Texture characterization using 2D cumulant-based lattice adaptive filtering

We take into account the non-Gaussian properties of textures and we propose a new approach for their characterization based on bidimensional adaptive modeling using higher order statistics. The 2D-OLRIV (bidimensional overdetermined lattice recursive instrumental variable) algorithm allows accurate texture model estimation. Sets of 2D-AR coefficients obtained from the 2D reflection coefficients of the lattice model are used to characterize the texture model. This algorithm has the advantage of yielding non-biased estimates of the 2D-AR model even when the texture image is disturbed by Gaussian noise. A multilayer neural network deals with these coefficients in order to classify different textures. In order to evaluate the performance of this approach, the classification sensitivity is evaluated on a set of eight different textures. This characterization approach gives very promising results