A study of chaotic maps in differential evolution applied to gray-level image thresholding

U. Mlakar, J. Brest, Iztok Fister, Iztok Fister
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引用次数: 6

Abstract

Image segmentation is an important preprocessing step in many computer vision applications, using the image thresholding as one of the simplest and the most applied methods. Since the optimal thresholds' selection can be regarded as an optimization problem, it can be found easily by applying any meta-heuristic with an appropriate objective function. This paper investigates the impact of different chaotic maps, embedded into a self-adaptive differential evolution for the purpose of image thresholding. The Kapur entropy is used as an objective function that maximizes the entropy of different regions in the image. Three chaotic maps, namely the Kent, Logistic and Tent, found commonly in literature, are studied in this paper. The applied chaotic maps are compared to the original differential evolution, self-adaptive differential evolution, and the state-of-the-art L-Shade tested on four images. The results show that the applied chaotic maps improve the results obtained using the traditional randomized method.
差分演化中混沌映射在灰度图像阈值分割中的应用研究
图像分割是许多计算机视觉应用中重要的预处理步骤,其中图像阈值分割是最简单、应用最广泛的方法之一。由于最优阈值的选择可以看作是一个优化问题,因此可以通过使用具有适当目标函数的任何元启发式方法很容易地找到它。本文研究了不同混沌映射的影响,嵌入到自适应差分进化中,用于图像阈值分割。将Kapur熵作为目标函数,使图像中不同区域的熵最大化。本文研究了文献中常见的三种混沌图,即Kent、Logistic和Tent。将应用的混沌映射与原始微分演化、自适应微分演化和最先进的L-Shade进行了比较,并对四幅图像进行了测试。结果表明,混沌映射的应用改善了传统随机化方法的结果。
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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