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Proceedings Paper

Cumulant-based stationary and nonstationary models for classification and synthesis of random fields
Author(s): Guotong Zhou; Georgios B. Giannakis
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Paper 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.

Paper Details

Date Published: 30 November 1992
PDF: 12 pages
Proc. SPIE 1770, Advanced Signal Processing Algorithms, Architectures, and Implementations III, (30 November 1992); doi: 10.1117/12.130949
Show Author Affiliations
Guotong Zhou, Univ. of Virginia (United States)
Georgios B. Giannakis, Univ. of Virginia (United States)


Published in SPIE Proceedings Vol. 1770:
Advanced Signal Processing Algorithms, Architectures, and Implementations III
Franklin T. Luk, Editor(s)

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