ParticleSeg3D: A scalable out-of-the-box deep learning segmentation solution for individual particle characterization from micro CT images in mineral processing and recycling-Reference-Cited by-同舟云学术

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ParticleSeg3D: A scalable out-of-the-box deep learning segmentation solution for individual particle characterization from micro CT images in mineral processing and recycling

Published:2024-02 Issue: Volume:434 Page:119286
ISSN:0032-5910
Container-title:Powder Technology
language:en
Short-container-title:Powder Technology

Author:

Gotkowski Karol^ORCID,Gupta Shuvam^ORCID,Godinho Jose R.A.,Tochtrop Camila G.S.,Maier-Hein Klaus H.,Isensee Fabian^ORCID

Publisher

Elsevier BV

Subject

General Chemical Engineering

Reference44 articles.

1. Nondestructive characterisation of the effect of asphalt mixture compaction on aggregate orientation and segregation using X-ray computed tomography;Hassan;Int. J. Pavement Res. Technol.,2012

2. X-ray computed tomography: A geometallurgical tool for 3D textural analysis of drill core;Becker,2016

3. S. Dominy, I. Platten, L. Howard, P. Elangovan, R. Armstrong, R. Minnitt, R. Abel, Characterisation of gold ores by X-ray computed tomography–Part 2: Applications to the determination of gold particle size and distribution, in: Proceedings of the First AusIMM International Geometallurgy Conference, Brisbane, Australia, 2011, pp. 5–7.

4. Volume quantification in interphase voxels of ore minerals using 3D imaging;Godinho;Miner. Eng.,2019

5. An improved boundary-aware U-Net for ore image semantic segmentation;Wang;Sensors,2021

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