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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.05347 |
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| _version_ | 1866929619607748608 |
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| author | Nadimi, Sadegh Angelidakis, Vasileios Maramizonouz, Sadaf Zhang, Chao |
| author_facet | Nadimi, Sadegh Angelidakis, Vasileios Maramizonouz, Sadaf Zhang, Chao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_05347 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Automated Dynamic Image Analysis for Particle Size and Shape Classification in Three Dimensions Nadimi, Sadegh Angelidakis, Vasileios Maramizonouz, Sadaf Zhang, Chao Computer Vision and Pattern Recognition Materials Science Statistical Mechanics Image and Video Processing 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. |
| title | Automated Dynamic Image Analysis for Particle Size and Shape Classification in Three Dimensions |
| topic | Computer Vision and Pattern Recognition Materials Science Statistical Mechanics Image and Video Processing |
| url | https://arxiv.org/abs/2412.05347 |