preprint · arXiv (Cornell University) · 2020

Machine learning shadowgraph for particle size and shape characterization

Li, Jiaqi, Shao, Siyao, Hong, Jiarong

Cited by 0Open access

Abstract

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.

Cite this paper

APA 7

Li, J., Shao, S., & Hong, J. (2020). Machine learning shadowgraph for particle size and shape characterization. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2003.14373

MLA 9

Li, Jiaqi, et al. “Machine learning shadowgraph for particle size and shape characterization.” arXiv (Cornell University), 2020, https://doi.org/10.48550/arxiv.2003.14373.

Chicago

Li, Jiaqi, Siyao Shao, and Jiarong Hong. 2020. “Machine learning shadowgraph for particle size and shape characterization.” arXiv (Cornell University). https://doi.org/10.48550/arxiv.2003.14373.

Harvard

Li, J., Shao, S. and Hong, J. (2020) ‘Machine learning shadowgraph for particle size and shape characterization’, arXiv (Cornell University). Available at: https://doi.org/10.48550/arxiv.2003.14373.

BibTeX

@misc{li2020machine,
  title = {{Machine learning shadowgraph for particle size and shape characterization}},
  author = {Li, Jiaqi and Shao, Siyao and Hong, Jiarong},
  journal = {arXiv (Cornell University)},
  year = {2020},
  doi = {10.48550/arxiv.2003.14373}
}

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