Texture-based Surface Segmentation Using Second-order Statistics of Illumination Series

Abstract

Many automated visual inspection applications rely on a segmentation of surfaces into meaningful regions, for instance into defective and non-defective areas. This paper presents a segmentation approach based on illumination series, by which we denote a set of images taken under variable directional illumination. We show that co-occurrence matrices calculated from the series of images enable the extraction of suitable features for a texture-based segmentation of the surface. Depending on the selected displacement vector, the co-occurrence matrices computed within a neighbor- hood contain information about spatial variations of the surface or about the average reflection properties. The method is developed on synthetic images and is then demon- strated with cutting inserts to segment areas featuring abrasion.