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Generalized circular autoregressive models for modeling isotropic and anisotropic textures

A new class of random field models, called generalized circular autoregressive (GCAR) models, is introduced. The GCAR models have non-causal neighbors which have the same autoregressive parameter values if they are on the same circle or ellipse, and have circular or elliptical correlation structure. The parameter estimation is also considered, and a multi-step estimation algorithm is presented. The efficacy of GCAR models in modeling real textures is demonstrated by synthesizing images resembling real textures.