Clustering-based particle detection method for digital holography to detect the three-dimensional location and in-plane size of particles
(2021) In Measurement Science and Technology 32(5).- Abstract
Digital holography (DH) has been extensively applied in particle field measurements due to its promising ability to simultaneously provide the three-dimensional location and in-plane size of particles. Particle detection methods are crucial in hologram data processing to determine particle size and particle in-focus depth, which directly affect the measurement accuracy and robustness of DH. In this work, inspired by clustering algorithms, a new clustering-based particle detection (CBPD) method was proposed for DH. To the best of our knowledge this is the first time that clustering algorithms have been applied in processing holograms for particle detection. The results of both simulations and experiments confirmed the feasibility of our... (More)
Digital holography (DH) has been extensively applied in particle field measurements due to its promising ability to simultaneously provide the three-dimensional location and in-plane size of particles. Particle detection methods are crucial in hologram data processing to determine particle size and particle in-focus depth, which directly affect the measurement accuracy and robustness of DH. In this work, inspired by clustering algorithms, a new clustering-based particle detection (CBPD) method was proposed for DH. To the best of our knowledge this is the first time that clustering algorithms have been applied in processing holograms for particle detection. The results of both simulations and experiments confirmed the feasibility of our proposed method. This data-driven method features automatic recognition of particles, particle edges and background, and accurate separation of overlapping particles. Compared with seven conventional particle detection methods, the CBPD method has improved accuracy in measuring particle positions and displacements.
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- author
- Huang, Jianqing LU ; Li, Shen LU ; Zi, Yabo ; Qian, Yong LU ; Cai, Weiwei ; Aldén, Marcus LU and Li, Zhongshan LU
- organization
- publishing date
- 2021-05
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- 3D imaging, clustering algorithm, data-driven approach, digital holography, particle detection
- in
- Measurement Science and Technology
- volume
- 32
- issue
- 5
- article number
- 055205
- publisher
- IOP Publishing
- external identifiers
-
- scopus:85103780285
- ISSN
- 0957-0233
- DOI
- 10.1088/1361-6501/abd7aa
- language
- English
- LU publication?
- yes
- id
- 3d91b21e-813b-400d-bf0c-a3dcd8323642
- date added to LUP
- 2021-04-19 09:00:40
- date last changed
- 2022-04-27 01:36:04
@article{3d91b21e-813b-400d-bf0c-a3dcd8323642, abstract = {{<p>Digital holography (DH) has been extensively applied in particle field measurements due to its promising ability to simultaneously provide the three-dimensional location and in-plane size of particles. Particle detection methods are crucial in hologram data processing to determine particle size and particle in-focus depth, which directly affect the measurement accuracy and robustness of DH. In this work, inspired by clustering algorithms, a new clustering-based particle detection (CBPD) method was proposed for DH. To the best of our knowledge this is the first time that clustering algorithms have been applied in processing holograms for particle detection. The results of both simulations and experiments confirmed the feasibility of our proposed method. This data-driven method features automatic recognition of particles, particle edges and background, and accurate separation of overlapping particles. Compared with seven conventional particle detection methods, the CBPD method has improved accuracy in measuring particle positions and displacements.</p>}}, author = {{Huang, Jianqing and Li, Shen and Zi, Yabo and Qian, Yong and Cai, Weiwei and Aldén, Marcus and Li, Zhongshan}}, issn = {{0957-0233}}, keywords = {{3D imaging; clustering algorithm; data-driven approach; digital holography; particle detection}}, language = {{eng}}, number = {{5}}, publisher = {{IOP Publishing}}, series = {{Measurement Science and Technology}}, title = {{Clustering-based particle detection method for digital holography to detect the three-dimensional location and in-plane size of particles}}, url = {{http://dx.doi.org/10.1088/1361-6501/abd7aa}}, doi = {{10.1088/1361-6501/abd7aa}}, volume = {{32}}, year = {{2021}}, }