Fan Shape Model for Object Detection

Abstract

We propose a novel shape model for object detection called Fan Shape Model (FSM). We model contour sample points as rays of final length emanating for a reference point. As in folding fan, its slats, which we call rays, are very flexible. This flexibility allows FSM to tolerate large shape variance. However, the order and the adjacency relation of the slats stay invariant during fan deformation, since the slats are connected with a thin fabric. In analogy, we enforce the order and adjacency relation of the rays to stay invariant during the deformation. Therefore, FSM preserves discriminative power while allowing for a substantial shape deformation. FSM allows also for precise scale estimation during object detection. Thus, there is not need to scale the shape model or image in order to perform object detection. Another advantage of FSM is the fact that it can be applied directly to edge images, since it does not require any linking of edge pixels to edge fragments (contours).