Analysis of three-dimensional movement using Fourier descriptors

Abstract

Recent improvements in Fourier descriptor (FD) shape analysis enable rapid identification of three-dimensional objects using FD feature vectors derived from their boundaries. In three-dimensional shape analysis, it is essential to preserve all information to achieve good performance. In the real-time situation it is, of course, equally important to use a computationally efficient method. The method of three-dimensional shape analysis using normalized Fourier descriptors is information preserving, yet is as fast as previous suboptimum methods. In addition, the feature vector has a linear property, allowing to interpolate between library projections and effectively define a continuum of library projections rather than a finite set. This method is applied to the analysis of sequential data varying in resolution and orientation relative to the camera. Computational considerations are discussed, and it is seen that real-time implementation of the method is feasible.