Mărginean Radu, Andreica Anca, Dioşan Laura, Bálint Zoltán
IMOGEN Research Institute, County Clinical Emergency Hospital, 400006 Cluj-Napoca, Romania.
Faculty of Mathematics and Computer Science, Babeş-Bolyai University, 400084 Cluj-Napoca, Romania.
Entropy (Basel). 2020 Sep 15;22(9):1028. doi: 10.3390/e22091028.
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.
利用周围自然和人工空间中复杂系统所展现的重要特征，将提高计算模型的性能。因此，本文的目的是使用细胞自动机作为模拟复杂性的工具，仅基于简单的局部相互作用就能产生有趣的全局行为。我们表明，在图像分割的背景下，当我们扰动细胞自动机的邻域系统时会出现蝴蝶效应。具体而言，我们增强了具有混沌特征的经典GrowCut细胞自动机，这也能够提高其性能（例如，对于二维图像，骰子系数为71%）。这种增强的GrowCut变体（称为基于带的GrowCut）使用扩展的随机邻域，其中随机选择的远程邻居增强了标准的局部邻居。我们通过对合成图像和自然图像进行数值实验，证明了蝴蝶效应的存在以及分割性能的提高。因此，我们的结果表明，通过在执行任务的初始条件上进行微小变化，我们可以在分割的最终结果中引发重大变化。
