# Review on Computer Aided Weld Defect Detection from Radiography Images
## 5 Defect Classification
### New Methods
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Ref** & **Base** & **Pre-Processing** & \
\begin{tabular}{c} **Feature** \\ **Number** \\ **Type** \\ \end{tabular} &
\begin{tabular}{c} **Feature** \\ **Selection** \\ \end{tabular} & **Classifier** & **Results** & **Evaluation** \\ \hline
[30] & Line profile & - & \begin{tabular}{c} 25 \\ profile \\ measurements \\ \end{tabular} & - & \begin{tabular}{c} Fuzzy \\ k-NN \\ Fuzzy (C-means) \\ \end{tabular} & \
\begin{tabular}{c} 6.01\% \\ 18.67\% \\ \end{tabular} &
\begin{tabular}{c} Missing \\ rate False alarm \\ \end{tabular} \\ \hline
[31] & Line profile & - & \
\begin{tabular}{c} 3 \\ profile \\ measurements \\ \end{tabular} & - &
\begin{tabular}{c} Fuzzy \\ k-NN \\ Fuzzy C-means \\ \end{tabular} & - & False alarm \\ \hline
[33] & Line profile & - & \begin{tabular}{c} 36 \\ profile \\ measurements \\ \end{tabular} & - & \
\begin{tabular}{c} NN; \\ Decision-tree \\ \end{tabular} & complex &
\begin{tabular}{c} Generalization; \\ Quantity; \\ Cost; \\ etc. \\ \end{tabular} \\ \hline
[32] & Line profile & - & \
\begin{tabular}{c} 3 \\ profile \\ measurements \\ \end{tabular} & - &
\begin{tabular}{c} Fuzzy \\ reasoning \\ \end{tabular} & 100\% & Accuracy \\ \hline
[9] & 2D image & \begin{tabular}{c} Noise removal; \\ Contrast \\ improve, \\ Defect segment \\ \end{tabular} & 12 & - & \
\begin{tabular}{c} Fuzzy \\ k-NN; \\ numeric \\ \end{tabular} & 92.39\% &
\begin{tabular}{c} Bootstrap \\ accuracy \\ \end{tabular} \\ \hline
[41] & 2D image & \begin{tabular}{c} Potential \\ defect segment \\ \end{tabular} & 148 & \begin{tabular}{c} SPS \\ texture \\ \end{tabular} & \
\begin{tabular}{c} Polynomial; \\ Mahalanobis; Nearest neighbor \\ \end{tabular} & 90.91\% &
\begin{tabular}{c} Area \\ under the ROC \\ \end{tabular} \\ \hline
[35] & 2D image & - & \begin{tabular}{c} 12 \\ geometric \\ \end{tabular} & Filter methods & \
\begin{tabular}{c} Fuzzy \\ k-NN; \\ MLP \\ \end{tabular} & 0.9205 &
\begin{tabular}{c} Bootstrap \\ accuracy \\ \end{tabular} \\ \hline
[36] & 2D image & \begin{tabular}{c} Noise removal; \\ contrast \\ improve; \\ \end{tabular} & 4 & - & \
\begin{tabular}{c} Nonlinear \\ pattern classifiers using NN \\ \end{tabular} & complex &
\begin{tabular}{c} Classification \\ performance; \\ reference; \\ Principal \\ components \\ \end{tabular} \\ \hline
[40] & 2D image & \begin{tabular}{c} Noise removal; \\ Enhancer; \\ Segmentation \\ \end{tabular} & 8 & - & \
\begin{tabular}{c} SVM; \\ Fuzzy \\ NN \\ \end{tabular} & 83.3\% &
\begin{tabular}{c} Accuracy \\ rate \\ \end{tabular} \\ \hline
[37] & 2D image & - & 7 & - & \
\begin{tabular}{c} Nonlinear \\ classifier \\ \end{tabular} & 92\% &
\begin{tabular}{c} Bootstrap \\ accuracy \\ \end{tabular} \\ \hline
[29] & 2D image & \
\begin{tabular}{c} Defect segment \\ \end{tabular} & 43 geometric+ texture & SBS &
\begin{tabular}{c} SVM; \\ ANN; \\ k-NN \\ \end{tabular} & 98.51\% & cross validation accuracy \\ \hline
[15] & 2D image & \
\begin{tabular}{c} Noise removal; \\ Contrast \\ improve \\ \end{tabular} & 8,44,44 & - & ANN & 86.1\% &
\begin{tabular}{c} Classification \\ accuracy \\ \end{tabular} \\ \hline
[12] & 2D image & \begin{tabular}{c} Noise removal; \\ contrast \\ improve, \\ image \\ \end{tabular} & 16 texture & \
\begin{tabular}{c} 16 texture \\ geometric \\ 72 geometric \\ \end{tabular} & - & ANN & 87.34\% &
\begin{tabular}{c} Classification \\ accuracy \\ \end{tabular} \\ \hline
[15] & 1D signal & - & \
\begin{tabular}{c} 13 MFCCs* \\ features \\ \end{tabular} & - & ANN & 100\% &
\begin{tabular}{c} Recognition \\ rates \\ \end{tabular} \\ \hline
[6] & 2D image & \begin{tabular}{c} Noise removal; \\ Contrast \\ improve \\ Defect segment \\ \end{tabular} & 12 & - & \
\begin{tabular}{c} ANN \\ ANFIS \\ \end{tabular} & 100\% &
\begin{tabular}{c} Classification \\ accuracy \\ \end{tabular} \\ \hline
[11] & \begin{tabular}{c} Power \\ Density Spectra \\ \end{tabular} & \begin{tabular}{c} Enhancement; \\ image \\ segmentation \\ \end{tabular} & \
\begin{tabular}{c} MFCCs+ \\ polynomial \\ features \\ \end{tabular} & - & ANN & 100\% &
\begin{tabular}{c} Probability \\ of detection \\ False \\ alarm rate \\ \end{tabular} \\ \hline
[14] & 2D image & \begin{tabular}{c} Noise removal; \\ Contrast \\ improve; \\ image \\ segment \\ \end{tabular} & \
\begin{tabular}{c} Energy \\ of the wavelet coefficients \\ segment \\ \end{tabular} & - & SVM & 99.5\% &
\begin{tabular}{c} Classification \\ rate \\ \end{tabular} \\ \hline
[34] & 2D image & \
\begin{tabular}{c} Location \\ of the weld bead region \\ \end{tabular} & 8 & - & MLP & 88.6\% &
\begin{tabular}{c} Accuracy \\ 87.5\% \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}