Avenues for the use of cellular automata in image segmentation

The majority of Cellular Automata (CA) described in the literature are binary or three-state. While several abstractions are possible to generalise to more than three states, only a negligible number of multi–state CA rules exist with concrete practical applications. This paper proposes a generic rule for multi–state CA. The rule allows for any number of states, and allows for the states are semantically related. The rule is illustrated on the concrete example of image segmentation, where the CA agents are pixels in an image, and their states are the pixels’ greyscale values. We investigate in detail the proposed rule and some of its variations, and we also compare its effectiveness against the existing Greenberg–Hastings automaton, as the closest relative of our proposed technique. We apply the proposed methods to both synthetic and real-world images, evaluating the results with a variety of measures. The experimental results demonstrate that our proposed method can segment images accurately and effectively.</p

20th European Conference on the Applications of Evolutionary Computation, EvoApplications 2017

The proceedings contain 57 papers. The special focus in this conference is on Applications of Evolutionary Computation. The topics include: Minimization of systemic risk for directed network using genetic algorithm; pricing rainfall based futures using genetic programming; dynamic portfolio optimization in ultra-high frequency environment; integration of reaction kinetics theory and gene expression programming to infer reaction mechanism; improving the reproducibility of genetic association results using genotype resampling methods; characterising the influence of rule-based knowledge representations in biological knowledge extraction from transcriptomics data; application to blood glucose forecasting; genetic programming representations for multi dimensional feature learning in biomedical classification; meta-heuristically seeded genetic algorithm for independent job scheduling in grid computing; analysis of average communicability in complex networks; configuring dynamic heterogeneous wireless communications networks using a customised genetic algorithm; multi-objective evolutionary algorithms for influence maximization in social networks; Lamarckian and lifelong memetic search in agent-based computing; two-phase strategy managing insensitivity in global optimization; avenues for the use of cellular automata in image segmentation; localization on hubs and delocalized diffusion; hybrid multi-ensemble scheduling; driving in TORCS using modular fuzzy controllers; automated game balancing in ms pacman and starcraft using evolutionary algorithms; evolving game specific UCB alternatives for general video game playing; analysis of vanilla rolling horizon evolution parameters in general video game playing and evolutionary art using the fly algorithm