Texture model validation using higher-order statistics

Thomas E. Hall, Georgios B. Giannakis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

Abstract

Higher-than-second-order statistics are used to derive and implement 2-D Gaussianity and linearity tests which validate the assumptions of random models which characterize texture analysis and synthesis in terms of first- and second-order statistics. The non-redundant region of the 2-D cumulant sequence and its Fourier transform, the bispectrum, are correctly defined and proven. General non-minimum phase and asymmetric non-causal AR (autoregressive) and ARMA (autoregressive moving average) models of textures are derived using cumulant statistics. Parameter estimators are obtained both by solving a set of linear equations and by minimizing a cumulant-matching criterion. Simulations on synthetic data are performed and the results of the higher-order analysis on real textures are reported.

Original languageEnglish (US)
Title of host publicationProceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing
Editors Anon
PublisherPubl by IEEE
Pages2673-2676
Number of pages4
ISBN (Print)078030033
StatePublished - Dec 1 1991
EventProceedings of the 1991 International Conference on Acoustics, Speech, and Signal Processing - ICASSP 91 - Toronto, Ont, Can
Duration: May 14 1991May 17 1991

Publication series

NameProceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing
Volume4
ISSN (Print)0736-7791

Other

OtherProceedings of the 1991 International Conference on Acoustics, Speech, and Signal Processing - ICASSP 91
CityToronto, Ont, Can
Period5/14/915/17/91

Fingerprint

Dive into the research topics of 'Texture model validation using higher-order statistics'. Together they form a unique fingerprint.

Cite this