Methods for Extracting the Skeleton of an Image Based on Cellular Automata With a Hexagonal Coating Form and Radon Transform

R. Motornyuk, S. Bilan
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引用次数: 3

Abstract

The chapter describes a brief history of the emergence of the theory of cellular automata, their main properties, and methods for constructing. The image skeletonization methods based on the Euler zero differential are described. The advantages of using hexagonal coverage for detecting moving objects in the image are shown. The software and hardware implementation of the developed methods are presented. Based on the obtained results, a hexagonal-coated cellular automata was developed to identify images of objects based on the Radon transform. The method and mathematical model of the selection of characteristic features for the selection of the skeleton and implementation on cellular automata with a hexagonal coating are described. The Radon transform allowed to effectively extract the characteristic features of images with a large percentage of noise. An experiment for different images with different noises was conducted. Experimental analysis showed the advantages of the proposed methods of image processing and extraction of characteristic features.
基于六边形涂层形式和Radon变换的元胞自动机图像骨架提取方法
本章描述了元胞自动机理论出现的简史,它们的主要性质和构造方法。介绍了基于欧拉零微分的图像骨架化方法。说明了利用六边形覆盖检测图像中运动物体的优点。给出了所开发方法的软硬件实现。在此基础上,提出了基于Radon变换的六边形包覆元胞自动机。描述了骨架选择的特征选择方法和数学模型,以及在六边形涂层元胞自动机上的实现。Radon变换可以有效地提取噪声较大的图像的特征特征。对不同噪声下的不同图像进行了实验。实验分析表明了所提出的图像处理和特征提取方法的优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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