  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.
