A novel robust model fitting approach towards multiple-structure data segmentation

We propose a novel and effective robust model fitting approach based on the Structure Decision Graph (SDG) to segment multiple-structure data in the presence of outliers. The proposed approach is motivated by the observations that each structure can be characterized by one representative hypothesis, called as the Structure Prototype (SP), and the SPs have relatively large distances among them. In this paper, instead of analyzing each hypothesis individually, the residuals over all the hypotheses are used to explicitly construct an SDG, where a sorted weight score set and a minimum arrived distance set are respectively computed. Based on the SDG, the SPs corresponding to different structures can be easily determined. Compared with conventional robust model fitting approaches, one distinguishing characteristic of our approach is that the clustering procedure is not required. Therefore, the proposed approach is less disturbed by noises and outliers, and is relatively easy to implement. Experimental results on synthetic data and real-world image datasets demonstrate the superiority of the proposed approach over the state-of-the-art robust model fitting approaches for multiple-structure data segmentation.