A practical use of cellular neural networks: the stereo-vision problem as an optimisation

Abstract

A variational way of deriving the relevant parameters of a cellular neural network (CNN) is introduced. The approach exploits the CNN spontaneous internal-energy decrease and is applicable when a given problem can be expressed in terms of an optimisation task. The presented approach is fully mathematical as compared with the typical heuristic search for the correct parameters in the literature on CNNs. This method is practically employed in recovering information on the three-dimensional structure of the environment, through the stereo vision problem. A CNN able to find the conjugate points in a stereogram is fully derived in the proposed framework. Results of computer simulations on several test cases are provided.