Department of Statistics, Columbia University, New York, NY 10027, USA.
IEEE Trans Pattern Anal Mach Intell. 2012 Dec;34(12):2351-64. doi: 10.1109/TPAMI.2012.52.
We propose a novel robust estimation algorithm—the generalized projection-based M-estimator (gpbM), which does not require the user to specify any scale parameters. The algorithm is general and can handle heteroscedastic data with multiple linear constraints for single and multicarrier problems. The gpbM has three distinct stages—scale estimation, robust model estimation, and inlier/outlier dichotomy. In contrast, in its predecessor pbM, each model hypotheses was associated with a different scale estimate. For data containing multiple inlier structures with generally different noise covariances, the estimator iteratively determines one structure at a time. The model estimation can be further optimized by using Grassmann manifold theory. We present several homoscedastic and heteroscedastic synthetic and real-world computer vision problems with single and multiple carriers.
我们提出了一种新颖的稳健估计算法——广义基于投影的 M 估计器（gpbM），它不需要用户指定任何比例参数。该算法具有通用性，可以处理具有多个线性约束的异方差数据，适用于单载波和多载波问题。gpbM 有三个不同的阶段——尺度估计、稳健模型估计和内/外点二分法。相比之下，在其前身 pbM 中，每个模型假设都与不同的尺度估计相关联。对于包含多个具有一般不同噪声协方差的内点结构的数据，该估计器可以一次迭代确定一个结构。通过使用 Grassmann 流形理论，可以进一步优化模型估计。我们提出了几个单载波和多载波的同方差和异方差合成和真实世界计算机视觉问题。
