On the Cell-based Complexity of Recognition of Bounded Configurations by Finite Dynamic Cellular Automata

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dc.creatorMakatchev, Maxim
dc.date2002-10-11
dc.date.accessioned2026-07-07T03:18:55Z
dc.date.available2026-07-07T03:18:55Z
dc.descriptionThis 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.
dc.description11 pages, 1 figure
dc.identifierhttps://arxiv.org/abs/cs/0210009
dc.identifierhttp://arxiv.org/abs/cs/0210009
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/31311
dc.subjectComputational Complexity
dc.subjectComputer Vision and Pattern Recognition
dc.subjectF.1.3; F.2.2; F.2.3; I.4.3; I.5.1; I.5.4; I.5.5
dc.titleOn the Cell-based Complexity of Recognition of Bounded Configurations by Finite Dynamic Cellular Automata
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