Feature extraction for welding defect image based on contourlet transform and kernel principal component analysis

In order to further improve the accuracy and efficiency of welding defect recognition, a method was proposed to extract feature of welding defect image based on contourlet transform and kernel principal component analysis(KPCA) by chaotic particle swarm optimization(CPSO). Firstly, multi-scale decomposition of welding defect images was performed by contourlet transform. Low-frequency components and high-frequency components in a certain direction were extracted. Then, features of training samples and testing samples of welding defects were extracted using KPCA by CPSO, respectively. Finally, the type of welding defect testing samples was determined according to the Euclidean distance between features of training samples and features of testing samples. A large number of experimental results show that, compared with the feature extraction method based on KPCA and the feature extraction method based on the combination of wavelet transform and KPCA, the proposed method can extract feature more completely and has higher recognition rate and operating speed.