Pixels Classification With Neurocomputer

Abstract

This paper developes a new method for the pixel classification of an intensity image. A neural networks of non -linear analog neurons have been shown extremely effective. This problem is considered as an optimally classification of an image based on their original activation. Optimization is defined in terms of energy which is a function of neurons the output values which vary continuously. The neurons are modelled as amplifiers which have sigmoid monotonic input -output relations. A synapse between two neurons is defined by a conductance which connects the output of neuron to the input of another neuron. The net input current to any neuron is the sum of the currents flowing through the set of resistors connecting its input to the outputs of the other neurons. We have formulate the problems in terms of desired optima, subject to certain constraints. ABSTRACTThis paper developes a new method for the pixel classification of an intensity image. A neural networks of non linear analog neurons have been shown extremely effective. This problem is considered as an optimally classification of an image based on their original activation. Optimization is defined in terms of energy which is a function of neurons the output values which vary continuously. The neurons are modelled as amplifiers which have sigmoid monotonic input output relations. A synapse between two neurons is defined by a conductance which connects the output of neuron to the input of another neuron. The net input current to any neuron is the sum of the currents flowing through the set of resistors connecting its input to the outputs of the other neurons. We have formulate the problems in terms of desired optima, subject to certain constraints. 1J J where R is a constant, I. is the external input current and V. = g(u.) is the sigmoid monotonic transfer function.