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: Mikhail I. Volkov, Polina S. Kargina
Format: Article
Language:English
Published: Tomsk Polytechnic University 2024-06-01
Series:Известия Томского политехнического университета: Промышленная кибернетика
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Online Access:https://indcyb.ru/journal/article/view/53/48
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author Mikhail I. Volkov
Polina S. Kargina
author_facet Mikhail I. Volkov
Polina S. Kargina
author_sort Mikhail I. Volkov
collection DOAJ
description 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.
format Article
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institution OA Journals
issn 2949-5407
language English
publishDate 2024-06-01
publisher Tomsk Polytechnic University
record_format Article
series Известия Томского политехнического университета: Промышленная кибернетика
spelling doaj-art-416d7ece007b48828c85080f021cb2882025-08-20T02:35:01ZengTomsk Polytechnic UniversityИзвестия Томского политехнического университета: Промышленная кибернетика2949-54072024-06-0122394410.18799/29495407/2024/2/53Semantic air segmentation in core with noise reduction and post-processingMikhail I. Volkov0Polina S. Kargina1National Research Tomsk Polytechnic University, Tomsk, Russian FederationNational Research Tomsk Polytechnic University, Tomsk, Russian FederationThis 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.https://indcyb.ru/journal/article/view/53/48semantic segmentation of air poresnoise reductionbm3dpost-processingmedian filter
spellingShingle Mikhail I. Volkov
Polina S. Kargina
Semantic air segmentation in core with noise reduction and post-processing
Известия Томского политехнического университета: Промышленная кибернетика
semantic segmentation of air pores
noise reduction
bm3d
post-processing
median filter
title Semantic air segmentation in core with noise reduction and post-processing
title_full Semantic air segmentation in core with noise reduction and post-processing
title_fullStr Semantic air segmentation in core with noise reduction and post-processing
title_full_unstemmed Semantic air segmentation in core with noise reduction and post-processing
title_short Semantic air segmentation in core with noise reduction and post-processing
title_sort semantic air segmentation in core with noise reduction and post processing
topic semantic segmentation of air pores
noise reduction
bm3d
post-processing
median filter
url https://indcyb.ru/journal/article/view/53/48
work_keys_str_mv AT mikhailivolkov semanticairsegmentationincorewithnoisereductionandpostprocessing
AT polinaskargina semanticairsegmentationincorewithnoisereductionandpostprocessing