论文标题
机器学习阴影仪的粒度和形状表征
Machine learning shadowgraph for particle size and shape characterization
论文作者
论文摘要
粒子阴影图像的常规图像处理通常是耗时的，并且在处理由具有不同背景的复杂形状和簇状粒子组成的图像时会遭受退化的图像分割。在本文中，我们使用单个卷积神经网络（CNN）介绍了一种基于学习的方法，以分析粒子阴影图像。我们的方法采用两通道输出U-NET模型来生成二进制粒子图像和粒子质心图像。随后，二进制粒子图像通过标记控制的分水岭将粒子质心图像作为标记图像进行分割。与最先进的非机器学习方法相比，在合成和实验气泡图像上对该方法的评估显示出更好的性能。提出的机器学习阴影图像处理方法为实时粒子图像分析提供了有希望的工具。
Conventional image processing for particle shadow image is usually time-consuming and suffers degraded image segmentation when dealing with the images consisting of complex-shaped and clustered particles with varying backgrounds. In this paper, we introduce a robust learning-based method using a single convolution neural network (CNN) for analyzing particle shadow images. Our approach employs a two-channel-output U-net model to generate a binary particle image and a particle centroid image. The binary particle image is subsequently segmented through marker-controlled watershed approach with particle centroid image as the marker image. The assessment of this method on both synthetic and experimental bubble images has shown better performance compared to the state-of-art non-machine-learning method. The proposed machine learning shadow image processing approach provides a promising tool for real-time particle image analysis.
