Cellular Neural Networks with dynamic cell activity control for Hausdorff distance estimation

A concept of Cellular Neural Networks with dynamic cell activity control is proposed in the paper. The concept is an extension to the Fixed State Map mechanism and it assumes that cells can be disabled or enabled for processing based on assessment of current distributions of their neighboring signals. A particular case, where this assessment is made by thresholding a result of cross-correlation between feedback template and neighborhood outputs is shown to provide a simple means for efficient min/max problem handling. This idea requires introducing only minor modifications to a cell structure. As an example, application of the proposed network for fast estimation of Hausdorff distance between two sets has been considered.