Journal article
Atom cloud detection and segmentation using a deep neural network
- Abstract:
- We use a deep neural network (NN) to detect and place region-of-interest (ROI) boxes around ultracold atom clouds in absorption and fluorescence images—with the ability to identify and bound multiple clouds within a single image. The NN also outputs segmentation masks that identify the size, shape and orientation of each cloud from which we extract the clouds' Gaussian parameters. This allows 2D Gaussian fits to be reliably seeded thereby enabling fully automatic image processing. The method developed performs significantly better than a more conventional method based on a standardized image analysis library (Scikit-image) both for identifying ROI and extracting Gaussian parameters.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, 1.3MB, Terms of use)
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- Publisher copy:
- 10.1088/2632-2153/abf5ee
Authors
- Publisher:
- IOP Publishing
- Journal:
- Machine Learning: Science and Technology More from this journal
- Volume:
- 2
- Issue:
- 4
- Article number:
- 045008
- Publication date:
- 2021-07-15
- Acceptance date:
- 2021-04-08
- DOI:
- EISSN:
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2632-2153
- Language:
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English
- Keywords:
- Pubs id:
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1171872
- Local pid:
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pubs:1171872
- Deposit date:
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2021-04-18
Terms of use
- Copyright holder:
- Hofer et al.
- Copyright date:
- 2021
- Rights statement:
- © 2021 The Author(s). Published by IOP Publishing Ltd. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
- Licence:
- CC Attribution (CC BY)
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