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.
Similar content being viewed by others
Author information
Authors and Affiliations
Additional information
Received: 1 August 1997 / Accepted: 29 September 1999
Rights and permissions
About this article
Cite this article
Taraglio, S., Zanela, A. A practical use of cellular neural networks: the stereo-vision problem as an optimisation. Machine Vision and Applications 11, 242–251 (2000). https://doi.org/10.1007/s001380050107
Issue Date:
DOI: https://doi.org/10.1007/s001380050107