Information fusion on the two-layer network for robust estimation of multiple geometric structures

Abstract

This paper proposes a novel and effective model fitting method to estimate multiple geometric structures on a two-layer network, where vertices in the first layer denote model hypotheses and vertices in the second layer denote data points. Instead of only considering the consensus information on model hypotheses or the preference information on data points, we combine these two kinds of information into a two-layer network. Based on this formulation, we first distinguish vertices with the quantities of information they contain in both layers by using an information theoretic algorithm. We then fuse the retained model hypotheses in the first layer together with the generated hypotheses from the retained data points in the second layer. Finally, the proposed method, namely Information Fusion on Two-Layer Network (IFTLN), detects model instances from the fused model hypotheses vertices according to three key elements (i.e., the local maximum value of weighting score, the distance between vertices, and the local density). Overall, IFTLN can not only automatically and simultaneously estimate the number and parameters of model instances with a large number of outliers, but also effectively handle significantly unbalanced distribution of data points among model instances. Comprehensive experiments are performed on both synthetic data and real images, and superior performances are achieved by the proposed method in comparison with some state-of-the-art model fitting methods.