Wood Materials Defects Detection Using Image Block Percentile Color Histogram and Eigenvector Texture Feature

DOI

10.2991/icismme-15.2015.163How to use a DOI?

Keywords

Wood materials; defects detection; percentile color histogram; singular value decomposition; image block feature; support vector machine (SVM).

Abstract

To automatic detect wood surface defects, a method based on image block percentile color histogram and eigenvector texture feature classification is proposed. Firstly, a wood surface image is divided into several same size image blocks. Secondly, for each image block, a percentile color histogram is calculated as image block color feature. Meanwhile, singular value decomposition (SVD) is adopted to extract k-max eigenvectors as image block texture feature. Then the percentile color histogram and eigenvector texture feature is combined to a feature vector for image block representation. Finally, a support vector machine (SVM) classifier is trained and used to determine which image block is sound or defect wood. The experimental results show that the proposed method can effectively detect wood surface defects, especially the knot type defects.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Weiwei Song
AU  - Tianyi Chen
AU  - Zhenghua Gu
AU  - Wen Gai
AU  - Weikai Huang
AU  - Bin Wang
PY  - 2015/07
DA  - 2015/07
TI  - Wood Materials Defects Detection Using Image Block Percentile Color Histogram and Eigenvector Texture Feature
BT  - Proceedings of the First International Conference on Information Sciences, Machinery, Materials and Energy
PB  - Atlantis Press
SP  - 779
EP  - 783
SN  - 1951-6851
UR  - https://doi.org/10.2991/icismme-15.2015.163
DO  - 10.2991/icismme-15.2015.163
ID  - Song2015/07
ER  -