Pulse coupled neural network based image classification

The objective of the project is to study the feature extraction and dynamic properties of the pulse coupled neural network (PCNN) and determine its potential for classification of images. The pulse coupled neurons are significantly different from conventional artificial neurons as they intend to model the essence of the understanding of image interpretation process in biological neural system. The basis for PCNN is the linking field of Eckhorn, Reitboeck, Arndt and Dicke (1989). The model produces synchronous bursts of pulses from inputs/neurons with similar activity, effectively grouping them by phase and pulse frequency. It gives a basic new function: grouping by similarity. PCNN generates an object-specific time signal (referred to as an icon) that can be used as an object signature for object recognition. The signal detected may be made invariant to translation, scale, rotation, distortion, and intensity. The time signals generated by the PCNN were given as input to the classification network. The network recognized and classified the time signals with 90% accuracy