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Binarization Method for On-Line Ferrograph Image Based on Uniform Curvelet Transformation

机译:基于均匀曲线小波变换的在线铁谱图像二值化方法

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Through real-time acquisition of visual characteristics of wear debris in lube oil, OLVF achieves on-line monitoring of equipment wear in practice. However, since a large number of bubbles exist in lube oil, which appear as a dynamically changing interference shadow in OLVF ferrograms, the traditional algorithms may easily misidentify the interference shadow as wear debris, resulting in a large error in the extracted wear debris characteristic. Based on this, an UDCT -based method for binarization of wear debris images was proposed. Through multi-scale analysis of OLVF ferrogram using UDCT and non-linear transformation of UDCT coefficients, low-frequency suppression and high-frequency denoising of wear debris images were conducted. Then, Otsu algorithm was used to realize binarization of wear debris images under strong interferences.
机译:通过实时获取润滑油中磨损碎片的视觉特征,OLVF可以在实践中实现对设备磨损的在线监测。但是,由于润滑油中存在大量气泡,这些气泡在OLVF铁磁图中表现为动态变化的干扰阴影,因此传统算法可能容易将干扰阴影识别为磨损残渣,从而导致提取的磨损残渣特性出现较大误差。在此基础上,提出了一种基于UDCT的磨损碎片图像二值化方法。通过使用UDCT对OLVF铁谱进行多尺度分析和UDCT系数的非线性变换,对磨损碎片图像进行了低频抑制和高频去噪。然后,使用Otsu算法实现在强烈干扰下的磨损碎片图像的二值化。

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