Image reconstruction from contour data using a back-propogation neural network

This paper describes a three layer neural network for the reconstruction of images from their contour data. To avoid a large number of nodes within the defined neural network, we are applying, recursively, a quadrature segmentation of the input contour map image to obtain smaller adjacent regions having a number of points less than or equal to a specified maximum. The contour points in the end regions are then used to train a three layer back-propagation neural network to be used for reconstructing an approximation of the original image. It is shown that the neural network has a better performance than other available classical algorithms