1 June 1995 Surface roughness classification using pattern recognition theory
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Optical Engineering, 34(6), (1995). doi:10.1117/12.203125
Abstract
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.
Wei Min Shi, Siak-Piang Lim, Kim Seng Lee, "Surface roughness classification using pattern recognition theory," Optical Engineering 34(6), (1 June 1995). http://dx.doi.org/10.1117/12.203125
JOURNAL ARTICLE
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KEYWORDS
Image classification

Pattern recognition

Feature extraction

Light scattering

Surface roughness

Computer simulations

Machine learning

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