Surface defect detection with histogram-based texture features

In this paper the performance of two histogram-based texture analysis techniques for surface defect detection is evaluated. These techniques are the co-occurrence matrix method and the local binary pattern method. Both methods yield a set of texture features that are computed from a small image window. The unsupervised segmentation procedure is used in the experiments. It is based on the statistical self-organizing map algorithm that is trained only with fault-free surface samples. Results of experiments with both feature sets are good and there is Ilo clear difference in their performances. The differences are found in their computational requirements: where the features of the local binary pattern method are better in several aspects.