基于LSHADE的多级阈值分割

Guang Yang, Zhaoguang Liu, Zongna Zhu
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引用次数: 0

摘要

为了解决传统多阈值图像分割方法存在的问题,提出了一种基于LSHADE的多阈值图像分割方法。LSHADE在SHADE的基础上,采用线性种群大小缩减方法,不断减小种群大小,提高算法的性能。首先,基于类间方差和熵进行多阈值图像分割,并比较两种方法的分割结果。然后以PSNR和目标函数值作为评价标准,将LSHADE算法与其他算法进行比较。结果表明,该方法提高了分割速度和分割精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-level threshold segmentation based on LSHADE
In order to solve the problems in traditional multi-threshold image segmentation methods, a Multi-threshold image segmentation method based on LSHADE is proposed. On the basis of SHADE, LSHADE uses a linear population size reduction method to continuously reduce the population size to improve the performance of the algorithm. First, perform multi-threshold image segmentation based on the between-class variance and entropy, and compare the segmentation results of the two methods. Then using PSNR and objective function values as evaluation criteria, comparing LSHADE with other algorithms. The results show that the segmentation speed and segmentation accuracy have been improved.
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