Parallel implementation of elastic grid matching using cellular neural networks

Abstract

The following paper presents a method that allows for a parallel implementation of the most computationally expensive element of the deformable template paradigm, which is a grid-matching procedure. Cellular Neural Network Universal Machine has been selected as a frame-work for the task realization. A basic idea of deformable grid matching is to guide node location updates in a way that minimizes dissimilarity between an image and grid-recorded information, and that ensures minimum grid deformations. The proposed method provides a parallel implementation of this general concept and includes a novel approach to grid's elasticity modeling. The method has been experimentally verified using two different analog hardware environments, yielding high execution speeds and satisfactory processing accuracy. © Springer-Verlag Berlin Heidelberg 2007.