2026-07-072026-07-07http://salesiana.dossiersoluciones.com/handle/123456789/31311This paper studies complexity of recognition of classes of bounded configurations by a generalization of conventional cellular automata (CA) -- finite dynamic cellular automata (FDCA). Inspired by the CA-based models of biological and computer vision, this study attempts to derive the properties of a complexity measure and of the classes of input configurations that make it beneficial to realize the recognition via a two-layered automaton as compared to a one-layered automaton. A formalized model of an image pattern recognition task is utilized to demonstrate that the derived conditions can be satisfied for a non-empty set of practical problems.11 pages, 1 figureComputational ComplexityComputer Vision and Pattern RecognitionF.1.3; F.2.2; F.2.3; I.4.3; I.5.1; I.5.4; I.5.5text