Image thresholding based on swarm intelligence technique for image segmentation

Shivali, Ekta Sharma, P. Mahapatra, Amit Doegar
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引用次数: 4

Abstract

Image thresholding is a critical task of image segmentation. Selection of the optimal value of the threshold is the most important task for image thresholding. The better the value of threshold better is the quality of segmentation. In this paper, recent swarm intelligence technique (fireworks algorithm) has been used for image thresholding. Fireworks algorithm is used to maximize two functions, namely Kapur and Otsu. Results show that quality of segmentation is better in case of Firewok-Otsu than Firework-Kapur. Comparison of results has been done on the basis of PSNR value.
基于群体智能的图像阈值分割技术
图像阈值分割是图像分割的一项关键任务。选取最优阈值是图像阈值分割中最重要的任务。阈值越高,分割质量越好。本文将最新的群体智能技术(烟花算法)用于图像阈值分割。烟花算法用于最大化Kapur和Otsu两个函数。结果表明,“firework - otsu”的分割质量优于“Firework-Kapur”。根据PSNR值对结果进行了比较。
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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