A Hierarchical Voting Scheme for Robust Geometric Model Fitting

In this paper, we propose an efficient and robust model fitting method, called Hierarchical Voting scheme based Fitting (HVF), to deal with multiple-structure data. HVF starts from a hierarchical voting scheme, which simultaneously analyses the consensus information of data points and the preference information of model hypotheses. Based on the proposed hierarchical voting scheme, HVF effectively removes “bad” model hypotheses and outliers to improve the efficiency and accuracy of fitting results. Then, HVF introduces a continuous relaxation based clustering algorithm to fit and segment multiple-structure data. The proposed HVF can effectively estimate model instances from the model hypotheses generated by random sampling, which usually includes a large proportion of “bad” model hypotheses. Experimental results show that the proposed HVF method has significant superiority over several state-of-the-art fitting methods on both synthetic data and real images.

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