Texture characterization using 2D cumulant-based lattice adaptive filtering

In this work. we take into account the non gaussian properties of textures and we propose a new approach for their characterization based on bidimensional adaptive modelisation using higher order statistics. The 2DOLRIV (Bidimensionnal Overdetermined Lattice Recursive Instrumental Variable) algorithm allows accurate texture model estimation. Sets of ZD-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 ZD-AR model even when the texture image is disturbed by gaussian noise. A multilaycr neural network deals with these coefficients in order to classify different textures. In order to evaluate the performance of this approach, classification sensitivity is evaluated on a set of eight different textures. This characterization approach gives very promising results.