Combining gradient and albedo data for rotation invariant classification of 3D surface texture

Abstract

We present a new texture classification scheme which is invariant to surface-rotation. Many texture classification approaches have been presented in the past that are image-rotation invariant. However, image rotation is not necessarily the same as surface rotation. We have therefore developed a classifier that uses invariants that are derived from surface properties rather than image properties. Previously we developed a scheme that used surface gradient (normal) fields estimated using photometric stereo. In this paper we augment these data with albedo information and also employ an additional feature set: the radial spectrum. We used 30 real textures to test the new classifier. A classification accuracy of 91% was achieved when albedo and gradient 1D polar and radial features were combined. The best performance was also achieved by using 2D albedo and gradient spectra. The classification accuracy is 99%.