built to recover the 3D structure of volumes.