Unsupervised contour closure algorithm for range image edge-based segmentation

This paper presents an efficient technique for extracting closed contours from range images´ edge points. Edge points are assumed to be given as input to the algorithm (i.e., previously computed by an edge-based range image segmentation technique). The proposed approach consists of three steps. Initially, a partially connected graph is generated from those input points. Then, the minimum spanning tree of that graph is computed. Finally, a postprocessing technique generates a single path through the regions´ boundaries by removing noisy links and closing open contours. The novelty of the proposed approach lies in the fact that, by representing edge points as nodes of a partially connected graph, it reduces the contour closure problem to a minimum spanning tree partitioning problem plus a cost function minimization stage to generate closed contours. Experimental results with synthetic and real range images, together with comparisons with a previous technique, are presented.