A two-layer Radon transform cellular neural network

A two-layer continuous-time cellular neural network for finding the Radon transform of a binary image is presented. The functionality of this cellular neural network follows from the functionality of the connected component detector cellular neural network.
The Radon transform is a mathematical tool for obtaining a one-dimensional function of intensity values of a binary image in discrete-space. To this end, a double-layer cellular neural network (CNN) for continuous-time operation is introduced. The functionality of the CNN is shown to be dependent on the functionality of the connected component detector CNN.