Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Asymptotic Bias and Variance of Conventional Bispectrum Estimates for 2-D Signals
Multidimensional Systems and Signal Processing
ARMA-cepstrum Recursion Algorithm for the Estimation of the MA Parameters of 2-D ARMA Models
Multidimensional Systems and Signal Processing
Higher-order statistics based blind estimation of non-Gaussian bidimensional moving average models
Signal Processing - Fractional calculus applications in signals and systems
Satellite image restoration using statistical models
Signal Processing
A Fast Algorithm for 2-D ARMA Parameters Estimation
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
Rotation- and scale-invariant texture classification using log-polar and ridgelet transforms
Machine Graphics & Vision International Journal
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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