二维图像的Lempel-Ziv复杂度计算方法及其在缺陷检测中的应用
Jiancheng Yin1, Wentao Sui1, Xuye Zhuang1
1School of Mechanical Engineering, Shandong University of Technology, Zibo 255049, China.
Entropy (Basel, Switzerland)
|October 28, 2025
概括
这项研究引入了一种新的二维Lempel-Ziv复杂性 (LZC) 用于图像分析,将其使用范围扩展到一维时间序列之外. 该方法在缺陷检测方面达到100%的准确性,证明了其在图像模式分析方面的有效性.
科学领域:
- 计算机视觉 计算机视觉
- 图像处理 图像处理
- 模式识别 模式识别
背景情况:
- 伦佩尔-齐夫复杂度 (LZC) 对于分析一维时间序列是有效的,但不能直接应用于二维图像.
- 现有的方法缺乏直接分析图像数据中的复杂模式的能力.
研究的目的:
- 开发一种用于分析二维图像的二维Lempel-Ziv复杂度 (LZC) 方法.
- 将LZC的应用扩展到图像分析和缺陷检测.
- 改进识别图像中的独立模式变化.
主要方法:
- 将LZC与来自卷积神经网络的局部受体场概念结合起来.
- 标准化图像像素和大小,然后根据4x4区域值排序进行编码.
- 重组图像编码为一个向量用于LZC计算.
- 与扩展和Sobel操作器集成,用于缺陷检测.
主要成果:
- 成功地将LZC扩展到二维图像.
- 证明有效识别图像中的独立模式变化.
- 在实际的缺陷检测情况下实现了100%的准确性.
结论:
- 拟议的2D LZC方法将LZC的应用范围扩大到图像分析.
- 该方法有效检测缺陷,提供高精度.
- 这种方法为分析图像数据中的复杂模式提供了一个新的工具.
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