缩放Haralick特征与图像比特深度和灰色水平共发生矩阵移位向量对线性梯度的缩放
Ana Oprisan1, Sorinel Adrian Oprisan1
1Department of Physics and Astronomy, College of Charleston, Charleston, SC 29424, USA.
Computers in biology and medicine
|August 14, 2025
概括
这项研究得出了Haralick纹理特征的分析缩放规律,使得量子化不变纹理分类成为可能. 这些新的规范化方法提高了各种成像数据集的可复制性和可比性.
科学领域:
- 计算机视觉 计算机视觉
- 图像分析 图像分析
- 纹理识别 纹理识别
背景情况:
- 人类的感知将纹理等价性与二次统计数据联系起来,与图像梯度有关.
- 当前的纹理分析方法在成像条件和量子化变化方面存在困难.
研究的目的:
- 为哈拉利克纹理特征推导分析缩放规律.
- 能够实现量化不变和可重复的纹理分类.
- 在异质成像条件下改进纹理分析.
主要方法:
- 分析了从线性图像梯度的灰级共发生矩阵 (GLCM) 对称性.
- 为能量,对比度,相关性和逆差时刻特征衍生了封闭形式的缩放定律.
- 为哈拉利克特征开发了理论上合理的规范化因子.
主要成果:
- 由于线性梯度,在对角线上展示了GLCM入口对齐.
- 通过数值模拟验证了分析缩放规律.
- 展示了推导的规范化因子在经验方法上的优异性能.
结论:
- 建立了Haralick特征规范化的原则框架.
- 在纹理分析中提高可复制性,可比性和可解释性.
- 基于信息的特征选择和分类器设计,用于强大的纹理分析,用于放射学和遥感等应用.
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