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...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | International Journal of Analytical Chemistry |
| Online Access: | http://dx.doi.org/10.1155/2022/6755771 |
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| _version_ | 1832562548375814144 |
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| author | Weiping Liu Fangzhou Jin |
| author_facet | Weiping Liu Fangzhou Jin |
| author_sort | Weiping Liu |
| collection | DOAJ |
| 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 | Article |
| id | doaj-art-9aedb45116724110a206618195c2d149 |
| institution | Kabale University |
| issn | 1687-8779 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Analytical Chemistry |
| spelling | doaj-art-9aedb45116724110a206618195c2d1492025-02-03T01:22:26ZengWileyInternational Journal of Analytical Chemistry1687-87792022-01-01202210.1155/2022/6755771Electrochemical Intelligent Recognition of Mineral Materials Based on Superpixel Image SegmentationWeiping Liu0Fangzhou Jin1Department of Fundamental SubjectsDepartment of Fundamental SubjectsIn 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.http://dx.doi.org/10.1155/2022/6755771 |
| spellingShingle | Weiping Liu Fangzhou Jin Electrochemical Intelligent Recognition of Mineral Materials Based on Superpixel Image Segmentation International Journal of Analytical Chemistry |
| 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 |
| url | http://dx.doi.org/10.1155/2022/6755771 |
| work_keys_str_mv | AT weipingliu electrochemicalintelligentrecognitionofmineralmaterialsbasedonsuperpixelimagesegmentation AT fangzhoujin electrochemicalintelligentrecognitionofmineralmaterialsbasedonsuperpixelimagesegmentation |