Optimal rigid motion estimation and performance evaluation with bootstrap
Optimal rigid motion estimation and performance evaluation with bootstrap
复制标题
使用 bootstrap 进行最佳刚性运动估计和性能评估
DOI：
10.1109/cvpr.1999.786961
复制
发表时间：
1999
期刊：
影响因子：
--
通讯作者：
P. Meer
中科院分区：
文献类型：
--
作者：
B. Matei;P. Meer
在最一般的假设下推导了一种用于3D刚性运动估计的新方法，即测量被非均匀和各向异性破坏，异方差噪声例如，当要从图像对确定校准的立体头部的运动时就是这种情况。四元数空间中的线性化将问题转化为多变量异方差变量误差（HEIV）回归，从中同时获得旋转和平移估计。显着的性能改善示出，为真实的数据，与文献中描述的四元数，子空间和重整化方法的结果进行比较。广泛使用bootstrap，一种来自统计学的高级数值工具，用于估计3D数据点的协方差，并获得旋转和平移估计的置信区域。Bootstrap仅使用作为输入的两个图像对就可以准确地恢复这些信息。
A new method for 3D rigid motion estimation is derived under the most general assumption that the measurements are corrupted by inhomogeneous and anisotropic, i.e., heteroscedastic noise. This is the case, for example, when the motion of a calibrated stereo-head is to be determined from image pairs. Linearization in the quaternion space transforms the problem into a multivariate, heteroscedastic errors-in-variables (HEIV) regression, from which the rotation and translation estimates are obtained simultaneously. The significant performance improvement is illustrated, for real data, by comparison with the results of quaternion, subspace and renormalization based approaches described in the literature. Extensive use as made of bootstrap, an advanced numerical tool from statistics, both to estimate the covariances of the 3D data points and to obtain confidence regions for the rotation and translation estimates. Bootstrap enables an accurate recovery of these information using only the two image pairs serving as input.