Texture inspection for defects using neural networks and support vector machines

Investigates two methods for the detection of defects on textured surfaces using neural networks and support vector machines. Every pixel from the inspection image is characterized by a feature vector, which serves as a local measure of homogeneity of texture. The feature vectors from the gray-level arrangement of neighboring pixels are transformed to eigenspace using Principal Component Analysis (PCA). The transformed features from a predetermined set of training images are used to train the classifier. The trained classifier is used to classes every pixel from inspection image into two-class, i.e. with- or without-defect. The experimental results on real fabric defects show that the proposed scheme can successfully segment the defects from the inspection images.