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Proceedings Paper

Feature extraction and classification in surface grading application using multivariate statistical projection models
Author(s): José M. Prats-Montalbán; Fernando López; José M. Valiente; Alberto Ferrer
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Paper 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.

Paper Details

Date Published: 29 May 2007
PDF: 11 pages
Proc. SPIE 6356, Eighth International Conference on Quality Control by Artificial Vision, 63560N (29 May 2007); doi: 10.1117/12.736919
Show Author Affiliations
José M. Prats-Montalbán, Technical Univ. of Valencia (Spain)
Fernando López, Technical Univ. of Valencia (Spain)
José M. Valiente, Technical Univ. of Valencia (Spain)
Alberto Ferrer, Technical Univ. of Valencia (Spain)


Published in SPIE Proceedings Vol. 6356:
Eighth International Conference on Quality Control by Artificial Vision

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