几何模型拟合的超图建模
Hypergraph Modelling for Geometric Model Fitting
摘要：
在本文中，我们提出了一种新的超图为基础的方法（称为HF），以适应和分割的多结构数据。建议HF制定的几何模型拟合问题作为一个超图划分问题的基础上一个新的超图模型。在超图模型中，顶点表示数据点，超边表示模型假设。超图的超边数大且“数据决定”，可以表达模型假设与数据点之间的复杂关系。此外，我们开发了一个强大的超图划分算法来检测模型拟合的子超图。HF可以有效地和高效地估计的数量，和参数，模型实例在多结构数据严重损坏与离群值同时。在合成数据和真实图像上的实验结果表明，该方法优于以往的方法。
英文摘要：
In this paper, we propose a novel hypergraph based method (called HF) to fit and segment multi-structural data. The proposed HF formulates the geometric model fitting problem as a hypergraph partition problem based on a novel hypergraph model. In the hypergraph model, vertices represent data points and hyperedges denote model hypotheses. The hypergraph, with large and "data-determined" degrees of hyperedges, can express the complex relationships between model hypotheses and data points. In addition, we develop a robust hypergraph partition algorithm to detect sub-hypergraphs for model fitting. HF can effectively and efficiently estimate the number of, and the parameters of, model instances in multi-structural data heavily corrupted with outliers simultaneously. Experimental results show the advantages of the proposed method over previous methods on both synthetic data and real images.
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