A SOLUTION FOR EVALUATING THE STOPPER QUALITY IN THE CORK INDUSTRY

Beatriz Paniagua-Paniagua, Miguel A. Vega-Rodríguez, Juan A. Gómez-Pulido, Juan M. Sánchez-Pérez

Abstract

In this paper we study a possible solution to a problem existing in the cork industry: the cork stopper/disk classification according to their quality using a visual inspection system. Cork is a natural and heterogeneous material, therefore, its automatic classification (usually, seven different quality classes exist) is very difficult. The solution proposed in this paper shows all the stages made in our study: quality discriminatory features selection and extraction, texture analysis, analysis of different (global and local) automatic thresholding techniques and possible classifiers. In each stage we have given more importance to the study of those aspects that we think could influence the cork quality. In this paper we attempt to evaluate each of the stages in our solution to the problem of the cork classification in an industrial environment, and therefore, finding a way to justify the design of our final classification system. In conclusion, our experiments show that the best results are obtained by a system that works with the following features: total cork area occupied by defects (thresholding with heuristic fixed value 69), textural contrast, textural entropy and size of the biggest defect in the cork, all of them working in an Euclidean classifier. The obtained results have been very encouraging.

References

  1. Chow, C.K., Kaneko, T., 1972. Automatic Boundary Detection of Left Ventricle from Cineangiograms. In Comput. Biomed. Res., vol. 5, pp 338-410.
  2. CorkQC, The Natural Cork Quality Council, 2006. Industry Statistics. At http://www.corkqc.com.
  3. Fisher, R., Perkins, S., Walker, A., Wolfart, E., 2004. HIPR2: Image Processing Learning Resources. At http://homepages.inf.ed.ac.uk/rbf/HIPR2.
  4. Fortes, M.A., 1993. Cork and Corks. In European Rev., vol. 1, pp 189-195.
  5. Haralick, R.M., Shanmugam, K., Dinstein, I., 1973. Textural Features for Image Classification. In IEEE Trans. Systems Man Cybernet., vol. 3, pp 610-621.
  6. ICMC, Instituto del Corcho, Madera y Carbón Vegetal, 2006. Instituto de Promoción del Corcho (ICMCIPROCOR). At http://www.iprocor.org, Spain.
  7. Johannsen, G., Bille, J., 1982. A Thresholding Selection Method Using Information Measures. In Proc. 6th International Conference on Pattern Recognition, Munich, Germany, pp 140-143.
  8. Kapur, J.N., Sahoo, P.K., Wong, A.K.C., 1985. A New Method for Gray-Level Picture Thresholding Using the Entropy of the Histogram. In Computer Vision, Graphics and Image Processing, vol. 29, pp 273-285.
  9. Otsu, N., 1978. A Threshold Selection Method from GrayLevel Histogram. In IEEE Trans. Systems Man Cybernet., SMC-8, pp 62-66.
  10. Pun, T., 1980. A New Method for Gray-Level Picture Thresholding Using the Entropy of the Histogram. In Signal Process., vol. 2, pp 223-237.
  11. Pun, T., 1981. Entropic Thresholding: A New Approach. In Computer Vision, Graphics and Image Processing, vol. 16, pp 210-239.
  12. Rosenfeld, A., De la Torre, P., 1983. Histogram Concavity Analysis as an Aid in Threshold Selection. In IEEE Trans. Systems Man Cybernet., SMC-13, pp 231-235.
  13. Sahoo, P.K., Soltani, S., Wong, A.K.C., 1988. A Survey of Thresholding Techniques. In Computer Vision, Graphics, and Image Processing, vol. 41, pp 233-260.
  14. Shah, S.K., Gandhi, V., 2004. Image Classification Based on Textural Features Using Artificial Neural Network (ANN). In IE(I) Journal-ET, vol. 84, pp 72-77.
  15. Shapiro, L.G., Stockman, G.C., 2001. Computer Vision. Ed. Prentice Hall, New Jersey.
  16. Sonka, M., Hlavac, V., Boyle, R., 1998. Image Processing, Analysis and Machine Vision. Ed. PWS Publishing, USA, 2nd edition.
  17. Tsai, W., 1985. Moment-Preserving Thresholding: A New Approach. In Computer Vision, Graphics and Image Processing, vol. 29, pp 377-393.
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Paper Citation


in Harvard Style

Paniagua-Paniagua B., A. Vega-Rodríguez M., A. Gómez-Pulido J. and M. Sánchez-Pérez J. (2006). A SOLUTION FOR EVALUATING THE STOPPER QUALITY IN THE CORK INDUSTRY . In Proceedings of the Third International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO, ISBN 978-972-8865-60-3, pages 334-339. DOI: 10.5220/0001209703340339


in Bibtex Style

@conference{icinco06,
author={Beatriz Paniagua-Paniagua and Miguel A. Vega-Rodríguez and Juan A. Gómez-Pulido and Juan M. Sánchez-Pérez},
title={A SOLUTION FOR EVALUATING THE STOPPER QUALITY IN THE CORK INDUSTRY},
booktitle={Proceedings of the Third International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,},
year={2006},
pages={334-339},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001209703340339},
isbn={978-972-8865-60-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Third International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,
TI - A SOLUTION FOR EVALUATING THE STOPPER QUALITY IN THE CORK INDUSTRY
SN - 978-972-8865-60-3
AU - Paniagua-Paniagua B.
AU - A. Vega-Rodríguez M.
AU - A. Gómez-Pulido J.
AU - M. Sánchez-Pérez J.
PY - 2006
SP - 334
EP - 339
DO - 10.5220/0001209703340339