光栅投影式3维照相系统中光栅投影仪的标定

提出了一种光栅投影式立体照相系统中光栅投影平面的标定方法。将光栅投影到已知目标平面,通过数字摄影得到光栅平面与目标平面相交的光栅图像。根据光栅图像记录的信息,在每条交线上提取两点,作为对应光栅平面拟合用的两点。按一定规律移动目标平面,即可得到每个光栅平面上若干对点。利用最小二乘法分别拟合出每个光栅平面的空间平面方程。     

一种优化的消失点估计方法及误差分析

空间一组平行直线在图像平面上所成的像的交点称为消失点. 消失点可以提供大量的场景三维结构信息. 本文提出一种新的优化的消失点估计方法. 该方法基于随机采样一致算法(Random sample consensus, RANSAC)对图像空间中的线段进行聚类, 通过最小化Sampson误差获得消失点的极大似然估计(Maximum likelihood estimation, MLE). 该方法不需要预知摄像机参数及直线的三维位置信息. 为了对该算法进行定量评估, 构造了基于反向传播的消失点误差传递模型. 实验结果验证了本文提出算法的有效性.     

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.     

基于场景几何约束未标定两视图的三维模型重建

提出了一种从两幅未标定图象重建场景三维模型的方法 .这种方法充分利用了人造结构场景中大量存在的平行性和正交性几何约束 ,即利用每幅视图中三组互相垂直的平行线 ,计算出 3个影灭点 ,从而对每幅视图进行标定 .对两幅未标定图象 ,从基本矩阵只能得到射影重构 ,如果每幅图象都已标定 ,则可将基本矩阵转化为本质矩阵 .三维重构过程有两个步骤 :先是恢复相机的位置和运动 ;后是用三角测量法计算出点的三维坐标 .对多平面组成的场景进行三维重构实验 ,所得三维模型产生新的视点图象 ,与所观察的场景一致 ,重构的两个平面夹角与实际值相近 ,实验结果表明 ,该算法是行之有效的     

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 2 个灭点和局部尺度的三维空间尺度估计

三维空间尺度估计是三维重建中的一个重要工作,现实世界中也存在一些基于单幅图像进行三维空间尺度估计的需求。通常情况下,尺度估计需先对相机进行标定。根据单目图像符合透视原理的特性,提出了一种基于 2 个灭点和局部尺度信息的方法对相机进行标定,从而得到单目图像物体中三维空间尺度信息的估计。首先,从单目图像中选择 2 组互相正交的平行线组,得到对应 2 个灭点的坐标;然后,利用灭点坐标和焦距信息得到世界坐标系和相机坐标系之间的旋转矩阵,再利用灭点的性质和已知局部尺度信息得到平移向量,完成单目相机的标定;最后,还原二维图像中像素点对应的三维世界坐标值,计算出图像中 2 个像素点在三维空间的尺度信息。实验结果表明,该方法能有效地对单幅图像中的建筑物体进行尺度估计。     

一种快速的人工场景图像消失点检测方法

人工场景中包含了大量的空间平行线以及垂直边,这些空间平行线映射到图像中相交产生的交点即消失点。消失点检测对摄像机标定、三维场景重建等都有着重要的意义。传统的消失点检测算法往往基于二维霍夫参数空间,复杂度高、效率低。因此,提出一种新的方法,先检测图像中较长的边界线,并将检测到的线段进行筛选、分组;然后利用消失点与焦距之间的制约关系,确定三向消失点的位置以及焦距的大小。该方法将传统的二维霍夫参数空间转换为二级一维霍夫参数空间。实验表明,这种方法运算复杂度低、运行时间短。在室外场景图像中,鲁棒性好,且保持较高的准确率。     

A study on the dual vanishing point property

Vanishing points and vanishing lines are useful information in computer vision. In this study, an interesting dual property of vanishing point is first introduced. Next, we point out that there also exists a dual property of vanishing line. With the dual vanishing point and vanishing line properties, some 3D intersection inference can be made based on their image lines. Two applications are given to illustrate the usage of the new results. The first one is to derive the 3D pose determination of a circle using two parallel image lines. The second one uses six specially designed 3D lines to adjust the cameras with respect to a fixture in a binocular vision system such that the resultant camera coordinate axes become parallel.     

Hypercatadioptric line images for 3D orientation and image rectification

In central catadioptric systems 3D lines are projected into conics. In this paper we present a new approach to extract conics in the raw catadioptric image, which correspond to projected straight lines in the scene. Using the internal calibration and two image points we are able to compute analytically these conics which we name hypercatadioptric line images. We obtain the error propagation from the image points to the 3D line projection in function of the calibration parameters. We also perform an exhaustive analysis on the elements that can affect the conic extraction accuracy. Besides that, we exploit the presence of parallel lines in man-made environments to compute the dominant vanishing points (VPs) in the omnidirectional image. In order to obtain the intersection of two of these conics we analyze the self-polar triangle common to this pair. With the information contained in the vanishing points we are able to obtain the 3D orientation of the catadioptric system. This method can be used either in a vertical stabilization system required by autonomous navigation or to rectify images required in applications where the vertical orientation of the catadioptric system is assumed. We use synthetic and real images to test the proposed method. We evaluate the 3D orientation accuracy with a ground truth given by a goniometer and with an inertial measurement unit (IMU). We also test our approach performing vertical and full rectifications in sequences of real images.     

EM enhancement of 3D head pose estimated by point at infinity

Head pose estimation is a key task for visual surveillance, HCI and face recognition applications. In this paper, a new approach is proposed for estimating 3D head pose from a monocular image. The approach assumes the full perspective projection camera model. Our approach employs general prior knowledge of face structure and the corresponding geometrical constraints provided by the location of a certain vanishing point to determine the pose of human faces. To achieve this, eye-lines, formed from the far and near eye corners, and mouth-line of the mouth corners are assumed parallel in 3D space. Then the vanishing point of these parallel lines found by the intersection of the eye-line and mouth-line in the image can be used to infer the 3D orientation and location of the human face. In order to deal with the variance of the facial model parameters, e.g. ratio between the eye-line and the mouth-line, an EM framework is applied to update the parameters. We first compute the 3D pose using some initially learnt parameters (such as ratio and length) and then adapt the parameters statistically for individual persons and their facial expressions by minimizing the residual errors between the projection of the model features points and the actual features on the image. In doing so, we assume every facial feature point can be associated to each of features points in 3D model with some a posteriori probability. The expectation step of the EM algorithm provides an iterative framework for computing the a posterori probabilities using Gaussian mixtures defined over the parameters. The robustness analysis of the algorithm on synthetic data and some real images with known ground-truth are included.     

基于侧影轮廓的图像三维重建方法

针对自标定单幅图像的三维重建问题,提出一种基于侧影轮廓的图像三维重建方法。使用成角度平面镜装置拍摄目标物体,对所得图像进行边缘跟踪,得到闭合的轮廓曲线,利用满足平面镜成像原理的2对侧影轮廓计算灭点,根据灭点间的约束关系计算相机参数,在此基础上重建得到物体的三维模型。实验结果表明,该方法能快速重建逼真的三维模型。     

Perspective Geometry Based Single Image Camera Calibration

This paper presents a novel method for 3D camera calibration. Calculation of the focal length and the optical center of the camera are the main objectives of this research work. The proposed technique requires a single image having two vanishing points. A rectangular prism is employed as the calibration target to generate vanishing points. The special arrangement of the calibration object adds more accuracy in finding the intrinsic parameters. Based on the geometry of the perspective distortion of the edges of the prisms from the image, vanishing points are found. There on, fixing up the picture plane followed by fixing up of the station point is carried out based on the relations that are formulated. Experimental results of our method are likened with Zhang’s method. Results are tabulated to show the accuracy of the proposed approach.

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.     

高速公路云台相机的自动标定

目的 云台相机因监控视野广、灵活度高,在高速公路监控系统中发挥出重要的作用,但因云台相机焦距与角度不定时地随监控需求变化,对利用云台相机的图像信息获取真实世界准确的物理信息造成一定困难,因此进行云台相机非现场自动标定方法的研究对高速公路监控系统的应用具有重要价值。方法 本文提出了一种基于消失点约束与车道线模型约束的云台相机自动标定方法,以建立高速公路监控系统的图像信息与真实世界物理信息之间准确描述关系。首先,利用车辆目标运动轨迹的级联霍夫变换投票实现纵向消失点的准确估计,其次以车道线模型物理度量为约束,并采用枚举策略获取横向消失点的准确估计,最终在已知相机高度的条件下实现高速公路云台相机标定参数的准确计算。结果 将本文方法在不同的场景下进行实验,得到在不同的距离下的平均误差分别为4.63%、4.74%、4.81%、4.65%,均小于5%。结论 对多组高速公路监控场景的测试实验结果表明,本文提出的云台相机自动标定方法对高速公路监控场景的物理测量误差能够满足应用需求,与参考方法相比较而言具有较大的优势和一定的应用价值,得到的相机内外参数可用于计算车辆速度与空间位置等。     

单目平行线约束下的空间点坐标恢复

提出一种从单目像机拍摄的二维投影图像中恢复空间点三维坐标的简化算法.假定摄像机与空间中的一对平行线的位置关系已知,同时摄像机坐标系下平行线所在平面中的任意空间点在像平面中的投影也已知,通过射影几何可得图像坐标到目标点三维空间坐标的转换系数,即坐标转换因数,从而估计出三维空间点的坐标.实验中通过对人体行走轨迹进行估计来测试算法性能,结果表明算法简单有效,计算开销小.     

交通视觉监控中的摄像机参数求解

该文提出了一种适合于交通场景视觉监控实际应用中的方便的投影参数求取方法，主要着眼于方法的易用性，并在此基础上改进了模型可视化算法，它首先利用地平面与图像中的对应点建立一个对应矩阵(homography)，然后利用已知的摄像机高度以及地面上垂线在图像中的投影等约束信息来求取摄像的参数，实验证明，该文提出的方法是有效的，而且能够比别的方法更方便地解决视觉交通监控实际中的摄像机标定和模型可视化问题。     

基于平行线的室内视觉导航

根据一组空间直线上的无穷远线素集在视平面上所形成的消失点对直线的平移具有稳定性的特点，提出了基于平行线的摄像机参数标定和自主移动平台室内视觉导航算法．在对摄像机进行标定时，将摄像机模型简化成关于移动平台航向角和X方向距离的线性模型，并利用走廊左右踢脚线在视平面上的投影直线的斜率、消失点坐标来标定摄像机的内、外参数；在视觉导航时，视走廊左右踢脚线为一组平行线，由其在视平面上的投影直线的斜率、消失点坐标，控制自主移动平台行驶的X方向距离和航向角，实现平台的室内视觉导航．本文采用YIQ彩色模型分割楼道图像，由Hough变换与最小二乘法相结合的方法提取楼道踢脚线．     

Calibration of Central Catadioptric Cameras Using a DLT-Like Approach

In this study, we present a calibration technique that is valid for all single-viewpoint catadioptric cameras. We are able to represent the projection of 3D points on a catadioptric image linearly with a 6×10 projection matrix, which uses lifted coordinates for image and 3D points. This projection matrix can be computed from 3D–2D correspondences (minimum 20 points distributed in three different planes). We show how to decompose it to obtain intrinsic and extrinsic parameters. Moreover, we use this parameter estimation followed by a non-linear optimization to calibrate various types of cameras. Our results are based on the sphere camera model which considers that every central catadioptric system can be modeled using two projections, one from 3D points to a unitary sphere and then a perspective projection from the sphere to the image plane. We test our method both with simulations and real images, and we analyze the results performing a 3D reconstruction from two omnidirectional images.     

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。     

基于仿真的无人机遥感图像拼接误差分析

针对多幅无人机遥感图像连续拼接时,累积误差逐渐增大导致拼接的图像严重扭曲变形的问题,提出了一种基于前方交会的投影误差修正算法。该算法根据前方交会原理,解算出同名点对所对应3D点的空间坐标;然后,将所有3D点正射投影到同一物平面上,并将正射点重投影到像平面上,获取校正后的同名点对;最后,利用M估计抽样一致性(MSAC)算法估计单应性矩阵,并进行图像拼接演示。仿真实验结果表明,所提算法能有效地消除投影误差,进而达到抑制无人机遥感图像拼接误差的目的。