Cumulant-based stationary and nonstationary models for classification and synthesis of random fields

Guotong Zhou, Georgios B. Giannakis

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

1 Scopus citations

Abstract

Cumulants are employed for classification and synthesis of textured images because they suppress additive Gaussian noise of unknown covariance and are capable of resolving phase and causality issues in stationary non-Gaussian random fields. Their performance is compared with existing autocorrelation based approaches which offer sample estimates of smaller variance and lower computational complexity. Nonlinear matching techniques improve over linear equation methods in estimating parameters of non-Gaussian random fields especially under model mismatch. Seasonal 1-D sequences allow for semi-stationary 2-D models and their performance is illustrated on synthetic space variant textures. The potential of prolate spheroidal basis expansion is also described for parsimonious nonstationary modeling of space variant textured images.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
PublisherPubl by Int Soc for Optical Engineering
Pages444-455
Number of pages12
ISBN (Print)081940943X
StatePublished - Dec 1 1992
EventAdvanced Signal Processing Algorithms, Architectures, and Implementations III - San Diego, CA, USA
Duration: Jul 19 1992Jul 21 1992

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume1770
ISSN (Print)0277-786X

Other

OtherAdvanced Signal Processing Algorithms, Architectures, and Implementations III
CitySan Diego, CA, USA
Period7/19/927/21/92

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