TY  - JOUR
T1  - Automatic Classification Vision Based System for Welds Joints Defects
Using Support Vector Machine (SVMs)
AU - Shah, Hairol Nizam Mohd AU - Sulaiman, Marizan AU - Shukor, Ahmad Zaki 
JO  - International Journal of Soft Computing
VL  - 12
IS  - 1
SP  - 66
EP  - 71
PY  - 2017
DA  - 2001/08/19
SN  - 1816-9503
DO  - ijscomp.2017.66.71
UR  - https://makhillpublications.co/view-article.php?doi=ijscomp.2017.66.71
KW  - Classify
KW  -CCD camera
KW  -gray absolute
KW  -training data
KW  -Support Vector Machine (SVMs)
AB  - The goal of this study is to classify the welds joint defects capture by CCD camera into three
categories which are good welds, excess welds and insufficient welds in three weld joint shapes; straight lines,
curve and tooth saw. Firstly, we extract the features characteristic from the input images represent in 2D gray
values of coocurrence matrix and gray absolute histogram of edge amplitude consists of energy, correlation,
homogeneity and contrast. Then zooming input image by 0.5 to calculated the next characteristic feature values.
Furthermore, use the Support Vector Machine (SVMs) classifier to classify the welds joint defect according to
the feature vector belongs to the same categories as the training data. The experimental result taken from 45
welds joints samples in three welds joint shapes; straight lines, curve and tooth saw where 3 samples as training
set and 2 samples as testing set show that the proposal approach able to classify the welds joint defects
effective automatically.
ER  -