Recognition Ability of Interval Fusion with Preference Aggregation in Weld Defects Images Analysis; Global trends in Testing, Diagnostics & Inspection for 2030
| Parent link: | Global trends in Testing, Diagnostics & Inspection for 2030.— 2020.— [P. 271-276] |
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| Samenvatting: | Title screen This paper describes a potential applicability of the interval fusion with preference aggregation (IF&PA) approach to the weld image segmentation as the key stage in recognizing a welding joint defective region. In the proposed method, the weld image is divided into a series of equal horizontal bands. For each band, an intensity histogram is plotted, using which the lower and upper bounds are defined for the intervals, which are expected to characterize the defect (foreground) and defect-free (background) areas. The intervals are represented by inrankings forming the foreground and background preference profiles. The Kemeny ranking algorithm is applied to the two profiles in order to determine the best representative points values (in RGB code) of the foreground and background areas. The values serve then as seed parameters of the region growing algorithm applied to distinguish defect and background regions during the segmentation. This approach was tested in segmenting a number of typical weld defect images. The experimental results showed that the proposed approach allows to accurately separate the defect-free region from the defective one. |
| Taal: | Engels |
| Gepubliceerd in: |
2020
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| Online toegang: | https://www.imeko.org/publications/tc10-2020/IMEKO-TC10-2020-039.pdf |
| Formaat: | Elektronisch Hoofdstuk |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=665069 |
| Samenvatting: | Title screen This paper describes a potential applicability of the interval fusion with preference aggregation (IF&PA) approach to the weld image segmentation as the key stage in recognizing a welding joint defective region. In the proposed method, the weld image is divided into a series of equal horizontal bands. For each band, an intensity histogram is plotted, using which the lower and upper bounds are defined for the intervals, which are expected to characterize the defect (foreground) and defect-free (background) areas. The intervals are represented by inrankings forming the foreground and background preference profiles. The Kemeny ranking algorithm is applied to the two profiles in order to determine the best representative points values (in RGB code) of the foreground and background areas. The values serve then as seed parameters of the region growing algorithm applied to distinguish defect and background regions during the segmentation. This approach was tested in segmenting a number of typical weld defect images. The experimental results showed that the proposed approach allows to accurately separate the defect-free region from the defective one. |
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