On the use of gradient space eigenvalues for rotation invariant texture classification

Many image-rotation invariant texture classification approaches have been presented previously. This paper proposes a novel surface-rotation invariant scheme. It uses the eigenvalues of a surface's gradient-space distribution as its features. Unlike the partial derivatives, from which they are computed, these eigenvalue features are invariant to surface rotation. First, we show that a simple classifier using a single isotropic feature (grey-level standard deviation) is not invariant to surface rotation. Then a practical surface rotation invariant classifier that uses photometric stereo to estimate surface derivatives is developed. Results for both classifiers are presented.