Shape matching and recognition using a physically based object model

We present a novel approach of shape matching and recognition of 3D objects using artificial potential fields. The potential model assumes that boundary of every 3D template object of identical volume is uniformly charged. An initially small input object placed inside a template object will experience repulsive force and torque arising from the potential field. A better match in shape between the template object and the input object can be obtained if the input object translates and reorients itself to reduce the potential while growing in size. The template object which allows the maximum growth of the input object corresponds to the best match and thus represents the shape of the input object. The above repulsive force and torque are analytically tractable for an input object represented by its boundary samples, which makes the shape matching efficient. The proposed approach is intrinsically invariant under translation, rotation and size changes of the input object.