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Reconstruction and segmentation from sparse sequential X-ray measurements of wood logs

Springer, Sebastian; Glielmo, Aldo; Senchukova, Angelina; Kauppi, Tomi; Suuronen, Jarkko; Roininen, Lassi; Haario, Heikki; Hauptmann, Andreas (2023-09-01)

 
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https://doi.org/10.3934/ammc.2023002

Springer, Sebastian
Glielmo, Aldo
Senchukova, Angelina
Kauppi, Tomi
Suuronen, Jarkko
Roininen, Lassi
Haario, Heikki
Hauptmann, Andreas
American institute of mathematical sciences
01.09.2023

Sebastian Springer, Aldo Glielmo, Angelina Senchukova, Tomi Kauppi, Jarkko Suuronen, Lassi Roininen, Heikki Haario, Andreas Hauptmann. Reconstruction and segmentation from sparse sequential X-ray measurements of wood logs. Applied Mathematics for Modern Challenges, 2023, 1(1): 1-20. doi: 10.3934/ammc.2023002

https://rightsstatements.org/vocab/InC/1.0/
© 2023 American Institute of Mathematical Sciences. This article has been published in a revised form in Applied mathematics for modern challenges https://doi.org/10.3934/ammc.2023002. This version is free to download for private research and study only. Not for redistribution, re-sale or use in derivative works.
https://rightsstatements.org/vocab/InC/1.0/
doi:https://doi.org/10.3934/ammc.2023002
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https://urn.fi/URN:NBN:fi:oulu-202401121216
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Abstract

In industrial applications, it is common to scan objects on a moving conveyor belt. If slice-wise 2D computed tomography (CT) measurements of the moving object are obtained we call it a sequential scanning geometry. In this case, each slice on its own does not carry sufficient information to reconstruct a useful tomographic image. Thus, here we propose the use of a Dimension reduced Kalman Filter to accumulate information between slices and allow for sufficiently accurate reconstructions for further assessment of the object. Additionally, we propose to use an unsupervised clustering approach known as Density Peak Advanced, to perform a segmentation and spot density anomalies in the internal structure of the reconstructed objects. We evaluate the method in a proof of concept study for the application of wood log scanning for the industrial sawing process, where the goal is to spot anomalies within the wood log to allow for optimal sawing patterns. Reconstruction and segmentation quality are evaluated from experimental measurement data for various scenarios of severely undersampled X-measurements. Results show clearly that an improvement in reconstruction quality can be obtained by employing the Dimension reduced Kalman Filter allowing to robustly obtain the segmented logs.
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