Abstract
The exploitation of the important features exhibited by the complex systems found in the surrounding natural and artificial space will improve computational model performance. Therefore, the purpose of the current paper is to use cellular automata as a tool simulating complexity, able to bring forth an interesting global behaviour based only on simple, local interactions. We show that, in the context of image segmentation, a butterfly effect arises when we perturb the neighbourhood system of a cellular automaton. Specifically, we enhance a classical GrowCut cellular automaton with chaotic features, which are also able to improve its performance (e.g., a Dice coefficient of 71% in case of 2D images). This enhanced GrowCut flavor (referred to as Band-Based GrowCut) uses an extended, stochastic neighbourhood, in which randomly-selected remote neighbours reinforce the standard local ones. We demonstrate the presence of the butterfly effect and an increase in segmentation performance by numerical experiments performed on synthetic and natural images. Thus, our results suggest that, by having small changes in the initial conditions of the performed task, we can induce major changes in the final outcome of the segmentation.
Highlights
IntroductionThe various complex systems we directly or indirectly interact with, are either natural (e.g., societies, ecologies, living organisms, organs) or artificial (e.g., artificial intelligence systems, artificial neural networks, evolutionary programs, parallel and distributed computing systems)
The various complex systems we directly or indirectly interact with, are either natural or artificial.Both categories are characterized by apparently complex phenomena that emerge as a result of often nonlinear spatio-temporal interactions among a large number of elements at different levels of organization [1]
In this experiment we investigated the presence of a butterfly effect by assessing whether by changing the initial conditions of the system we can determine a change in image segmentation properties
Summary
The various complex systems we directly or indirectly interact with, are either natural (e.g., societies, ecologies, living organisms, organs) or artificial (e.g., artificial intelligence systems, artificial neural networks, evolutionary programs, parallel and distributed computing systems). Both categories are characterized by apparently complex phenomena that emerge as a result of often nonlinear spatio-temporal interactions among a large number of elements at different levels of organization [1]. We believe that image segmentation does not yet benefit from all advantages of self-organization and emergence potentially occurring in cellular automata, as in current approaches, these computational methods are mostly used as a parallelization tool. This motivates the study of image segmentation methods that build upon cellular automata on the edge of chaos, where remarkable phenomena occur in complex systems
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