The Taub Faculty of Computer Science Events and Talks

Random field models characterize the correlation among neighboring pixels in an image. Specifically, a wide-sense Markov model is obtained by assuming a separable correlation function for a 2D auto-regressive (AR) model. This model is applicable to image restoration, image compression, and texture classification and segmentation. In this work we explore the effect of sampling on statistical features of an image such as histogram and the autocorrelation function. We show that the Markovian property is preserved for the 2nd-order case (of the wide-sense model) and use this result to prove that, under mild conditions, the histogram of images that obey this model is invariant under sampling. Furthermore, we develop relations between the statistics of the image and its sampled version, in terms of moments and generating model noise characteristics. Motivated by these results, we propose a new method for texture interpolation, based on an orthogonal decomposition model for textures. Experiments with natural texture images demonstrate the advantages of the proposed method over presently available interpolation methods.

*MSc. research under the supervision of Dr. Moshe Porat.