# Bubble identification from images with machine learning methods

arXiv: https://arxiv.org/abs/2202.03107 · HTML: https://mlsanity.com/paper/2202.03107/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 29 references that are in the map.
- First posted on arXiv 7 Feb 2022.

## Builds on (most cited first, 7 of 7)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Cell Detection with Star-convex Polygons](https://mlsanity.com/paper/1806.03535/) (2018)
- [MultiStar: Instance Segmentation of Overlapping Objects with Star-Convex Polygons](https://mlsanity.com/paper/2011.13228/) (2020)
- [BubGAN: Bubble Generative Adversarial Networks for Synthesizing Realistic Bubbly Flow Images](https://mlsanity.com/paper/1809.02266/) (2018)
- [Machine learning shadowgraph for particle size and shape characterization](https://mlsanity.com/paper/2003.14373/) (2020)

## Built on by (most cited first, 5 of 5)

- [Uncertainty Estimation in Instance Segmentation with Star-convex Shapes](https://mlsanity.com/paper/2309.10513/) (2023)
- [VideoSAM: A Large Vision Foundation Model for High-Speed Video Segmentation](https://mlsanity.com/paper/2410.21304/) (2024)
- [Segmenting the Complex and Irregular in Two-Phase Flows: A Real-World Empirical Study with SAM2](https://mlsanity.com/paper/2508.05227/) (2025)
- [Data-Centric Visual Development for Self-Driving Labs](https://mlsanity.com/paper/2512.02018/) (2025)
- [(HB-ARFM) History-Bootstrapped Flow Matching for Inverse Boiling Reconstruction](https://mlsanity.com/paper/2606.00349/) (2026)

## Abstract (arXiv)

An automated and reliable processing of bubbly flow images is highly needed to analyse large data sets of comprehensive experimental series. A particular difficulty arises due to overlapping bubble projections in recorded images, which highly complicates the identification of individual bubbles. Recent approaches focus on the use of deep learning algorithms for this task and have already proven the high potential of such techniques. The main difficulties are the capability to handle different image conditions, higher gas volume fractions and a proper reconstruction of the hidden segment of a partly occluded bubble. In the present work, we try to tackle these points by testing three different methods based on Convolutional Neural Networks (CNNs) for the two former and two individual approaches that can be used subsequently to address the latter. To validate our methodology, we created test data sets with synthetic images that further demonstrate the capabilities as well as limitations of our combined approach. The generated data, code and trained models are made accessible to facilitate the use as well as further developments in the research field of bubble recognition in experimental images.