Local part chamfer matching for shape-based object detection

Abstract

Chamfer matching is one of the elegant and powerful tools for shape-based detection in cluttered images. However, the chamfer matching methods, including oriented chamfer matching (OCM) and directional chamfer matching (DCM), tend to produce bad detections due to deformation of object shapes and cluttering in the scene. To improve detection accuracy of these chamfer matching methods, we propose local part oriented chamfer matching (LPOCM) and local part directional chamfer matching (LPDCM). First, shape templates and discriminative contour fragments are learned, and then a shape representation is built using a Markov random field (MRF). Finally, the template detection in an input image is formulated as an inference in the MRF. Experimental results for benchmark datasets including ETHZ Shape Classes, INRIA Horses and Weizmann Horses clearly demonstrate that the proposed LPOCM and LPDCM significantly improve the detection accuracy of OCM and DCM without sacrificing much time efficiency.