<title>Feature Extractors For Distortion-Invariant Robot Vision</title>

Abstract

Various feature extractors/classifiers for a hierarchical feature-space pattern recognition system are described. The system is intended to achieve multiclass distortion-invariant object identification. Although only a Fourier transform feature space is used, our basic hierarchical concepts, our theoretical analysis, and our general conclusions are applicable to other feature spaces. The performance using intensity and phase Fourier transform features and the performance in the presence of noise are studied and quantified for two different two-class pattern recognition data bases.