Applications of CNN processing by template decomposition

High connectivity cellular neural network (CNN) templates are inherently less robust than templates of lower connectivity. However, some types of detection tasks requiring a high degree of connectivity can be decomposed and realized by an algorithmic approach, instead of a single CNN template. The processing comprises several robust template types and logical operations. The basic template type proposed for the decomposition is at an intermediate point between high-connectivity CNN template processing and processing using digital logic exclusively.