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使用修改后的灰狼优化器进行多级值彩色图像分割.

Pei Hu1, Yibo Han1, Zheng Zhang1

  • 1School of Computer and Software, Nanyang Institute of Technology, Nanyang 473004, China.

Biomimetics (Basel, Switzerland)
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PubMed
概括
此摘要是机器生成的。

本研究引入了修改后的灰狼优化器 (MGWO),用于在彩色图像分割中高效的多级值. 与传统方法相比,MGWO提高了准确性和速度,改善了细分结果.

关键词:
灰狼优化器 灰狼优化器图像分割 图像细分 图像细分有多层次的值.

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科学领域:

  • 计算机视觉 计算机视觉
  • 图像处理 图像处理
  • 人工智能的人工智能

背景情况:

  • 图像分割的准确性在很大程度上依赖于最佳值选择.
  • 多级值提供了更高的细节,但在计算上是密集的.

研究的目的:

  • 开发一种高效准确的多层次值方法,用于彩色图像细分.
  • 增强灰狼优化器 (GWO) 以提高值性能.

主要方法:

  • 修改后的灰狼优化器 (MGWO) 包含了增强的领导人选择,位置更新和突变策略.
  • 使用Otsu方法和卡普尔作为值优化的客观函数.
  • 在BSD500数据集上使用PSNR,SSIM和FSIM等指标评估性能.

主要成果:

  • MGWO在彩色图像的多级值方面表现出卓越的表现.
  • 实验和统计分析证实了拟议的MGWO算法的有效性.
  • 在目标值,差异,PSNR,SSIM和FSIM方面取得了出色的结果.

结论:

  • 修改后的灰狼优化器 (MGWO) 为准确和高效的多层次彩色图像分割提供了有效的解决方案.
  • 对于复杂的图像分割任务,MGWO显著改进了现有的方法.
  • 改进的算法为推进图像处理技术提供了一个有希望的方法.