SwinMin: A mineral recognition model incorporating convolution and multi-scale contexts into swin transformer-Reference-Cited by-同舟云学术

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SwinMin: A mineral recognition model incorporating convolution and multi-scale contexts into swin transformer

Published:2024-02 Issue: Volume:184 Page:105532
ISSN:0098-3004
Container-title:Computers & Geosciences
language:en
Short-container-title:Computers & Geosciences

Author:

Jia Liqin,Chen Feng,Yang Mei,Meng Fang,He Mingyue,Liu Hongmin^ORCID

Publisher

Elsevier BV

Reference60 articles.

1. Application of decision tree algorithm for classification and identification of natural minerals using SEM-EDS;Akkas;Comput. Geosci.,2015

2. Automated mineral identification algorithm using optical properties of crystals;Aligholi;Comput. Geosci.,2015

3. Improved mineralogical analysis in copper ores by laser-induced breakdown spectroscopy;Álvarez;J. Anal. Atomic Spectrom.,2022

4. Mineral identification using color spaces and artificial neural networks;Baykan;Comput. Geosci.,2010

5. Minerals Identification Dataset;Brempong,2019

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. 3D feature characterization of flotation froth based on a dual-attention encoding volume stereo matching model and binocular stereo vision extraction;Minerals Engineering;2024-10

2. Froth image based monitoring of platinum group metals flotation with vision transformers and convolutional neural networks;Minerals Engineering;2024-09

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