29 May 2007 Feature extraction and classifcation in surface grading application using multivariate statistical projection models
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Proceedings Volume 6356, Eighth International Conference on Quality Control by Artificial Vision; 63560N (2007) https://doi.org/10.1117/12.736919
Event: Eighth International Conference on Quality Control by Artificial Vision, 2007, Le Creusot, France
Abstract
In this paper we present an innovative way to simultaneously perform feature extraction and classification for the quality control issue of surface grading by applying two well known multivariate statistical projection tools (SIMCA and PLS-DA). These tools have been applied to compress the color texture data describing the visual appearance of surfaces (soft color texture descriptors) and to directly perform classification using statistics and predictions computed from the extracted projection models. Experiments have been carried out using an extensive image database of ceramic tiles (VxC TSG). This image database is comprised of 14 different models, 42 surface classes and 960 pieces. A factorial experimental design has been carried out to evaluate all the combinations of several factors affecting the accuracy rate. Factors include tile model, color representation scheme (CIE Lab, CIE Luv and RGB) and compression/classification approach (SIMCA and PLS-DA). In addition, a logistic regression model is fitted from the experiments to compute accuracy estimates and study the factors effect. The 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 classi.er achieved a 97.36% of accuracy.
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José M. Prats-Montalbán, José M. Prats-Montalbán, Fernando López, Fernando López, José M. Valiente, José M. Valiente, Alberto Ferrer, Alberto Ferrer, } "Feature extraction and classifcation in surface grading application using multivariate statistical projection models", Proc. SPIE 6356, Eighth International Conference on Quality Control by Artificial Vision, 63560N (29 May 2007); doi: 10.1117/12.736919; https://doi.org/10.1117/12.736919
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