eprintid: 8180
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/81/80
datestamp: 2023-11-09 16:20:02
lastmod: 2023-11-09 16:20:02
status_changed: 2023-11-09 16:11:59
type: article
metadata_visibility: show
creators_name: Hittawe, M.M.
creators_name: Sidibé, D.
creators_name: Beya, O.
creators_name: Mériaudeau, F.
title: Machine vision for timber grading singularities detection and applications
ispublished: pub
keywords: Classification (of information); Cracks; Defects; Feature extraction; Forestry; Learning systems; Timber; Wood, Automated methods; Bag of words; Depth ratio; Image computation; Juvenile woods; Mechanical behavior; Mechanical requirements; Singularities detection, Computer vision
note: cited By 3
abstract: 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.
date: 2017
publisher: SPIE
official_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
id_number: 10.1117/1.JEI.26.6.063015
full_text_status: none
publication: Journal of Electronic Imaging
volume: 26
number: 6
refereed: TRUE
issn: 10179909
citation:   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).   ISSN 10179909