Cargando…
Electrochemical Intelligent Recognition of Mineral Materials Based on Superpixel Image Segmentation
In order to study the needs of identifying rock thin-section samples by manual observation in the field of geology, a method of electrochemical intelligent recognition of mineral materials based on superpixel image segmentation is proposed. The image histogram of this method can be used to represent...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9217615/ https://www.ncbi.nlm.nih.gov/pubmed/35756146 http://dx.doi.org/10.1155/2022/6755771 |
_version_ | 1784731688197160960 |
---|---|
author | Liu, Weiping Jin, Fangzhou |
author_facet | Liu, Weiping Jin, Fangzhou |
author_sort | Liu, Weiping |
collection | PubMed |
description | In order to study the needs of identifying rock thin-section samples by manual observation in the field of geology, a method of electrochemical intelligent recognition of mineral materials based on superpixel image segmentation is proposed. The image histogram of this method can be used to represent the distribution of each pixel value of the image. This interval is consistent with the number of pixels in the method. And using the experiment, the CPU used in the experiment is Intel® Core™ i7-8700 3.2 GHz, the memory is 16 GB, and the GPU is NVIDIA GeForce GT × 1080 Ti, which ensures the accuracy of the experiment. Based on all the experimental results, it can be seen that after the two-stage processing of the designed superpixel algorithm and the region merging algorithm, the final sandstone slice image segmentation results are close to the results of manual labeling, which is helpful for the subsequent research on sandstone component identification. The feasibility of this method was verified. |
format | Online Article Text |
id | pubmed-9217615 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-92176152022-06-23 Electrochemical Intelligent Recognition of Mineral Materials Based on Superpixel Image Segmentation Liu, Weiping Jin, Fangzhou Int J Anal Chem Research Article In order to study the needs of identifying rock thin-section samples by manual observation in the field of geology, a method of electrochemical intelligent recognition of mineral materials based on superpixel image segmentation is proposed. The image histogram of this method can be used to represent the distribution of each pixel value of the image. This interval is consistent with the number of pixels in the method. And using the experiment, the CPU used in the experiment is Intel® Core™ i7-8700 3.2 GHz, the memory is 16 GB, and the GPU is NVIDIA GeForce GT × 1080 Ti, which ensures the accuracy of the experiment. Based on all the experimental results, it can be seen that after the two-stage processing of the designed superpixel algorithm and the region merging algorithm, the final sandstone slice image segmentation results are close to the results of manual labeling, which is helpful for the subsequent research on sandstone component identification. The feasibility of this method was verified. Hindawi 2022-06-15 /pmc/articles/PMC9217615/ /pubmed/35756146 http://dx.doi.org/10.1155/2022/6755771 Text en Copyright © 2022 Weiping Liu and Fangzhou Jin. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Liu, Weiping Jin, Fangzhou Electrochemical Intelligent Recognition of Mineral Materials Based on Superpixel Image Segmentation |
title | Electrochemical Intelligent Recognition of Mineral Materials Based on Superpixel Image Segmentation |
title_full | Electrochemical Intelligent Recognition of Mineral Materials Based on Superpixel Image Segmentation |
title_fullStr | Electrochemical Intelligent Recognition of Mineral Materials Based on Superpixel Image Segmentation |
title_full_unstemmed | Electrochemical Intelligent Recognition of Mineral Materials Based on Superpixel Image Segmentation |
title_short | Electrochemical Intelligent Recognition of Mineral Materials Based on Superpixel Image Segmentation |
title_sort | electrochemical intelligent recognition of mineral materials based on superpixel image segmentation |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9217615/ https://www.ncbi.nlm.nih.gov/pubmed/35756146 http://dx.doi.org/10.1155/2022/6755771 |
work_keys_str_mv | AT liuweiping electrochemicalintelligentrecognitionofmineralmaterialsbasedonsuperpixelimagesegmentation AT jinfangzhou electrochemicalintelligentrecognitionofmineralmaterialsbasedonsuperpixelimagesegmentation |