一种基于梯度分布属性和电力场景中的规模不变特征的红外和可见图像对齐方法
Lin Zhu1, Yuxing Mao1, Chunxu Chen1
1State Key Laboratory of Power Transmission Equipment Technology, School of Electrical Engineering, Chongqing University, Chongqing 400044, China.
Journal of imaging
|January 24, 2025
概括
本研究提出了一种用于在电网检查中调整红外和可见光图像的新方法. 尽管旋转,尺度和视角发生了变化,但该技术能够稳定地匹配特征点,从而提高检查系统的准确性.
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:
- 红外和可见光图像的自动记录对于智能电网检查系统至关重要.
- 由于可见和红外图像之间的显著属性差异,直接对齐具有挑战性.
研究的目的:
- 开发一种强大的图像对齐方法,用于电源场景中的红外和可见光图像.
- 为了克服与直接图像对齐相关的困难.
主要方法:
- 使用Sobel运算符提取边缘信息.
- 通过曲率尺度空间 (CSS) 角探测器进行特征点检测.
- 使用定向梯度直方图 (HOG) 进行描述器生成,并用尺度不变特征转换 (SIFT) 规范化.
- 通过改进的快速近似近邻和自适应值来实现匹配.
主要成果:
- 在旋转,尺度和视角变化下的特征点的稳健匹配.
- 在实验评估中表现出卓越的匹配性能.
- 红外和可见光图像对的成功对齐.
结论:
- 拟议的方法有效地解决了对红外和可见光图像对齐的挑战.
- 该技术提高了电网中的智能检查系统的可靠性.
- 这种方法为关键基础设施监控的自动图像记录提供了显著的进步.
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