We present a new method to segment images of structured surfaces from illumination series, i.e. sets of images of an object recorded with different lighting settings. We use a parallel light source whose angle of incidence is described by the azimuth and the elevation angle. Depending on the surface topography, characteristic patterns are described by the intensities viewed by the camera depending on the illumination direction. The segmentation itself is based on cluster analysis in a multi-dimensional feature space. The resulting classes correspond with the identified segments of the surface image. A crucial step within this approach is the definition of meaningful features. We focus on features that can be extracted from the signal described by the intensities at a single surface location depending on the illumination direction. We investigate features based on moments of this intensity signal as well as on its frequency decomposition with respect to the illumination direction. Furthermore, we show that features of this kind can be used to robustly segment a wide variety of textures on structured surfaces. In any case, since no spatial neighbourhood is utilized to compute the features, i.e.
"averaging" takes place only in illumination domain, no spatial resolution must be sacrificed. Consequently, even very small regions can reliably be segmented, as is necessary when defects are to be detected.