基于二维经验模态分解的液体不透明度检测方法

Guo Qiang, Song Wen-ming
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引用次数: 0

摘要

针对液体浑浊度检测易受噪声影响的问题,提出了一种基于二维经验模态分解(BEMD)和Robert算子的液体浑浊度检测方法。该方法的关键部分是BEMD算法,该算法将液体图像分解为若干个内禀模态函数(IMFs),然后利用Robert算子检测每个内禀模态函数的边缘,有选择地重建图像边缘,突出液体和杂质的边缘细节。实验结果表明,该方法能有效降低随机噪声对浑浊度检测的影响,提高浑浊度检测的精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Liquid Opacity Detection Method Based on Bidimensional Empirical Mode Decomposition
According to the problem that liquid turbidity detection is vulnerable to the noise, a novel liquid turbidity detection method based on Bidimensional Empirical Mode Decomposition (BEMD) and Robert operator is proposed. The key part of method is the BEMD algorithm, with which, liquid images can be decomposed to several Intrinsic Mode Functions (IMFs), then we can use Robert operator to detect the edge of each IMF to reconstruct the image edges selectively for highlighting edge details of the liquid and impurity. Experimental results show that the method presented can reduce the influence of random noise on the turbidity detection effectively, and improve the accuracy of turbidity detection.
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