Invariant pattern recognition using neural networks combined with optical wavelet preprocessor

A novel pattern-recognition system that is invariant against scale-, positi on- 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. Th e wavelet decomposition of two-dimensional patterns is optically realized b y the VanderLugt correlator. To obtain the multiwavelet transforms simultan eously, 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 invarian t recognition of the system.