Robust multi-view correspondence of noisy 2D points using relaxation

Abstract

An efficient ghost removal algorithm for reliably extracting correspondence information for 2D points from several views using epipolar constraints is proposed. The point matching relations are modelled using a weighted k-partite graph and a globally convergent iterative algorithm is used to extract reliable maximum cliques from the graph using probabilistic relaxation labelling. Experimental results show the effectiveness of the proposed method.