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Hypergraph modelling for geometric model fitting
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DOI：10.1016/j.patcog.2016.06.026.png)
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Abstract
En 中文
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. (C) 2016 Elsevier Ltd. All rights reserved.
Keywords:
Hypergraph modelling
Geometric model fitting
Hypergraph partition
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