Bispectral Analysis and Model Validation of Texture Images

Thomas E. Hall, Georgios B. Giannakis

Research output: Contribution to journalArticlepeer-review

52 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)996-1009
Number of pages14
JournalIEEE Transactions on Image Processing
Issue number7
StatePublished - Jul 1995

Bibliographical note

Funding Information:
Manuscript received August 22, 1993; revised July 8, 1994. The work in this paper was partly supported by ONR Grant no. N00014-93-1-0485, The associate editor coordinating the review of this paper and approving it for publication was Prof. Robert M. Haralick. The authors are with the Department of Electrical Engineering, University of Virginia, Charlottesville, VA 22903-2442 USA. IEEE Log Number 9411857.


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