Paper Presentation On IMAGE PROCESSING For CSE & IT

A novel method for image processing and pattern recognition using Discrete Fourier Transformations on the global pulse signal of a pulse-coupled neural network (PCNN) is presented in this paper. The PCNN is an image transform that removes “unimportant” details while improving the overall quality of the image. It may also provides substantial noise smoothing without losing image pattern and shape information. We describe the mathematical model of the PCNN and an original way of analyzing the pulse of the network in order to achieve better quality of the image ,scale and translation-independent recognition for isolated objects. During the last few years there was a shift of the emphasis in the artificial neural network community toward spiking or pulse-coupled neural networks. Motivated by biological discoveries, many studies consider pulse coupled neural networks with spike-timing as an essential component in information processing by the brain. Pulse-coupled neural networks (PCNN) were introduced as a simple model for the cortical neurons in the visual area of the cat's brain.These neural models are proposed by Eckhorn and Johnson.