Texture characterization based on 2-D reflection coefficients
Olivier Alata, Pierre Baylou, Mohamed M. M. Najim · 2002
In the framework of model based image processing, we propose a new parametric approach for classifying textured images. The image, considered as a two-dimensional stochastic process, is characterized by a set of reflection coefficients computed using a two-dimensional adaptive lattice filter based on the recursive least squares (RLS) criterion. The corresponding algorithm is named the two-dimensional fast lattice RLS. In order to evaluate this method, classification rates are calculated on a set of 8 different textures from the Brodatz album. We carry out performance comparisons with methods of characterization based on two-dimensional AR coefficients computed with two-dimensional transversal filters or based on statistical features calculated from co-occurrence matrices and neighbouring matrices.