Best Feature Selection for Texture Classification

被引：0

作者：

Kim, Daeyoun ^{[1
]}

Liu, J. Jay ^{[2
]}

Han, Chonghun ^{[1
]}

机构：

[1] Seoul Natl Univ, Sch Chem & Biol Engn, San 56-1, Seoul 151742, South Korea

[2] Samsung Elect, Asan 336840, South Korea

来源：

10TH INTERNATIONAL SYMPOSIUM ON PROCESS SYSTEMS ENGINEERING | 2009年 / 27卷

关键词：

Wavelet texture analysis; texture classification; feature selection; product surfaces; best features selection; TRANSFORM;

D O I：

暂无

中图分类号：

TQ [化学工业];

学科分类号：

0817 ;

摘要：

Texture analysis techniques enable to determine the quality of product surfaces measured by image sensors. In previous works, wavelet texture analysis based on the conventional wavelet transform and wavelet packets have been recognized as the most successful technique for classifying steel quality. In this work, we propose a texture classification strategy based on a best feature selection method, which improves classification accuracy. Our proposed methodology has been applied and validated in the classification of the surface quality of rolled steel sheets.

引用

页码：1491 / 1496

页数：6

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