Abstract
A generalized architecture for discrete-time cellular neural networks (DTCNNs) is provided. It allows multiple layers of different architecture, which can be combined in several interconnection modes. Three elementary building blocks are introduced. Another important extension is the use of time-variant templates. They allow the definition of cyclic templates, where the coefficients are changed every iteration step and a set of templates is applied periodically. The definition of convergence has been adopted for this network structure, and important classes of templates are proved to be convergent. Examples are given for the following image processing tasks: rectangular hull extraction, skeletonization, and halftoning.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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