DocumentCode
152569
Title
Classification of KNOT defect types
Author
Cetiner, Sbrahim ; Var, A. Ali ; Cetiner, H.
Author_Institution
Keciborlu M.Y.O. Elektron. Teknolojisi, Suleyman Demirel Univ., Isparta, Turkey
fYear
2014
fDate
23-25 April 2014
Firstpage
1086
Lastpage
1089
Abstract
In this study, the experimental studies were carried out on a database containing the types of wood knot. After preprocessing on the images in the database, specific features to knot were obtained using wavelet moments feature extraction algorithm. Type description is carried out with KNN classification algorithm by selecting most distinguishing the approximation coefficients on these features. In conclusion, knot images could be classified with the success rate of 98%.
Keywords
feature extraction; image classification; wavelet transforms; wood; KNN classification algorithm; approximation coefficients; image preprocessing; knot defect types classification; knot image classification; wavelet moments feature extraction algorithm; wood knot; Approximation methods; Bagging; Classification algorithms; Conferences; Databases; Feature extraction; Signal processing; Approximation Coefficients; KNN Classification; Knot types; Wavelet Moment;
fLanguage
English
Publisher
ieee
Conference_Titel
Signal Processing and Communications Applications Conference (SIU), 2014 22nd
Conference_Location
Trabzon
Type
conf
DOI
10.1109/SIU.2014.6830422
Filename
6830422
Link To Document