Performance evaluation of multivariate texture descriptor for classification of timber defect
This paper presents performance evaluation of texture features based on orientation independent Grey Level Dependence Matrix (GLDM) for the classification of timber defects and clear wood. A series of processes including feature extraction and feature analysis were implemented to facilitate data und...
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2016
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my.utm.722512017-11-23T01:37:08Z http://eprints.utm.my/id/eprint/72251/ Performance evaluation of multivariate texture descriptor for classification of timber defect Hashim, U. R. Hashim, S. Z. M. Muda, A. K. QA75 Electronic computers. Computer science This paper presents performance evaluation of texture features based on orientation independent Grey Level Dependence Matrix (GLDM) for the classification of timber defects and clear wood. A series of processes including feature extraction and feature analysis were implemented to facilitate data understanding in order to construct a good feature set that could significantly discriminate between defects and clear wood classes. To further evaluate the discrimination capability of the features extracted, classification experiments were performed on defects and clear wood images of Meranti timber species using common classifiers. The classification performance were further compared between other timber species which are Merbau, KSK and Rubberwood. Results from the analysis reveals that the proposed texture features provide better performance than other feature sets from related works, performs acceptably well across various defect types and across multiple timber species. Elsevier GmbH 2016 Article PeerReviewed Hashim, U. R. and Hashim, S. Z. M. and Muda, A. K. (2016) Performance evaluation of multivariate texture descriptor for classification of timber defect. Optik, 127 (15). pp. 6071-6080. ISSN 0030-4026 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84966430665&doi=10.1016%2fj.ijleo.2016.04.005&partnerID=40&md5=e2ddcf005ebe61f03a9cc7e70117b90d |
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QA75 Electronic computers. Computer science Hashim, U. R. Hashim, S. Z. M. Muda, A. K. Performance evaluation of multivariate texture descriptor for classification of timber defect |
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This paper presents performance evaluation of texture features based on orientation independent Grey Level Dependence Matrix (GLDM) for the classification of timber defects and clear wood. A series of processes including feature extraction and feature analysis were implemented to facilitate data understanding in order to construct a good feature set that could significantly discriminate between defects and clear wood classes. To further evaluate the discrimination capability of the features extracted, classification experiments were performed on defects and clear wood images of Meranti timber species using common classifiers. The classification performance were further compared between other timber species which are Merbau, KSK and Rubberwood. Results from the analysis reveals that the proposed texture features provide better performance than other feature sets from related works, performs acceptably well across various defect types and across multiple timber species. |
format |
Article |
author |
Hashim, U. R. Hashim, S. Z. M. Muda, A. K. |
author_facet |
Hashim, U. R. Hashim, S. Z. M. Muda, A. K. |
author_sort |
Hashim, U. R. |
title |
Performance evaluation of multivariate texture descriptor for classification of timber defect |
title_short |
Performance evaluation of multivariate texture descriptor for classification of timber defect |
title_full |
Performance evaluation of multivariate texture descriptor for classification of timber defect |
title_fullStr |
Performance evaluation of multivariate texture descriptor for classification of timber defect |
title_full_unstemmed |
Performance evaluation of multivariate texture descriptor for classification of timber defect |
title_sort |
performance evaluation of multivariate texture descriptor for classification of timber defect |
publisher |
Elsevier GmbH |
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2016 |
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http://eprints.utm.my/id/eprint/72251/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-84966430665&doi=10.1016%2fj.ijleo.2016.04.005&partnerID=40&md5=e2ddcf005ebe61f03a9cc7e70117b90d |
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1643656392580530176 |
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13.160551 |