Multivariate Image Analysis in Gaussian Multi-Scale Space for Defect Detection

Inspired by the coarse-to-fine visual perception process of human vision system, a new approach based on Gaussian multi-scale space for defect detection of industrial products was proposed. By selecting different scale parameters of the Gaussian kernel, the multi-scale representation of the original image data could be obtained and used to constitute the multivariate image, in which each channel could represent a perceptual observation of the original image from different scales. The Multivariate Image Analysis (MIA) techniques were used to extract defect features information. The MIA combined Principal Component Analysis (PCA) to obtain the principal component scores of the multivariate test image. The Q-statistic image, derived from the residuals after the extraction of the first principal component score and noise, could be used to efficiently reveal the surface defects with an appropriate threshold value decided by training images. Experimental results show that the proposed method performs better than the gray histogram-based method. It has less sensitivity to the inhomogeneous of illumination, and has more robustness and reliability of defect detection with lower pseudo reject rate.