%0 Journal Article %1 Xiao2016748 %A Xiao, Guobao %A Wang, Hanzi %A Lai, Taotao %A Suter, David %D 2016 %J Pattern Recognition %K Geometric Hypergraph fitting, model modelling, partition %P 748 - 760 %R https://doi.org/10.1016/j.patcog.2016.06.026 %T Hypergraph modelling for geometric model fitting %U http://www.sciencedirect.com/science/article/pii/S0031320316301431 %V 60 %X Abstract 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.

@article{Xiao2016748, abstract = {Abstract 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. }, added-at = {2022-03-14T06:22:26.000+0100}, author = {Xiao, Guobao and Wang, Hanzi and Lai, Taotao and Suter, David}, biburl = {https://www.bibsonomy.org/bibtex/296efdc74ea003b7e7a33ad7580ae53fc/dsuter}, doi = {https://doi.org/10.1016/j.patcog.2016.06.026}, interhash = {643dee18697be7bdfa46eb221c3a6093}, intrahash = {96efdc74ea003b7e7a33ad7580ae53fc}, issn = {0031-3203}, journal = {Pattern Recognition}, keywords = {Geometric Hypergraph fitting, model modelling, partition}, pages = {748 - 760}, timestamp = {2022-03-14T06:22:26.000+0100}, title = {Hypergraph modelling for geometric model fitting}, url = {http://www.sciencedirect.com/science/article/pii/S0031320316301431}, volume = 60, year = 2016 }