Chen Haifeng, Meer Peter
IEEE Trans Syst Man Cybern B Cybern. 2005 Jun;35(3):578-86. doi: 10.1109/tsmcb.2005.846659.
A technique is presented to combine n data points, each available with point-dependent uncertainty, when only a subset of these points come from N < n sources, where N is unknown. We detect the significant modes of the underlying multivariate probability distribution using a generalization of the nonparametric mean shift procedure. The number of detected modes automatically defines N, while the belonging of a point to the basin of attraction of a mode provides the fusion rule. The robust data fusion algorithm was successfully applied to two computer vision problems: estimating the multiple affine transformations, and range image segmentation.
本文提出了一种技术,用于在仅部分数据点来自N < n个源(N未知)且每个数据点都有与点相关的不确定性的情况下,合并n个数据点。我们使用非参数均值漂移过程的推广来检测潜在多元概率分布的显著模式。检测到的模式数量自动定义N,而一个点属于某个模式吸引域则提供了融合规则。该鲁棒数据融合算法已成功应用于两个计算机视觉问题:估计多个仿射变换和距离图像分割。