# Machine learning shadowgraph for particle size and shape characterization

arXiv: https://arxiv.org/abs/2003.14373 · HTML: https://mlsanity.com/paper/2003.14373/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2020.

## Built on by (most cited first, 2 of 2)

- [Bubble identification from images with machine learning methods](https://mlsanity.com/paper/2202.03107/) (2022)
- [Measurement of Hybrid Rocket Solid Fuel Regression Rate for a Slab Burner using Deep Learning](https://mlsanity.com/paper/2108.11276/) (2021)

## Abstract (arXiv)

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.