Invariant Pattern Recognition Using Neural Networks Combined with Optical Wavelet Preprocessor

A novel pattern-recognition system that is invariant against scale-, position- and rotation-changes is proposed. The system is composed of an array of modular neural networks with local space-invariant interconnections (FELSI) [Appl. Opt. 29 (1990) 4790] and a multiwavelet transform preprocessor. The wavelet decomposition of two-dimensional patterns is optically realized by the VanderLugt correlator. To obtain the multiwavelet transforms simultaneously, we synthesize a correlation filter of multiwavelets using computer-generated holograms. The learning process of the FELSI with the techniques of additional noise and weight decay is shown to contribute to the invariant recognition of the system.