Special Section on Quality Control by Artificial Vision

Multivariate statistical projection methods to perform robust feature extraction and classification in surface grading

[+] Author Affiliations
José Manuel Prats-Montalbán

Technical University of Valencia, Department of Applied Statistics, Operation Research and Quality, Camino de vera s/n, 46022 Valencia, Spain

Fernando López

Technical University of Valencia, Department of Computer Engineering, Camino de vera s/n, 46022 Valencia, Spain

José M. Valiente

Technical University of Valencia, Department of Computer Engineering, Camino de vera s/n, 46022 Valencia, Spain

Alberto Ferrer

Technical University of Valencia, Department of Applied Statistics, Operation Research and Quality, Camino de vera s/n, 46022 Valencia, Spain

J. Electron. Imaging. 17(3), 031106 (July 18, 2008). doi:10.1117/1.2957886
History: Received August 03, 2007; Revised October 24, 2007; Accepted October 26, 2007; Published July 18, 2008
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We present an innovative way to simultaneously perform feature extraction and classification for the quality-control issue of surface grading by applying two multivariate statistical projection methods: SIMCA and PLS-DA. These tools have been applied to compress the color texture data that describe the visual appearance of surfaces (soft color texture descriptors) and to directly perform classification using statistics and predictions from the projection models. Experiments have been carried out using an extensive ceramic images database (VxC TSG) comprised of 14 different models, 42 surface classes, and 960 pieces. A factorial experimental design evaluated all the combinations of several factors affecting the accuracy rate. These factors include the tile model, color representation scheme (CIE Lab, CIE Luv, and RGB), and compression/classification approach (SIMCA and PLS-DA). Moreover, a logistic regression model is fitted from the experiments to compute accuracy estimates and study the effect of the factors on the accuracy rate. Results show that PLS-DA performs better than SIMCA, achieving a mean accuracy rate of 98.95%. These results outperform those obtained in a previous work where the soft color texture descriptors in combination with the CIE Lab color space and the k-NN classifier achieved an accuracy rate of 97.36%.

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Citation

José Manuel Prats-Montalbán ; Fernando López ; José M. Valiente and Alberto Ferrer
"Multivariate statistical projection methods to perform robust feature extraction and classification in surface grading", J. Electron. Imaging. 17(3), 031106 (July 18, 2008). ; http://dx.doi.org/10.1117/1.2957886


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