2020
DOI: 10.3390/e22091028
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Butterfly Effect in Chaotic Image Segmentation

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 automat… Show more

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Cited by 6 publications
(4 citation statements)
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References 39 publications
(63 reference statements)
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“…In the process of image recognition and processing, image segmentation is an indispensable key technology. It divides the tested image into several parts according to specific attributes through some technical means, which is a key step from image processing to image analysis [28]. ere are many existing image segmentation methods, including segmentation methods based on image features and segmentation methods based on specific theoretical tools [29].…”
Section: Image Identificationmentioning
confidence: 99%
“…In the process of image recognition and processing, image segmentation is an indispensable key technology. It divides the tested image into several parts according to specific attributes through some technical means, which is a key step from image processing to image analysis [28]. ere are many existing image segmentation methods, including segmentation methods based on image features and segmentation methods based on specific theoretical tools [29].…”
Section: Image Identificationmentioning
confidence: 99%
“…The feasibility to automatically generate seeds for GrowCut is shown; besides, authors suggest a method to automate seed generation for the segmentation task in heart images. In addition, a conventional GrowCut cellular automaton using chaotic features is enhanced in [19]. This development employs an extended, stochastic neighborhood, where randomly-selected remote neighbors reinforce the conventional local neighbors.…”
Section: Introductionmentioning
confidence: 99%
“…The edge detection problem has been approached using cellular automata with fixed rules, e.g., linear rules [ 5 ] or custom, threshold-based rules [ 6 ]. Cellular automata models have also been applied to image segmentation, a related computer vision problem [ 7 ]. Additionally, there are methods which rely on automatically finding suitable rules by performing an exhaustive search [ 8 ], or by applying other models for this task, such as cellular learning automata [ 9 ] or particle swarm optimization (PSO) [ 10 ].…”
Section: Introductionmentioning
confidence: 99%