Semantic air segmentation in core with noise reduction and post-processing
This paper presents results on semantic air segmentation for digital concrete cores. Brightness normalization and noise reduction were used as image preprocessing. To increase the efficiency of segmentation, a method is proposed for segmentation in three directions, followed by summing the resulting...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Tomsk Polytechnic University
2024-06-01
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| Series: | Известия Томского политехнического университета: Промышленная кибернетика |
| Subjects: | |
| Online Access: | https://indcyb.ru/journal/article/view/53/48 |
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| Summary: | This paper presents results on semantic air segmentation for digital concrete cores. Brightness normalization and noise reduction were used as image preprocessing. To increase the efficiency of segmentation, a method is proposed for segmentation in three directions, followed by summing the resulting masks and using a median filter. The resulting image masks have a three-dimensional structure and can be used to generate a training dataset for 3D convolutional neural network architectures. The method proposed can be used as well to enlarge images in the training set if data labeling takes a long time. |
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| ISSN: | 2949-5407 |