通过社交蜘蛛优化 (SSO) 优化支持矢量机器 (SVM) 以在彩色图像中检测边缘.
1Suzhou Chien-Shiung Institute of Technology, Taicang, 215411, China. 2031546@tongji.edu.cn.
Scientific reports
|April 21, 2024
概括
一种新方法提高了彩色图像边缘检测使用支持矢量机 (SVM) 和社会蜘蛛优化 (SSO). 这种方法通过完善跨颜色层的边界来提高边缘精度,优于现有的技术.
科学领域:
- 计算机视觉 计算机视觉
- 图像处理 图像处理
- 机器学习 机器学习
背景情况:
- 边缘检测对于对象识别和医学图像分析至关重要.
- 彩色图像边缘检测是复杂的,因为多个颜色层和噪声.
研究的目的:
- 提出一种简单有效的方法,用于色彩图像中的边缘检测.
- 为了改善边缘定位,减少彩色图像中的噪声效应.
主要方法:
- 使用一个支持向量机 (SVM) 与一个辐射基函数 (RBF) 内核.
- 采用社交蜘蛛优化 (SSO) 算法来调整SVM的超参数和细化边缘.
- 结合了初始的灰度边缘估计与对对色层分析.
主要成果:
- 在BSDS500数据库中实现了93.11%的平均准确性.
- 与以前的边缘检测方法相比,其表现优越.
- 成功地识别出突出的图像边缘,同时减轻噪声.
结论:
- 拟议的SVM和SSO联合方法为彩色图像边缘检测提供了有效的解决方案.
- 该技术在复杂的图像场景中提高了准确性和稳定性.
- 这种方法为需要精确边缘识别的应用提供了有价值的工具.
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