Pattern recognition using pulse-coupled neural networks and discrete Fourier transforms

Abstract A novel method for pattern recognition using discrete Fourier transforms on the global pulse signal of a pulse-coupled neural network (PCNN) is presented in this paper. We describe the mathematical model of the PCNN and an original way of analyzing the pulse of the network in order to achieve scale- and translation-independent recognition for isolated objects. We also analyze the error as a result of rotation. The system is used for recognizing simple geometric shapes and letters.

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