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Bispectral analysis and model validation of texture images

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2 Author(s)
Hall, T.E. ; Dept. of Electr. Eng., Virginia Univ., Charlottesville, VA, USA ; Giannakis, G.B.

Statistical approaches to texture analysis and synthesis have largely relied upon random models that characterize the 2-D process in terms of its first- and second-order statistics, and therefore cannot completely capture phase properties of random fields that are non-Gaussian and/or asymmetric. In this paper, higher than second-order statistics are used to derive and implement 2-D Gaussianity, linearity, and spatial reversibility tests that validate the respective modeling assumptions. The nonredundant region of the 2-D bispectrum is correctly defined and proven. A consistent parameter estimator for nonminimum phase, asymmetric noncausal, 2-D ARMA models is derived by minimizing a quadratic error polyspectrum matching criterion. Simulations on synthetic data are performed and the results of the bispectral analysis on real textures are reported

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Image Processing, IEEE Transactions on  (Volume:4 ,  Issue: 7 )