共查询到16条相似文献,搜索用时 150 毫秒
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摄像机自标定的线性理论与算法 总被引:14,自引:2,他引:12
文中提出一种新的摄像机线性自标定的算法和理论。与文献中已有的方法相比,该文方法的主要优点是对摄像机的运动要求不苛刻,如不要求摄像机的运动为正交运动。该方法的关键步骤是确定无穷远平面的单应性矩阵(Homography)。文中从理论上严格证明了下述结论:摄像机作两组运动参数未知的运动M1={(R1,t^11),(R1,t^12)},M2={(R2,t^21),(R2,t^22)},若下述两个条件满足:(1)T1={t^11,t^12},T2={t^21,t^22}是两个线性无关组(即本组内的两个平移向量线性无关);(2)R1,R2的旋转轴不同,则可线性地唯一确定摄像机的内参矩阵和运动参数。另外,在四参数摄像机模型下,严格证明了一组运动可线性地唯一确定摄像机的内参数矩阵和运动参数。模拟实验和实际图像实验验证了本文方法的正确性和可行性。 相似文献
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基于单平面模板的摄像机定标研究 总被引:2,自引:0,他引:2
提出了一种摄像机定标方法,只需要摄像机从不同方向拍摄平面模板的多幅图像,摄像机与平面模板间可以自由地移动,运动的参数无需已知。对于每个视点获得图像,提取图像上的网格角点;平面模板与图像间的网格角点对应关系,确定了单应性矩阵;对每幅图像,就可确定一个单应性矩阵,这样就能够进行摄像机定标。该算法先有一个线性解法,然后基于极大似然准则对线性结果进行非线性优化求精。该方法同时也考虑了镜头畸变的影响。实验结果表明该算法简单易用。 相似文献
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由平行平面的投影确定无穷远平面的单应矩阵 总被引:1,自引:0,他引:1
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. 相似文献
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一种新的线性摄像机自标定方法 总被引:21,自引:2,他引:19
提出了一种新的基于主动视觉系统的线性摄像机自定标方法。所谓基于主动视觉系统,是指摄像机固定在摄像机平台上以平摄像机平台的运动可以精确控制。该方法的主要特点是可以线性求解摄像机的所有5个内参数。据作者所知。文献中现有的方法仅能线性求解摄像机的4个由参数。当摄像机为完全的射影模型时,即当有畸变因子(skew factor)存在时,文献中的线性方法均不再适用。该方法的基本思想是控制摄像机做5组平面正交运动,利用图像中的极点(epipoles)信息来线性标定摄像机。同时,针对摄像机做平移运动时基本矩阵的特殊形式,该文提出了求基本矩阵(fundamental matrix)的2点算法。与8点算法相比较,2点算法大大提高了所求极点的精度和鲁棒性。另外,该文对临近奇异状态(即5组平面正交运动中,有两组或者多组运动平面平行)作了较为详尽的分析,并提出了解决临近奇异状态的策略,从而增强了该文算法的衫性。模拟图像和真实图像实验表明该文的自标定方法具有较高的鲁棒性和准确性。 相似文献
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基于随机抽样一致性的多平面区域检测算法 总被引:1,自引:0,他引:1
在随机抽样一致性(RANSAC)的基础上,提出了一种对多个平面区域同时进行检测的算法.该算法假设对同一场景的一对未定标图像已经进行了特征点提取和匹配,首先利用对极几何约束计算出一对极点,然后随机抽取多组3对而非4对特征点定义多个待确定单应性矩阵模型,对图像对中的多个平面区域同时进行检测.模拟实验和真实实验都证明该算法具有运算量小、准确性高、鲁棒性好等优点. 相似文献
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基于正交平面的摄像机自定标 总被引:1,自引:0,他引:1
提出了一种基于正交平面摄像机定标的新算法。它利用场景中的正交平面,摄像机作五次以上的平移运动,根据每次运动关于平面的单应矩阵建立内参数的线性约束方程组,从而线性地确定内参数。与以往的定标方法相比,文章对摄像机的运动不苛刻,只需控制摄像机作平移运动,这在一般的实验平台上可以很容易地实现,并且线性地确定摄像机所有的五个内参数。模拟实验和真实图象实验表明,文章给出的方法在机器人视觉中具有一定的实用价值。 相似文献
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利用直线对应计算纯旋转运动参数的一种线性方法 总被引:4,自引:0,他引:4
从单镜头序列图象确定运动刚体的3维运动参数是计算机视觉中一个重要的问题.本文提出了一个利用直线对应计算纯旋转运动参数的线性方法.在该算法中,仅用图象中直线的两个不变量.假设两帧图象中已经抽取和匹配出4对以上的对应直线,则可以唯一地确定旋转运动参数,该算法适用予旋转轴过投影中心的情况.本文同时给出了实验结果. 相似文献
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研究了由多幅图像恢复摄像机矩阵和空间物体三维几何形状这一多视图三维重构问题,改进了由Hartley和Rother等人分别给出的基于由无穷远平面诱导的单应进行射影重构的算法,提出了一种新的线性算法,它仅需要空间中3个点在每幅图像上均可见。因为空间中不在同一直线上的3个点恰好确定一个平面,所以它避免了Hartley和Rother等方法中需要确定空间4个点是否共面这一比较棘手的问题。大量实验结果表明,这种方法快速、准确且受噪声影响小。 相似文献
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This paper proposes a new method for self-calibrating a set of stationary non-rotating zooming cameras. This is a realistic configuration, usually encountered in surveillance systems, in which each zooming camera is physically attached to a static structure (wall, ceiling, robot, or tripod). In particular, a linear, yet effective method to recover the affine structure of the observed scene from two or more such stationary zooming cameras is presented. The proposed method solely relies on point correspondences across images and no knowledge about the scene is required. Our method exploits the mostly translational displacement of the so-called principal plane of each zooming camera to estimate the location of the plane at infinity. The principal plane of a camera, at any given setting of its zoom, is encoded in its corresponding perspective projection matrix from which it can be easily extracted. As a displacement of the principal plane of a camera under the effect of zooming allows the identification of a pair of parallel planes, each zooming camera can be used to locate a line on the plane at infinity. Hence, two or more such zooming cameras in general positions allow the obtainment of an estimate of the plane at infinity making it possible, under the assumption of zero-skew and/or known aspect ratio, to linearly calculate the camera's parameters. Finally, the parameters of the camera and the coordinates of the plane at infinity are refined through a nonlinear least-squares optimization procedure. The results of our extensive experiments using both simulated and real data are also reported in this paper. 相似文献
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Zeinik-Manor L. Irani M. 《IEEE transactions on pattern analysis and machine intelligence》2002,24(2):214-223
The image motion of a planar surface between two camera views is captured by a homography (a 2D projective transformation). The homography depends on the intrinsic and extrinsic camera parameters, as well as on the 3D plane parameters. While camera parameters vary across different views, the plane geometry remains the same. Based on this fact, we derive linear subspace constraints on the relative homographies of multiple (⩾ 2) planes across multiple views. The paper has three main contributions: 1) We show that the collection of all relative homographies (homologies) of a pair of planes across multiple views, spans a 4-dimensional linear subspace. 2) We show how this constraint can be extended to the case of multiple planes across multiple views. 3) We show that, for some restricted cases of camera motion, linear subspace constraints apply also to the set of homographies of a single plane across multiple views. All the results derived are true for uncalibrated cameras. The possible utility of these multiview constraints for improving homography estimation and for detecting nonrigid motions are also discussed 相似文献
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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. 相似文献
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