Resolution-invariant separable ARMA modeling of images
We suggest a continuous-domain stochastic modeling of images that is invariant to spatial resolution. Specifically, we are proposing an estimator that is calibrated with respect to the sampling step, and that can potentially handle aliased data. Motivated by Markov random fields, we assume a continuous-domain ARMA model and suggest an algorithm for estimating the continuous-domain parameters from the sampled data. The continuous-domain parameters we estimate provide features that can further be used for image classification, segmentation and interpolation, regardless of sampling interval values and of aliasing effects that may appear in the digital image. Experimental results indicate that the proposed approach is preferable over a discrete-domain ARMA modeling.