Unsupervised novelty detection using Gabor filters for defect segmentation in textures

Abstract

Gabor wavelets are applied to develop an unsupervised novelty method for defect detection and segmentation that is fully automatic and free of any adjustable parameter. The algorithm combines the Gabor analysis of the sample image with a statistical analysis of the wavelet coefficients corresponding to each detail. The statistical distribution of the coefficients corresponding to the defect-free background texture is calculated from the coefficient's distribution of the sample under inspection. Once the background texture features are estimated, a threshold is automatically fixed and applied to all the details, whose information is merged into a single binary output image in which the defect appears segmented from the background. The method is applicable to random, nonperiodic, and periodic textures. Since all the information to inspect a sample is obtained from the sample itself, the method is proof against heterogeneities between different samples of the material, in-plane positioning errors, scale variations, and lack of homogeneous illumination. Experimental results are presented. Some results are compared with other unsupervised methods designed for defect segmentation in periodic textures.