论文标题
多结构几何模型拟合的超图优化
Hypergraph Optimization for Multi-structural Geometric Model Fitting
论文作者
论文摘要
最近，已经提出了一些基于超图的方法来处理计算机视觉中的模型拟合问题，这主要是由于超图具有卓越的能力来代表数据点之间的复杂关系。但是，当输入数据包含大量数据点（通常被噪音和异常值污染）时，超图就变得非常复杂，这将大大增加计算负担。为了克服上述问题，我们提出了一种新型的基于HyperGraph优化的模型拟合（HOMF）方法来构建简单但有效的超图。具体而言，HOMF包括两个主要部分：用于顶点优化的自适应嵌入式估计算法和用于HyperEdge优化的迭代性HyperEdge优化算法。所提出的方法高效，并且可以在一些迭代中获得准确的模型拟合结果。此外，HOMF然后可以直接应用光谱聚类，以实现良好的拟合性能。广泛的实验结果表明，HOMF在合成数据和真实图像上的表现都优于几种最先进的模型拟合方法，尤其是在采样效率和使用严重异常值的数据方面。
Recently, some hypergraph-based methods have been proposed to deal with the problem of model fitting in computer vision, mainly due to the superior capability of hypergraph to represent the complex relationship between data points. However, a hypergraph becomes extremely complicated when the input data include a large number of data points (usually contaminated with noises and outliers), which will significantly increase the computational burden. In order to overcome the above problem, we propose a novel hypergraph optimization based model fitting (HOMF) method to construct a simple but effective hypergraph. Specifically, HOMF includes two main parts: an adaptive inlier estimation algorithm for vertex optimization and an iterative hyperedge optimization algorithm for hyperedge optimization. The proposed method is highly efficient, and it can obtain accurate model fitting results within a few iterations. Moreover, HOMF can then directly apply spectral clustering, to achieve good fitting performance. Extensive experimental results show that HOMF outperforms several state-of-the-art model fitting methods on both synthetic data and real images, especially in sampling efficiency and in handling data with severe outliers.
