Machine vision for timber grading singularities detection and applications
This article deals with machine vision techniques applied to timber grading singularities. Timber used for architectural purposes must satisfy certain mechanical requirements, and, therefore, must be mechanically graded to ensure the manufacturer that the product complies with the requirements. Howe...
Saved in:
Main Authors: | , , , |
---|---|
Format: | Article |
Published: |
SPIE
2017
|
Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85034832135&doi=10.1117%2f1.JEI.26.6.063015&partnerID=40&md5=54912d6752d27c8ad165f93ab3debf0b http://eprints.utp.edu.my/19287/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my.utp.eprints.19287 |
---|---|
record_format |
eprints |
spelling |
my.utp.eprints.192872018-05-03T02:10:04Z Machine vision for timber grading singularities detection and applications Hittawe, M.M. Sidibé, D. Beya, O. Mériaudeau, F. This article deals with machine vision techniques applied to timber grading singularities. Timber used for architectural purposes must satisfy certain mechanical requirements, and, therefore, must be mechanically graded to ensure the manufacturer that the product complies with the requirements. However, the timber material has many singularities, such as knots, cracks, and presence of juvenile wood, which influence its mechanical behavior. Thus, identifying those singularities is of great importance. We address the problem of timber defects segmentation and classification and propose a method to detect timber defects such as cracks and knots using a bag-of-words approach. Extensive experimental results show that the proposed methods are efficient and can improve grading machines performances. We also propose an automated method for the detection of transverse knots, which allows the computation of knot depth ratio (KDR) images. Finally, we propose a method for the detection of juvenile wood regions based on tree rings detection and the estimation of the tree's pith. The experimental results show that the proposed methods achieve excellent results for knots detection, with a recall of 0.94 and 0.95 on two datasets, as well as for KDR image computation and juvenile timber detection. © 2017 SPIE and IS&T. SPIE 2017 Article PeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85034832135&doi=10.1117%2f1.JEI.26.6.063015&partnerID=40&md5=54912d6752d27c8ad165f93ab3debf0b Hittawe, M.M. and Sidibé, D. and Beya, O. and Mériaudeau, F. (2017) Machine vision for timber grading singularities detection and applications. Journal of Electronic Imaging, 26 (6). http://eprints.utp.edu.my/19287/ |
institution |
Universiti Teknologi Petronas |
building |
UTP Resource Centre |
collection |
Institutional Repository |
continent |
Asia |
country |
Malaysia |
content_provider |
Universiti Teknologi Petronas |
content_source |
UTP Institutional Repository |
url_provider |
http://eprints.utp.edu.my/ |
description |
This article deals with machine vision techniques applied to timber grading singularities. Timber used for architectural purposes must satisfy certain mechanical requirements, and, therefore, must be mechanically graded to ensure the manufacturer that the product complies with the requirements. However, the timber material has many singularities, such as knots, cracks, and presence of juvenile wood, which influence its mechanical behavior. Thus, identifying those singularities is of great importance. We address the problem of timber defects segmentation and classification and propose a method to detect timber defects such as cracks and knots using a bag-of-words approach. Extensive experimental results show that the proposed methods are efficient and can improve grading machines performances. We also propose an automated method for the detection of transverse knots, which allows the computation of knot depth ratio (KDR) images. Finally, we propose a method for the detection of juvenile wood regions based on tree rings detection and the estimation of the tree's pith. The experimental results show that the proposed methods achieve excellent results for knots detection, with a recall of 0.94 and 0.95 on two datasets, as well as for KDR image computation and juvenile timber detection. © 2017 SPIE and IS&T. |
format |
Article |
author |
Hittawe, M.M. Sidibé, D. Beya, O. Mériaudeau, F. |
spellingShingle |
Hittawe, M.M. Sidibé, D. Beya, O. Mériaudeau, F. Machine vision for timber grading singularities detection and applications |
author_facet |
Hittawe, M.M. Sidibé, D. Beya, O. Mériaudeau, F. |
author_sort |
Hittawe, M.M. |
title |
Machine vision for timber grading singularities detection and applications |
title_short |
Machine vision for timber grading singularities detection and applications |
title_full |
Machine vision for timber grading singularities detection and applications |
title_fullStr |
Machine vision for timber grading singularities detection and applications |
title_full_unstemmed |
Machine vision for timber grading singularities detection and applications |
title_sort |
machine vision for timber grading singularities detection and applications |
publisher |
SPIE |
publishDate |
2017 |
url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85034832135&doi=10.1117%2f1.JEI.26.6.063015&partnerID=40&md5=54912d6752d27c8ad165f93ab3debf0b http://eprints.utp.edu.my/19287/ |
_version_ |
1738656048711991296 |
score |
13.149126 |