Hypergraph Modelling for Geometric Model Fitting
This addresses geometric model fitting in computer vision, offering a novel approach for handling outliers in multi-structural data.
The authors tackled the problem of fitting and segmenting multi-structural data with outliers by proposing a hypergraph-based method (HF) that formulates it as a hypergraph partition problem, achieving effective and efficient estimation of model instances in corrupted data.
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.