利用位能場做三維物體之形狀比對與辨識

Abstract

The goal of this thesis is to investigate the shape matching and recognition of 3D objects using artificial potential fields. The potential-based approach recognizes the shape of a 3D object by identifying the best match from a selected group of template objects. The proposed model assumes that boundary of every 3D template object is uniformly charged. An initially small input object, represented by its boundary samples, placed inside a template object will experience the repulsive force and torque arising from the potential field. A better match in the 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 input object with the largest final size corresponds to the best match and represents the shape of the giveninput object. The potential and the associated repulsive force and torque between the input object and the template object are analytically tractable. The proposed approach is intrinsically invariant under translation, rotation and size changes of the input object.