Defect detection in textured materials using Gabor filters

Abstract

Vision-based inspection of industrial materials such as textile webs, paper or wood requires the development of defect segmentation techniques based on texture analysis. In this work, a multi-channel filtering technique that imitates the early human vision process is applied to images captured online. This new approach uses Bernoulli's rule of combination for integrating images from different channels. Physical image size and yarn impurities are used as key parameters for tuning the sensitivity of the proposed algorithm. Several real fabric samples along with the result of segmented defects are presented. The results achieved show that the developed algorithm is robust, scalable and computationally efficient for detection of local defects in textured materials.