Cross-correlation image analysis for real-time particle tracking
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arXiv
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| Hauptverfasser: | , , , , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866911959827349504 |
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| author | Werneck, Leonardo R. Jessup, Cody Brandenberger, Austin Knowles, Tyler Lewandowski, Charles W. Nolan, Megan Sible, Ken Etienne, Zachariah B. D'Urso, Brian |
| author_facet | Werneck, Leonardo R. Jessup, Cody Brandenberger, Austin Knowles, Tyler Lewandowski, Charles W. Nolan, Megan Sible, Ken Etienne, Zachariah B. D'Urso, Brian |
| contents | Accurately measuring the translations of objects between images is essential in many fields, including biology, medicine, chemistry, and physics. One important application is tracking one or more particles by measuring their apparent displacements in a series of images. Popular methods, such as the center-of-mass, often require idealized scenarios to reach the shot-noise limit of particle tracking and are, therefore, not generally applicable to multiple image types. More general methods, like maximum likelihood estimation, reliably approach the shot-noise limit, but are too computationally intense for use in real-time applications. These limitations are significant, as real-time, shot-noise-limited particle tracking is of paramount importance for feedback control systems. To fill this gap, we introduce a new cross-correlation-based algorithm that approaches shot-noise-limited displacement detection and a GPU-based implementation for real-time image analysis of a single particle. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_08770 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Cross-correlation image analysis for real-time particle tracking Werneck, Leonardo R. Jessup, Cody Brandenberger, Austin Knowles, Tyler Lewandowski, Charles W. Nolan, Megan Sible, Ken Etienne, Zachariah B. D'Urso, Brian Optics Image and Video Processing Accurately measuring the translations of objects between images is essential in many fields, including biology, medicine, chemistry, and physics. One important application is tracking one or more particles by measuring their apparent displacements in a series of images. Popular methods, such as the center-of-mass, often require idealized scenarios to reach the shot-noise limit of particle tracking and are, therefore, not generally applicable to multiple image types. More general methods, like maximum likelihood estimation, reliably approach the shot-noise limit, but are too computationally intense for use in real-time applications. These limitations are significant, as real-time, shot-noise-limited particle tracking is of paramount importance for feedback control systems. To fill this gap, we introduce a new cross-correlation-based algorithm that approaches shot-noise-limited displacement detection and a GPU-based implementation for real-time image analysis of a single particle. |
| title | Cross-correlation image analysis for real-time particle tracking |
| topic | Optics Image and Video Processing |
| url | https://arxiv.org/abs/2310.08770 |