TY  - JOUR
T1  - Comparison of Feature Extraction Techniques on Segmented Wood Defects
AU - Ahmad, Wan Siti Halimatul Munirah Wan AU - Tong, Hau-Lee AU - Ng, Hu AU - Yap, Timothy Tzen-Vun AU - Fauzi, Mohammad Faizal Ahmad 
JO  - Research Journal of Applied Sciences
VL  - 11
IS  - 6
SP  - 374
EP  - 379
PY  - 2016
DA  - 2001/08/19
SN  - 1815-932x
DO  - rjasci.2016.374.379
UR  - https://makhillpublications.co/view-article.php?doi=rjasci.2016.374.379
KW  - Segment
KW  -wood
KW  -defect
KW  -crack
KW  -SMV
AB  - This study introduces a analysis on the comparison of feature extraction techniques on the segmented wood defects. The method used is to classify four types of wood defects, namely knot, crack, holes and algae. The wood defect dataset used in this research consisted of 145 images were obtained from various sources. Fuzzy C-Means (FCM) is employed to segment wood defects into four clusters. Six types of feature extraction techniques namely Colour Histogram, Colour Coherence Vector, Local Binary Pattern, Gacfrdfbor Transform, Discrete Wavelet Frame and Gray Level Co-occurrence Matric are employed to describe the image’s feature. The performances of Support Vector Machines (SVM), Bayes Networks and tree-based classifiers are comparedon the different defects and the classifiers’ performances for each extraction technique are investigated. The experiment shows promising results with the highest classification accuracy of 94.5%, achieved by Random Forest classifier using Colour Histogram features. The proposed framework is useful in the automation of the detection of wood defects and is a superior alternative to manual selection and classification in the wood quality control.
ER  -