Boundary extraction based on stack filter, Hopfield neural network and self-organization neural network

In this paper, we propose a new method to extract boundary based on the combination of stack filter, Hopfield neural network and self-organization neural network in order to get all the advantages of the three methods. Compared to the boundary extraction method based on Hopfield neural network, the new method has a stronger ability to resist mixed-distributed noises and the result from boundary test is much better. The speed of optimal training on the stack filter is improved greatly and memory needed is decreased dramatically compared to that based on stack filter.