But the bundle adjustment needs a good initialization and extremely expensive computation,and the factorization method is limited by the restriction that all 3D points must be visible in all views recently, a linear algorithm of projective reconstruction based on the homography.
研究了由带有视差的图像对恢复摄像机矩阵和空间物体的三维几何形状这一多视图三维重构问题,结合Hartley和Rorther等人提出的基于无穷远平面诱导的单应进行射影重构算法,提出了根据图像间的对应关系确定插补图像像素的位置和灰度的新算法,通过这种新算法输出符合双眼特性的体视图像,试验证明插补后体视图像效果较好,速度快。
An algorithm for images matching was proposed,which used both epipolar and homography constraints.
提出一种综合应用极几何和单应约束的图像特征点匹配算法,首先使用互相关法对图像特征点集进行初始匹配,然后运用RANSAC方法鲁棒地估计基本矩阵和单应矩阵并相应地剔除错误匹配点,最后利用优化后的基本矩阵和单应矩阵引导匹配以获得更多、更精确的匹配点。
It is therefore concluded that a projection matrix can be computed through homography under certain condition.
通过这个原理就可以推导出投影矩阵在满足一定条件下可由单应矩阵得到。
There are geometrical deformations,named plane homography,between corresponding feature windows on different images of the same patch on scene surface.
不同视点图像中相应特征点邻域窗口之间存在几何上的透视畸变,这可用平面单应映射来表示,而目前大多匹配算法将该映射用仿射变换模型来近似,即用具有仿射不变性的特征进行图像的匹配。
There are geometrical deformations between corresponding feature windows on different images of the same scene sur- face,which can be represented by a homography on 2D plane.
不同视点图像中相应特征点邻域窗口之间存在几何上的透视畸变,这可以用平面单应映射来表示,而目前大多特征匹配算法将该映射用仿射变换模型来近似,即用具有仿射不变性的特征进行图像的匹配。
The homography between corresponding matching windows can be approximated by an affine model.
两幅图像中相应特征点邻域窗口之间的单应映射可以用仿射变换模型来近似。
Line-based Homography Estimation and Its Application in Visual Metrology;
基于线对应的单应矩阵估计及其在视觉测量中的应用
It has been proved in the literature that the homography from the x-y plane of the world coordinate system to the image plane can give rise two linear constraints on the camera s intrinsic parameters.
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。
As a result,homography may be computed from point correspondences between space points and image points.
在该方法中,首先利用Canny算子检测出空间平面和平面模板的图像中直线和椭圆的特征;然后运用最小二乘法拟合出直线和椭圆的方程,从而可以得到直线与椭圆的切点;根据射影几何中直线和点的结合性保持不变的性质,得到空间平面上的点与图像特征点间的对应,从而估计出空间平面与图像平面之间的单应矩阵;最后,由单应矩阵实现了对空间平面上任意两点距离的测量。
Deriving homography matrix of planar projective transform;
二维投影变换模型的单应矩阵表示
As same as other problems in computer vision, the homography matrix computation is very sensitive to noise.
在平面测量问题中,单应矩阵扮演着十分重要的角色。
Homography matrix between virtual and real coordinates of any frame can be obtained after the homography matrix between virtual and real coordinates is ca.
在此基础之上,研究了基于平面结构的虚实结合方法;通过计算表示虚实坐标之间关系的单应矩阵,可以得到任意一帧虚实坐标之间的单应矩阵,实现了虚实结合和虚拟物体的动态跟踪。
Current registration matrix can be calculated using homography between current and previous frame when marker is occluded.
首先利用已知尺寸的标识获取待注册平面的初始三维注册矩阵,在标识被遮挡情况下利用当前帧与前一帧图像间的单应性关系恢复出当前三维注册矩阵。
Our method is to calibrate the epipolar constrains and calculate the offset of two image along with the constraints of homography.
此方法先使用极几何校正和单应性约束求取图像偏差,然后采用快速匹配算法在局部范围内得到匹配点,充分满足了系统对实时性和精确性的要求。
The proof of Sherman-Morrison theorem based on the homography induced by the ocene plane;
基于场景平面诱导单应对Sherman-Morrison公式的证明