A new non-linear exponential 2-D adaptive filter and its application in texture characterization
Mounir Sayadi, Samir Sakrani, Farhat Fnaiech, Mohamed Cheriet · 2004
We propose, in this paper, a new non-linear exponential adaptive bi-dimensional (2D) filter for image modeling. The filter coefficients are updated with the least mean square (LMS) algorithm. Furthermore, the proposed nonlinear model is used for texture modeling with a 2D auto-regressive (AR) adaptive model. The characterization efficiency of the proposed exponential model is compared with the 2D linear AR model updated with the LMS algorithm. The comparison criteria is based on the computation of a characterization rate using the ratio of "between-class" variances with respect to "within-class" variances of the estimated coefficients. Extensive experiments show that the exponential model coefficients give better results in texture discrimination than those of the linear model, even in a noisy context.