Classification of wood defect images using local binary pattern variants
This paper presents an analysis of the statistical texture representation of the Local Binary Pattern (LBP) variants in the classification of wood defect images. The basic and variants of the LBP feature set that was constructed from a stage of feature extraction processes with the Basic LBP, Rotati...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Universitas Ahmad Dahlan
2020-03-01
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| Series: | IJAIN (International Journal of Advances in Intelligent Informatics) |
| Subjects: | |
| Online Access: | http://ijain.org/index.php/IJAIN/article/view/392 |
| _version_ | 1817989557107294208 |
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| author | Rahillda Nadhirah Norizzaty Rahiddin Ummi Rabaah Hashim Nor Haslinda Ismail Lizawati Salahuddin Ngo Hea Choon Siti Normi Zabri |
| author_facet | Rahillda Nadhirah Norizzaty Rahiddin Ummi Rabaah Hashim Nor Haslinda Ismail Lizawati Salahuddin Ngo Hea Choon Siti Normi Zabri |
| author_sort | Rahillda Nadhirah Norizzaty Rahiddin |
| collection | DOAJ |
| description | This paper presents an analysis of the statistical texture representation of the Local Binary Pattern (LBP) variants in the classification of wood defect images. The basic and variants of the LBP feature set that was constructed from a stage of feature extraction processes with the Basic LBP, Rotation Invariant LBP, Uniform LBP, and Rotation Invariant Uniform LBP. For significantly discriminating, the wood defect classes were further evaluated with the use of different classifiers. By comparing the results of the classification performances that had been conducted across the multiple wood species, the Uniform LBP was found to have demonstrated the highest accuracy level in the classification of the wood defects. |
| first_indexed | 2024-04-14T00:48:28Z |
| format | Article |
| id | doaj.art-f4d2851b61ae45dea787c060bd854a8c |
| institution | Directory Open Access Journal |
| issn | 2442-6571 2548-3161 |
| language | English |
| last_indexed | 2024-04-14T00:48:28Z |
| publishDate | 2020-03-01 |
| publisher | Universitas Ahmad Dahlan |
| record_format | Article |
| series | IJAIN (International Journal of Advances in Intelligent Informatics) |
| spelling | doaj.art-f4d2851b61ae45dea787c060bd854a8c2022-12-22T02:21:55ZengUniversitas Ahmad DahlanIJAIN (International Journal of Advances in Intelligent Informatics)2442-65712548-31612020-03-0161364510.26555/ijain.v6i1.392132Classification of wood defect images using local binary pattern variantsRahillda Nadhirah Norizzaty Rahiddin0Ummi Rabaah Hashim1Nor Haslinda Ismail2Lizawati Salahuddin3Ngo Hea Choon4Siti Normi Zabri5Centre for Advanced Computing Technology, Universiti Teknikal Malaysia MelakaCentre for Advanced Computing Technology, Universiti Teknikal Malaysia MelakaCentre for Advanced Computing Technology, Universiti Teknikal Malaysia MelakaCentre for Advanced Computing Technology, Universiti Teknikal Malaysia MelakaCentre for Advanced Computing Technology, Universiti Teknikal Malaysia MelakaCentre for Telecommunication Research & Innovation, Universiti Teknikal Malaysia MelakaThis paper presents an analysis of the statistical texture representation of the Local Binary Pattern (LBP) variants in the classification of wood defect images. The basic and variants of the LBP feature set that was constructed from a stage of feature extraction processes with the Basic LBP, Rotation Invariant LBP, Uniform LBP, and Rotation Invariant Uniform LBP. For significantly discriminating, the wood defect classes were further evaluated with the use of different classifiers. By comparing the results of the classification performances that had been conducted across the multiple wood species, the Uniform LBP was found to have demonstrated the highest accuracy level in the classification of the wood defects.http://ijain.org/index.php/IJAIN/article/view/392automated visual inspectiondefect detectionwood inspectionwood defect detectionlocal binary pattern |
| spellingShingle | Rahillda Nadhirah Norizzaty Rahiddin Ummi Rabaah Hashim Nor Haslinda Ismail Lizawati Salahuddin Ngo Hea Choon Siti Normi Zabri Classification of wood defect images using local binary pattern variants IJAIN (International Journal of Advances in Intelligent Informatics) automated visual inspection defect detection wood inspection wood defect detection local binary pattern |
| title | Classification of wood defect images using local binary pattern variants |
| title_full | Classification of wood defect images using local binary pattern variants |
| title_fullStr | Classification of wood defect images using local binary pattern variants |
| title_full_unstemmed | Classification of wood defect images using local binary pattern variants |
| title_short | Classification of wood defect images using local binary pattern variants |
| title_sort | classification of wood defect images using local binary pattern variants |
| topic | automated visual inspection defect detection wood inspection wood defect detection local binary pattern |
| url | http://ijain.org/index.php/IJAIN/article/view/392 |
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