English

Automated Dynamic Image Analysis for Particle Size and Shape Classification in Three Dimensions

Computer Vision and Pattern Recognition 2024-12-10 v1 Materials Science Statistical Mechanics Image and Video Processing

Abstract

We introduce OCULAR, an innovative hardware and software solution for three-dimensional dynamic image analysis of fine particles. Current state-of-the art instruments for dynamic image analysis are largely limited to two-dimensional imaging. However, extensive literature has demonstrated that relying on a single two-dimensional projection for particle characterisation can lead to inaccuracies in many applications. Existing three-dimensional imaging technologies, such as computed tomography, laser scanning, and orthophotography, are limited to static objects. These methods are often not statistically representative and come with significant post-processing requirements, as well as the need for specialised imaging and computing resources. OCULAR addresses these challenges by providing a cost-effective solution for imaging continuous particle streams using a synchronised array of optical cameras. Particle shape characterisation is achieved through the reconstruction of their three-dimensional surfaces. This paper details the OCULAR methodology, evaluates its reproducibility, and compares its results against X-ray micro computed tomography, highlighting its potential for efficient and reliable particle analysis.

Keywords

Cite

@article{arxiv.2412.05347,
  title  = {Automated Dynamic Image Analysis for Particle Size and Shape Classification in Three Dimensions},
  author = {Sadegh Nadimi and Vasileios Angelidakis and Sadaf Maramizonouz and Chao Zhang},
  journal= {arXiv preprint arXiv:2412.05347},
  year   = {2024}
}

Comments

11 pages, 5 figures

R2 v1 2026-06-28T20:26:07.181Z