Timber defect detection based on systematic feature analysis and one class classifier
Also available in printed version
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| Format: | Doctoral thesis |
| Language: | English |
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Universiti Teknologi Malaysia
2025
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| Online Access: | https://utmik.utm.my/handle/123456789/56590 |
| Abstract | Abstract here |
| _version_ | 1855605551728164864 |
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| author | Ummi Raba'ah Hashim |
| author2 | Siti Zaiton Mohd. Hashim, supervisor |
| author_facet | Siti Zaiton Mohd. Hashim, supervisor Ummi Raba'ah Hashim |
| author_sort | Ummi Raba'ah Hashim |
| description | Also available in printed version |
| format | Doctoral thesis |
| id | utm-123456789-56590 |
| institution | Universiti Teknologi Malaysia |
| language | English |
| publishDate | 2025 |
| publisher | Universiti Teknologi Malaysia |
| record_format | DSpace |
| record_pdf | Restricted |
| spelling | utm-123456789-565902025-08-20T23:29:13Z Timber defect detection based on systematic feature analysis and one class classifier Ummi Raba'ah Hashim Siti Zaiton Mohd. Hashim, supervisor Computing Also available in printed version Substantial research effort has been done in the automation of timber defect detection to improve the quality of timber products, optimise raw material resources, increase productivity and reduce error related to human labour. This study extends the work on automated inspection of timber boards to Malaysian timber species hoping that the outcome will benefit the local wood product industries. This study aims to propose a timber surface defect detection approach which is robust in detecting various defects on multiple timber species using significant texture features, validated using data from local timber species. In the experiments, defective samples from Malaysian Hardwood are collected and labelled under supervision of industry experts. Additionally, this work gives new insight into the characterisation of timber defect images by using statistical texture from orientation independent Grey Level Dependence Matrix (GLDM) with appropriate parameter analysis. A Systematic Feature Analysis (SFA) which includes exploratory and confirmatory multivariate analysis was performed to investigate the discriminative power of the proposed feature set. The SFA produces a feature set of timber surface defects capable of providing significant discrimination between defects and clear wood classes. Finally, a new concept in the domain of timber defect detection based on outlier detection concept was introduced to overcome the problem of imbalanced data. This study proposes a robust Mahalanobis one class classifier (MC) with Fast Minimum Covariance Determinant estimator (MC-FMCD) for species independent timber defect detection. The experimental results show that the proposed approach achieved superior performance over the classical Mahalanobis Distance (MD) and robust in detecting many types of defects across timber species atiff UTM 319 p. Thesis (Ph.D (Sains Komputer) - Universiti Teknologi Malaysia, 2015 2025-03-17T04:28:54Z 2025-03-17T04:28:54Z 2015 Doctoral thesis https://utmik.utm.my/handle/123456789/56590 valet-20170220-163155 vital:96176 ENG Closed Access UTM Complete Unpublished application/pdf Universiti Teknologi Malaysia |
| spellingShingle | Computing Ummi Raba'ah Hashim Timber defect detection based on systematic feature analysis and one class classifier |
| thesis_level | PhD |
| title | Timber defect detection based on systematic feature analysis and one class classifier |
| title_full | Timber defect detection based on systematic feature analysis and one class classifier |
| title_fullStr | Timber defect detection based on systematic feature analysis and one class classifier |
| title_full_unstemmed | Timber defect detection based on systematic feature analysis and one class classifier |
| title_short | Timber defect detection based on systematic feature analysis and one class classifier |
| title_sort | timber defect detection based on systematic feature analysis and one class classifier |
| topic | Computing |
| url | https://utmik.utm.my/handle/123456789/56590 |
| work_keys_str_mv | AT ummirabaahhashim timberdefectdetectionbasedonsystematicfeatureanalysisandoneclassclassifier |