论文标题
使用高斯/均匀混合模型的强大分解方法
Robust Factorization Methods Using a Gaussian/Uniform Mixture Model
论文作者
论文摘要
在本文中，我们解决了建立一类强大分解算法的问题，该算法可以解决具有仿射（弱视角）和透视摄像头模型的形状和运动参数。我们引入了高斯/均匀的混合模型及其相关的EM算法。这使我们能够在数据聚类方法中解决强大的参数估计。我们提出了一种强大的技术，该技术适用于任何启用分解方法，并使离群值可靠。此外，我们还展示了如何将这种框架进一步嵌入到迭代的透视分解方案中。我们进行了大量实验，以验证我们的算法并将其与现有算法进行比较。我们还将我们的方法与使用M估计器的分解方法进行了比较。
In this paper we address the problem of building a class of robust factorization algorithms that solve for the shape and motion parameters with both affine (weak perspective) and perspective camera models. We introduce a Gaussian/uniform mixture model and its associated EM algorithm. This allows us to address robust parameter estimation within a data clustering approach. We propose a robust technique that works with any affine factorization method and makes it robust to outliers. In addition, we show how such a framework can be further embedded into an iterative perspective factorization scheme. We carry out a large number of experiments to validate our algorithms and to compare them with existing ones. We also compare our approach with factorization methods that use M-estimators.
