Automatic Defect Classification Using Frequency and Spatial Features in a Boosting Scheme

Abstract

An automatic defect classification algorithm is proposed in a boosting manner. The proposed method exploits the histogram of spatial orientation and frequency features. Specifically, the spatial gradient orientations of defect image are accumulated to be a histogram, and they are trained by SVM to construct a classifier. The frequency features are the projection of 2D Haar patterns on the frequency responses. The classifiers using these spatial and frequency features are combined in a boosting manner to improve the classification performance. According to the experiments with 100 training and testing sets, the proposed boosting method improves the classification performance compared with the previous works using optical features such as colors, shapes, and sizes of defects.