Method of interval fusion with preference aggregation in brightness thresholds selection for automatic weld surface defects recognition; Measurement; Vol. 236
| Parent link: | Measurement.— .— Amsterdam: Elsevier Science Publishing Company Inc. Vol. 236.— 2024.— Article number 114969, 18 p. |
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| Awdur Corfforaethol: | |
| Awduron Eraill: | |
| Crynodeb: | Title screen Checking the weld joint quality is carried out during visual inspection process and depends significantly on an operator’s experience. In the article, an approach is proposed to automatically determination of geometric attributes and classification of a defective region, where the segmentation of the analyzed optical image of a weld (i.e., its division into defective and defect-free regions) is carried out using a combination of two procedures: region growing and edge detecting. The brightness thresholds for these procedures are calculated by the robust method of interval fusion with preference aggregation (IF&PA) based on the analysis of the fragmented image histograms and its gradients. The results obtained by the two procedures are consolidated to obtain a refined defective region. Testing the proposed technology on 150 real optical images showed its ability to identify geometric features and detect certain weld defects with higher accuracy compared to conventional Otsu and k-means methods Текстовый файл AM_Agreement |
| Iaith: | Saesneg |
| Cyhoeddwyd: |
2024
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| Pynciau: | |
| Mynediad Ar-lein: | https://doi.org/10.1016/j.measurement.2024.114969 |
| Fformat: | Electronig Pennod Llyfr |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=673341 |
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| 200 | 1 | |a Method of interval fusion with preference aggregation in brightness thresholds selection for automatic weld surface defects recognition |f S. V. Muravyov, Duc Cuong Nguyen | |
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| 330 | |a Checking the weld joint quality is carried out during visual inspection process and depends significantly on an operator’s experience. In the article, an approach is proposed to automatically determination of geometric attributes and classification of a defective region, where the segmentation of the analyzed optical image of a weld (i.e., its division into defective and defect-free regions) is carried out using a combination of two procedures: region growing and edge detecting. The brightness thresholds for these procedures are calculated by the robust method of interval fusion with preference aggregation (IF&PA) based on the analysis of the fragmented image histograms and its gradients. The results obtained by the two procedures are consolidated to obtain a refined defective region. Testing the proposed technology on 150 real optical images showed its ability to identify geometric features and detect certain weld defects with higher accuracy compared to conventional Otsu and k-means methods | ||
| 336 | |a Текстовый файл | ||
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| 461 | 1 | |t Measurement |c Amsterdam |n Elsevier Science Publishing Company Inc. | |
| 463 | 1 | |t Vol. 236 |v Article number 114969, 18 p. |d 2024 | |
| 610 | 1 | |a электронный ресурс | |
| 610 | 1 | |a труды учёных ТПУ | |
| 610 | 1 | |a weld defect | |
| 610 | 1 | |a visual inspection | |
| 610 | 1 | |a image segmentation | |
| 610 | 1 | |a region growing | |
| 610 | 1 | |a brightness threshold | |
| 610 | 1 | |a edge detection | |
| 610 | 1 | |a canny operator | |
| 610 | 1 | |a interval fusion | |
| 610 | 1 | |a preference aggregation | |
| 610 | 1 | |a kemeny rule | |
| 700 | 1 | |a Muravyov (Murav’ev) |b S. V. |c specialist in the field of control and measurement equipment |c Professor of Tomsk Polytechnic University,Doctor of technical sciences |f 1954- |g Sergey Vasilyevich |9 15440 | |
| 701 | 0 | |a Duc Cuong Nguyen | |
| 712 | 0 | 2 | |a National Research Tomsk Polytechnic University |c (2009- ) |9 27197 |4 570 |
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