  This 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.
