Three-dimensional shape descriptors and matching procedures

Abstract

Shape descriptors are used to identify objects in the same way that human fingerprints are used to identify people. Features of an object are extracted by applying functions to the digital representation of the object. These features are structured as a vector which is known as the shape descriptor (feature vector) of that object. The objective when constructing a shape descriptor is to find functions that will yield shape descriptors that can be used to uniquely identify or at least classify an object. A measure of similarity is required to identify or classify an object. The similarity between two objects is computed by applying a distance function to the shape descriptors of the two objects. The objective of this paper is to examine two of the possible techniques in three-dimensional shape descriptor construction based on Fourier analysis, and to find a descriptor that is able to not only classify, but also identify objects.