<title>Fourier-Transform Feature-Space Studies</title>

Abstract

A hierarchial multi-level feature-space pattern recognition system is described. Multi-class distortion-invariant object identification is the purpose of this study. Attention is given to dimensionality reduction (to simplify computations) and to the use of non-unitary transformations (to achieve discrimination). A Fourier transform feature space is used. However, our basic hierarchial concepts, our theoretical analysis, and our general conclusions are applicable to other feature spaces. The use of intensity versus phase features is studied and the performance of our system in the presence of noise is studied. Quantitative experimental data on 2 two-class pattern recognition databases are provided.