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Atom cloud detection and segmentation using a deep neural network

Published version
Peer-reviewed

Change log

Authors

Krstajić, Milan 
Smith, Robert P 

Abstract

jats:titleAbstract</jats:title> jats:pWe 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.</jats:p>

Description

Funder: Royal Society; doi: http://dx.doi.org/10.13039/501100000288


Funder: Trinity College, University of Cambridge; doi: http://dx.doi.org/10.13039/501100000727


Funder: John Fell Fund, University of Oxford; doi: http://dx.doi.org/10.13039/501100004789

Keywords

46 Information and Computing Sciences, 4601 Applied Computing, 4611 Machine Learning, Networking and Information Technology R&D (NITRD)

Journal Title

Machine Learning: Science and Technology

Conference Name

Journal ISSN

2632-2153
2632-2153

Volume Title

2

Publisher

IOP Publishing
Sponsorship
Engineering and Physical Sciences Research Council (EP/P009565/1)