Rotation invariant classification of rough surfaces

Rotation of a rough, textured surface will not produce a simple rotation of the image texture. It follows that where image texture is a function of surface topography, existing rotation invariant texture classification algorithms are not robust to surface rotation. The effect of surface rotation on the observed image is analyzed using an existing theory, a novel scheme to stabilize classification accuracy is proposed and evaluated. The scheme uses photometric stereo to estimate the surface derivatives, which are then used as the input to a classifier. Simulations indicate that, where the level of image noise is moderate or low, the approach is successful in maintaining classification accuracy. Furthermore, in some circumstances, the extra information used by the algorithm allows classification accuracy superior to that based on one image alone, even without rotation.