Segmentation of defects in textile fabric

For fast inspection of defects in textile fabric the complexity of calculations has to be reduced, in order to limit the system costs. Additionally, algorithms which are suitable for migration into hardware have to be chosen. Therefore, in this work a segmentation algorithm using first order statistics is applied. Preprocessing includes a logarithmic greyscale intransformation to obtain insensitivity to illumination changes. Afterwards texture features are extracted by a set of linear filters, which consider local neighbourhood relations. The filtered images are evaluated by histograms being calculated on a window grid. Finally, the histograms are classified by a Perceptron Net trained by Backpropagation. An interactive Teach-in program is provided to adapt the system to different kinds of textile fabric and appearances of defects.