使用增强的灰狼优化器进行面部图像细分
Hongliang Zhang1, Zhennao Cai2, Lei Xiao2
1Jilin Agricultural University Library, Jilin Agricultural University, Changchun 130118, China.
Biomimetics (Basel, Switzerland)
|October 27, 2023
概括
这项研究引入了改进的灰狼优化器,以增强使用卡普尔的脸部图像细分. 新方法高效地分段面对更高的准确性和更好的计算性能.
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:
- 图像细分对于人脸识别至关重要,将图像划分为区域以隔离面孔.
- 传统的值细分方法面临着越来越高的值水平的计算挑战.
- 高效准确的面部细分仍然是一个活跃的研究领域.
研究的目的:
- 为人脸识别提出一个高效的多门图像细分框架.
- 使用元启发式优化来提高细分质量和值的确定.
- 为了解决基于值的细分中的计算复杂性问题.
主要方法:
- 一个新的多门图像细分框架,集成卡普尔的.
- 一个改进的灰狼优化器变体,用于优化2D Kapur的.
- 使用灰度和非局部平均2D直方图进行图像分析.
主要成果:
- 与最先进的技术相比,拟议的方法显示出更高的性能.
- 实现了高的平均特征相似性 (0.8792) 和结构相似性 (0.8532).
- 在测试中获得了24.9dB的显著平均峰值信号噪声比.
结论:
- 开发的元启发式优化方法为面部图像细分提供了有效的解决方案.
- 该方法在细分效率和准确性方面取得了显著的进步.
- 这个框架可以作为各种面部识别应用程序的宝贵工具.
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