Parameter configurations for hole extraction in cellular neural networks (CNN)

It is shown that the holes of the objects in an input image with a CT-CNN [1] or a DT-CNN [2] may be obtained in a single transient using just one linear parameter configuration. A set of local rules is given that describe how a CNN with a linear configuration may extract the hole of the objects of an input image in a single transient. The parameter configuration for DT-CNNs or for CT-CNNs is obtained as the solution of a single linear programming problem, including robustness as an objective. The tolerances to multiplicative and additive errors caused by circuit inaccuracies for the linear hole-extraction configurations proposed have been deduced. These tolerable errors have been corroborated by simulations. The tolerance to errors and the speed of the CT-CNN linear configuration proposed for hole extraction are compared with those of the CT-CNN nonlinear configuration found in the bibliography [3].