TEXTURE ROUGHNESS ANALYSIS AND SYNTHESIS VIA EXTENDED SELF-SIMILAR (ESS) MODEL

被引：59

作者：

KAPLAN, LM

KUO, CCJ

机构：

[1] UNIV SO CALIF, INST SIGNAL & IMAGE PROC, LOS ANGELES, CA 90089 USA

[2] UNIV SO CALIF, DEPT ELECT ENGN SYST, LOS ANGELES, CA 90089 USA

来源：

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE | 1995年 / 17卷 / 11期

关键词：

FRACTALS; FRACTIONAL BROWNIAN MOTION; PROCESSES WITH STATIONARY INCREMENTS; TERRAIN MODELING; TEXTURE ANALYSIS; TEXTURE SYNTHESIS; RANDOM FIELDS; ROUGHNESS PERCEPTION;

D O I：

10.1109/34.473230

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The 2D fractional Brownian motion (fBm) model provides a useful tool to model textured surfaces whose roughness is scale-invariant. To represent textures whose roughness is scale-dependent, we generalize the fBm model to the extended selfsimilar (ESS) model in this research. We present an estimation algorithm to extract the model parameters from real texture data. Furthermore, a new incremental Fourier synthesis algorithm is proposed to generate the 2D realizations of the ESS model. Finally, the estimation and rendering methods are combined to synthesize real textured surfaces.

引用

页码：1043 / 1056

页数：14

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