Orientation-Based Segmentation of Textured Images Using Graph-Cuts

Abstract

In this work we present a hierarchical segmentation algorithm for textured images, where the textures are composed of different number of additively superimposed oriented patterns. The number of superimposed patterns is inferred by evaluating orientation tensor based quantities which can be efficiently computed from tensor invariants such as determinant, minors and trace. Since direct thresholding of these quantities leads to non-robust segmentation results, we propose a graph cut based segmentation approach. Our level dependent energy functions consist of a data term evaluating orientation tensor based quantities, and a smoothness term which assesses smoothness of the segmentation results. We present the robustness of the approach using both synthetic and real image data. © Springer-Verlag Berlin Heidelberg 2013.