Surface roughness classification using pattern recognition theory

Wei Min Shi; Siak-Piang Lim; Kim Seng Lee

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Surface roughness classification using pattern recognition theory

机译：基于模式识别理论的表面粗糙度分类

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Pattern recognition theory is introduced to perform rough surface classification. The light intensity distribution scattered from a rough surface is defined as the pattern vector of the rough surface. Using the Karhunen-Loeve transformation, the pattern vector is transformed into a feature vector for classification. To achieve a maximum separability, a modified method is proposed. The feasibility and effectiveness of the proposed method is demonstrated by computer simulation results.

机译：引入了模式识别理论以进行粗糙表面分类。从粗糙表面散射的光强度分布被定义为粗糙表面的图案矢量。使用Karhunen-Loeve变换，将模式向量变换为特征向量以进行分类。为了实现最大的可分离性，提出了一种改进的方法。计算机仿真结果证明了该方法的可行性和有效性。

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来源
《Optical engineering》 |1995年第6期|p.1756-1760|共5页
作者
Wei Min Shi; Siak-Piang Lim; Kim Seng Lee;
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作者单位

Defence Science Organisation 20 Science Park Drive Singapore 0511;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
中图分类光学仪器;
关键词
surface roughness; pattern recognition;

机译：表面粗糙度;图案识别;

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Surface roughness classification using pattern recognition theory

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