2-D moving average models for texture synthesis and analysis

被引：24

作者：

Eom, KB ^{[1
]}

机构：

[1] George Washington Univ, Dept Elect Engn & Comp Sci, Washington, DC 20052 USA

来源：

IEEE TRANSACTIONS ON IMAGE PROCESSING | 1998年 / 7卷 / 12期

关键词：

random field model; texture synthesis; time-series models;

D O I：

10.1109/83.730388

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this correspondence, 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.

引用

页码：1741 / 1746

页数：6

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