基于统计优化的单应矩阵估计算法

计算机科学中研究的图像是真实世界(即二维、三维欧式空间)到像平面的射影变换.平面射影变换(单应)估计是特征目标检测、注册、识别、三维重建等方面的关键步骤,但是如何鲁棒、精确地估计单应矩阵是一个没有很好解决的问题.在研究中发现,基于点与直线的直接的单应矩阵估计方法会导致出现较大误差的情况.针对这一情况,文中介绍了一种基于统计优化的单应矩阵估计方法,这种方法是通过单应矩阵的协方差张量的计算和优化来估计单应矩阵的.最后进行了简单的实验,比较了统计优化方法与进行归一化处理后的直接线性方法的估计结果,证明了基于优化统计的估计方法更加有效     

一种基于平面模板的单应矩阵求解方法

在视觉测量、摄像机标定和三维重建等领域中,单应矩阵的估计都起着至关重要的作用.用平面模板代替传统的传感器进行视觉测量和摄像机标定,由于其成本低,操作快速简单等优点,已经得到了越来越广泛的应用.文中主要讨论了二维成像平面的透视投影模型,给出了一种最大似然估计法获取平面模板与成像平面的单应矩阵的求解过程,并用Levenberg-Marquardt算法对结果进行非线性优化.该方法简单、快速、易实现,在计算机视觉应用中具有一定的理论意义和应用价值.     

一种基于平面模板的单应矩阵求解方法

在视觉测量、摄像机标定和三维重建等领域中,单应矩阵的估计都起着至关重要的作用。用平面模板代替传统的传感器进行视觉测量和摄像机标定,由于其成本低,操作快速简单等优点,已经得到了越来越广泛的应用。文中主要讨论了二维成像平面的透视投影模型,给出了一种最大似然估计法获取平面模板与成像平面的单应矩阵的求解过程,并用Levenberg-Marqurdt算法对结果进行非线性优化。该方法简单、快速、易实现,在计算机视觉应用中具有一定的理论意义和应用价值。     

基于单应矩阵的摄像机标定方法及应用

提出了一种基于单应矩阵的摄像机标定方法,并应用标定结果成功完成了移动机器人的视觉伺服任务。该方法首先根据图像平面和标定板平面之间特征点的对应关系,对单应矩阵进行了估计,进而利用旋转矩阵的单位正交性得到了其对摄像机内参数的约束条件。然后把摄像机内参数矩阵分解为有效焦距与主点位置两部分,并利用最小二乘法分别对其进行求解。针对镜头的径向畸变,恰当地选取了一种畸变模型,并由此得到了一种新的目标函数来对摄像机的所有参数进行非线性优化,从而使获得的畸变系数更适合于从二维图像信号中提取三维位姿信息。最后将标定结果成功应用于移动机器人视觉伺服系统之中,实验结果验证了该标定算法具有简单易用、精度较高等优良性能。     

由平行平面的投影确定无穷远平面的单应矩阵

在三维计算机视觉中,无穷远平面的单应矩阵扮演了极其重要的角色,可使众多视觉问题的求解得到简化.主要讨论如何利用平行平面的投影来求解两个视点间的无穷远平面的单应矩阵,用代数方法构造性地证明了下述结论:(1) 如果场景中含有一组平行平面,则可以通过求解一个一元4次方程来确定两个视点间的无穷远平面对应的单应矩阵;(2) 如果场景中含有两组平行平面,则可以线性地确定两个视点间的无穷远平面对应的单应矩阵.并对上述结果给出了相应的几何解释和具体算法.所给出的结果在三维计算机视觉,特别是摄像机自标定中具有一定的理论意义和应用价值.     

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.     

基于一组对应消失线的度量重建

提出了一种由3幅图像间一组对应消失线进行度量重建的方法.首先利用对应的消失线和模值约束计算出无穷远平面的单应矩阵,然后根据无穷远平面的单应矩阵保持绝对二次曲线的像不动的性质,线性求解摄像机内参数,最后得到度量重建.模拟实验和真实图像实验均验证了这种度量重建方法的可行性和正确性.     

基于单应矩阵的相对位姿改进算法

从单应矩阵恢复相对位姿在视觉导航、视觉伺服应用中具有重要价值,证明了单应矩阵的新性质,并利用该性质改进了基于单应矩阵分解的相对位姿估计算法。与已有算法相比,该算法的候选解个数减半,并扩大了适用范围。理论分析、合成数据和真实数据测试均表明,改进方法使从候选解中筛选出唯一解的运算时间减少了50%,提高了位姿估计的总体运算效率。     

平面单应矩阵对摄像机内参数约束的一些性质

空间x-y坐标平面与图像平面之间的单应矩阵可以提供关于摄像机内参数的2个线性约束.文中研究表明:给定一个一般空间平面与图像平面之间的单应矩阵,如果空间平面在世界坐标系的坐标已知,则该单应矩阵同样可以提供关于摄像机内参数的2个线性约束;如果空间平面在世界坐标系的坐标未知,则该单应矩阵不能构成对摄像机内参数的任何约束.另外,仅仅知道某一个场景中的2幅图像间的单应矩阵不能对摄像机内参数构成任何约束.该结果对从事摄像机标定和三维重建的研究人员有一定的参考作用.     

基于改进RANSAC算法的单应矩阵鲁棒估计方法

单应矩阵的鲁棒性和精度直接决定了其应用效果,如何利用RANSAC算法估计出鲁棒、精确的单应矩阵,仍是一个有待研究的热点问题。针对传统RANSAC算法迭代次数多、运行时间长、单应矩阵估计精度较低的问题,在SIFT特征匹配算法的基础上,从剔除样本集中不符合图像几何特性的部分外点、快速舍弃不合理单应矩阵和迭代精炼单应矩阵等方面对RANSAC算法进行改进,提出一种基于改进RANSAC算法的单应矩阵估计方法,提高了单应矩阵估计的精度和效率。实验结果表明,该方法有效解决了传统RANSAC算法存在的问题,能够快速、精确估计单应矩阵。另外,对于不同视角和大小的图像,该方法均具有较好的鲁棒性。     

A New Analytical Method for Relative Camera Pose Estimation Using Unknown Coplanar Points

We present a new analytical method for solving the problem of relative camera pose estimation. This method first calculates the homography matrix between two calibrated views using unknown coplanar points, and then, it decomposes the matrix to estimate the relative camera pose. We derive a set of new analytical expressions that are more concise than other homography decomposition methods. These analytical expressions are also used to improve the efficiency of a traditional SVD-based homography decomposition method. The performance of our analytical method is studied in terms of both efficiency and accuracy, and it is compared with other homography decomposition methods. Furthermore, the accuracy of our analytical method is tested under different conditions and compared with that of the five-point method through simulations and real image experiments. The experimental results demonstrate that our method is faster and more accurate than other homography decomposition methods and more accurate than the five-point method.     

线性确定无穷远平面的单应矩阵和摄象机自标定

引入了一种新的对无穷远平面的单应性矩阵(The infinite homography)的约束方程并 据此提出了一种新的摄象机线性自标定算法.与文献中已有的方法相比,该方法对摄象机的运 动要求不苛刻(如不要求摄象机的运动为正交运动),只须摄象机作一次平移运动和两次任意刚 体运动,就可线性唯一确定内参数.该方法主要优点在于:在确定无穷远平面的单应性矩阵的过 程中,不需要射影重构,也不需要有限远平面信息,唯一所需要的信息是图象极点,从而简化了 文献中现有的算法.另外同时给出了由极点确定(运动组)关于无穷远平面单应性矩阵的充分必 要条件.模拟实验和实际图象实验验证了该方法的正确性和可行性.     

Autocalibration of a projector-camera system

This paper presents a method for calibrating a projector-camera system that consists of multiple projectors (or multiple poses of a single projector), a camera, and a planar screen. We consider the problem of estimating the homography between the screen and the image plane of the camera or the screen-camera homography, in the case where there is no prior knowledge regarding the screen surface that enables the direct computation of the homography. It is assumed that the pose of each projector is unknown while its internal geometry is known. Subsequently, it is shown that the screen-camera homography can be determined from only the images projected by the projectors and then obtained by the camera, up to a transformation with four degrees of freedom. This transformation corresponds to arbitrariness in choosing a two-dimensional coordinate system on the screen surface and when this coordinate system is chosen in some manner, the screen-camera homography as well as the unknown poses of the projectors can be uniquely determined. A noniterative algorithm is presented, which computes the homography from three or more images. Several experimental results on synthetic as well as real images are shown to demonstrate the effectiveness of the method.     

Self-calibration with varying focal length from two images obtained by a stereo head

The self-calibration approach based on the absolute conic or its dual, the absolute dual quadric, has the merit of allowing the intrinsic camera parameters to vary in image sequence. In this paper, we show that certain linear equations resulting from the infinity homography can be added to a system of originally undetermined linear equations to find the absolute dual quadric for a stereo head. The special stereo configurations considered here allow one camera to rotate around either one or two of the coordinate axes defined by another camera. Experiments with synthetic images show that satisfactory results can be obtained with only the proposed linear methods. For real images obtained with non-ideal stereo configurations, and possibly with measurement noises, results obtained from the proposed linear methods may serve as reasonable initial guesses for some non-linear optimization procedure.     

多模态硬币图像单应性矩阵预测

对不同成像条件下拍摄的硬币图像进行配准是硬币表面缺陷算法的前置任务。然而,基于互信息的传统多模态配准方法速度慢、精度低,现有的通过基于深度学习的单应性矩阵预测方法实现的图像配准只适用于单模态的任务。为此,提出一种基于深度学习的适用于多模态硬币图像的单应性矩阵预测方法,进而使用预测的单应性矩阵完成图像配准。首先,使用单应性矩阵预测层预测输入图像对间的单应性矩阵,使用单应性矩阵对待配准图像进行透视变换;然后,使用图像转换层将透视变换后的待配准图像和目标图像映射到同一域,图像转换层可在推理时去除从而减少推理时间;最后,计算同一域下的图像间的损失,并进行训练。实验表明,该方法在测试集上的平均距离误差为 3.417 像素,相较于基于互信息的传统多模态配准方法 5.575 像素的平均距离误差降低 38.71%。且配准单对图像耗时为 17.74 ms,远低于基于互信息的传统多模态配准方法的 6 368.49 ms。     

Metric sensing and control of a quadrotor using a homography-based visual inertial fusion method

The combination of a camera and an Inertial Measurement Unit (IMU) has received much attention for state estimation of Micro Aerial Vehicles (MAVs). In contrast to many map based solutions, this paper focuses on optic flow (OF) based approaches which are much more computationally efficient. The robustness of a popular OF algorithm is improved using a transformed binary image from the intensity image. Aided by the on-board IMU, a homography model is developed in which it is proposed to directly obtain the speed up to an unknown scale factor (the ratio of speed to distance) from the homography matrix without performing Singular Value Decomposition (SVD) afterwards. The RANSAC algorithm is employed for outlier detection. Real images and IMU data recorded from our quadrotor platform show the superiority of the proposed method over traditional approaches that decompose the homography matrix for motion estimation, especially over poorly-textured scenes. Visual outputs are then fused with the inertial measurements using an Extended Kalman Filter (EKF) to estimate metric speed, distance to the scene and also acceleration biases. Flight experiments prove the visual inertial fusion approach is adequate for the closed-loop control of a MAV.     

Line Geometry and Camera Autocalibration

We provide a completely new rigorous matrix formulation of the absolute quadratic complex (AQC), given by the set of lines intersecting the absolute conic. The new results include closed-form expressions for the camera intrinsic parameters in terms of the AQC, an algorithm to obtain the dual absolute quadric from the AQC using straightforward matrix operations, and an equally direct computation of a Euclidean-upgrading homography from the AQC. We also completely characterize the 6×6 matrices acting on lines which are induced by a spatial homography. Several algorithmic possibilities arising from the AQC are systematically explored and analyzed in terms of efficiency and computational cost. Experiments include 3D reconstruction from real images. This work has been partly supported by the Spanish Administration agencies CDTI and CICYT under projects CENIT-VISION 2007-1007 and TEC2007-67764 respectively.     

基于多尺度残差网络的单应估计方法

单应估计是许多计算机视觉任务中的一个基础且重要的步骤。传统单应估计方法基于特征点匹配,难以在弱纹理图像中工作。深度学习已经应用于单应估计以提高其鲁棒性,但现有方法均未考虑到由于物体尺度差异导致的多尺度问题,所以精度受限。针对上述问题,提出了一种用于单应估计的多尺度残差网络。该网络能够提取图像的多尺度特征信息,并使用多尺度特征融合模块对特征进行有效融合,此外还通过估计四角点归一化偏移进一步降低了网络优化难度。实验表明,在MS-COCO数据集上,该方法平均角点误差仅为0.788个像素,达到了亚像素级的精度,并且在99%情况下能够保持较高的精度。由于综合利用了多尺度特征信息且更容易优化,该方法精度显著提高,并具有更强的鲁棒性。     

局部特征描述改进的LK跟踪注册方法

针对KLT跟踪方法抗光照变化和抗遮挡较差的问题,提出一种使用局部特征描述改进的LK跟踪注册方法(DF-LK)。使用ORB特征点求解初始位姿,通过自适应非极大值抑制重新划分特征点,选择均匀分布的特征点作为LK方法跟踪的控制点集。相邻帧图像之间的单应性矩阵通过在DF描述后的图像上使用LK方法进行求解,跟踪的结果由向前向后错误检测进行评估,由单应性矩阵和初始位姿求解出当前帧的摄像机位姿,并叠加虚拟信息。实验结果表明,该方法在光照变化、部分遮挡和透视变化时均有较好的稳定性和鲁棒性。