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

Guotong Zhou; Georgios B. Giannakis

doi:10.1117/12.130949

30 November 1992 Cumulant-based stationary and nonstationary models for classification and synthesis of random fields

Guotong Zhou, Georgios B. Giannakis

Proceedings Volume 1770, Advanced Signal Processing Algorithms, Architectures, and Implementations III; (1992) https://doi.org/10.1117/12.130949
Event: San Diego '92, 1992, San Diego, CA, United States

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.

Citation Download Citation

Guotong Zhou and Georgios B. Giannakis "Cumulant-based stationary and nonstationary models for classification and synthesis of random fields", Proc. SPIE 1770, Advanced Signal Processing Algorithms, Architectures, and Implementations III, (30 November 1992); https://doi.org/10.1117/12.130949

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available