Shape is one of the useful information for object detection. The human visual system can often recognize objects based on the 2-D outline shape alone. In this paper, we address the challenging problem of shape matching in the presence of complex background clutter and occlusion. To this end, we propose a graph-based approach for shape matching. Unlike prior methods which measure the shape similarity without considering the relation among edge pixels, our approach uses
the connectivity of edge pixels by generating a graph. A group of connected edge pixels, which is represented by an ”edge” of the graph, is considered together and their similarity cost is defined for the ”edge” weight by explicit comparison with the
corresponding template part. This approach provides the key advantage of reducing ambiguity even in the presence of background clutter and occlusion. The optimization is performed by means of a graph-based dynamic algorithm. The robustness of our method is demonstrated for several examples including long video sequences. Finally, we applied our algorithm to our grasping robot system by providing the object information in the form of prompt hand-drawn templates.