Contour grouping with shape manifold and distance transform

Object detection in clutter or occlusion is a hard problem in computer vision. We propose an object detection method based on contour grouping. Two stages are included: a novel distance transform is applied to match templates to the test image so that candidates and locations of the object are obtained; verification using shape manifold is performed to preclude outliers and identify the prior. We use the prior combined with bottom-up edge information to produce the final grouping result. Our contribution lies in two aspects: one is the novel distance transform saves much searching space; the other is introducing shape manifold in verifying candidates of grouping. Experiments show our method achieves considerable accuracy in occlusion and background clutter. Specially, the only feature used is edge and contour rather than combination of multi features.