Conceptual space based model fitting for multi-structure data

In this paper, we propose a novel fitting method, called the Conceptual Space based Model Fitting (CSMF), to fit and segment multi-structure data contaminated with a large number of outliers. CSMF includes two main parts: an outlier removal algorithm and a model selection algorithm. Specifically, we firstly construct a novel conceptual space to measure data points by only considering the good model hypotheses. Then we analyze the conceptual space to effectively remove the gross outliers. Based on the results of outlier removal, we propose to search center points (representing the estimated model instances) in the conceptual space for model selection. CSMF is able to efficiently and effectively remove gross outliers in data, and simultaneously estimate the number and the parameters of model instances without using prior information. Experimental results on both synthetic data and real images demonstrate the advantages of the proposed method over several state-of-the-art fitting methods.