2-D moving average models for texture synthesis and analysis

Author

Eom, Kie B.

Author_Institution

Dept. of Electr. Eng. & Comput. Sci., George Washington Univ., Washington, DC, USA

Volume

7

Issue

12

fYear

1998

fDate

12/1/1998 12:00:00 AM

Firstpage

1741

Lastpage

1746

Abstract

A random field model based on moving average (MA) time-series model is proposed for modeling stochastic and structured textures. A frequency domain algorithm to synthesize MA textures is developed, and maximum likelihood estimators are derived. The Cramer-Rao lower bound is also derived for measuring the estimator accuracy. The estimation algorithm is applied to real textures, and images resembling natural textures are synthesized using estimated parameters

Keywords

frequency-domain analysis; image texture; maximum likelihood estimation; moving average processes; random processes; time series; 2D moving average models; Cramer-Rao lower bound; MA time-series model; estimated parameters; estimation algorithm; estimator accuracy; frequency domain algorithm; maximum likelihood estimators; moving average time-series model; natural textures; random field model; real textures; texture analysis; texture synthesis; Finite impulse response filter; Frequency domain analysis; Frequency estimation; Image texture analysis; Maximum likelihood estimation; Parameter estimation; Stochastic processes; Time series analysis; Wavelet packets; Wavelet transforms;

fLanguage

English

Journal_Title

Image Processing, IEEE Transactions on

Publisher

ieee

ISSN

1057-7149

Type

jour

DOI

10.1109/83.730388

Filename

730388