Directionality detection in compositional textures

This paper deals with the problem of recovering directionality from compositional textures. It presents a new method that has proved to be effective for detection and classification of leather defects in the manufacturing industry. A flow field, estimated from texture, is represented as a set of projection coefficients onto suitable elementary basis vector fields. Since these bases are neither orthogonal nor complete, the coefficients must be estimated using a global optimization criterion. The method performs this optimization using a neural network. A region growing approach, based on single iterations of the same neural network, expands patches corresponding to oriented structures and produces the final segmentation map.