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Improving the industrial classification of cork stoppers by using image processing and Neuro-Fuzzy computing

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Abstract

This paper presents a solution to a problem existing in the cork industry: cork stopper/disk classification according to their quality using a visual inspection system. Cork is a natural and heterogeneous (remarkable variability among different samples, being impossible to find two samples with the same morphological distribution in its defects) material; therefore, its automatic classification (seven quality classes exist) is very difficult. The solution proposed in this paper evaluates the following procedures: quality discriminatory features extraction and classifiers analysis. Each procedure focused on the study of aspects that could influence cork quality. Experiments show that the best results are obtained by system specific features: cork area occupied by defects (after thresholding), size of the biggest defect within the cork area (morphological operations), and the Laws TEMs E5L5TR, E5E5TR, S5S5TR, W5W5TR, all working on a Neuro-Fuzzy classifier. In conclusion, the results of this study represent an important contribution to improve quality control in the cork industry.

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References

  • ASECOR (Agrupación Sanvicenteña de Empresarios del CORcho). (2007). Production statistics in the term 2007. http://www.asecor.com. Acceded 22 December 2007.

  • Asteriadis, S., Karpouzis, K., & Kollias, S. D. (2008). A Neuro-Fuzzy approach to user attention recognition. In 18th international conference, ICANN2008 (Vol. 1, pp. 927–936), Prague, Czech Republic.

  • Berberoğlu, S., & Satir, B. (2008). Fuzzy classification of Mediterranean type forest using Envisat Meris Data. In International archives of the photogrammetry, remote sensing and spatial information sciences (ISPRS 2008) (Vol. XXXVII, pp. 1109–1119).

  • Bouharati, S., Benmahammed, K., Harzallah, D., & El-Assaf, Y. M. (2008). Application of artificial Neuro-Fuzzy logic inference system for predicting the microbiological pollution in fresh water. Journal of Applied Sciences, 8(2), 309–315.

    Article  Google Scholar 

  • Brunetti, A., Cesareo, R., Golosio, B., Luciano, P., & Ruggero, A. (2002). Cork quality estimation by using Compton tomography. Nuclear Instruments & Methods in Physics Research. Section B, Beam Interactions with Materials and Atoms, 196, 161–168. doi:10.1016/S0168-583X(02)01289-2.

    Article  Google Scholar 

  • Chang, J., Han, G., Valverde, J. M., Griswold, N. C., Duque-Carrillo, J. F., & Sánchez-Sinencio, E. (1997). Cork quality classification system using a unified image processing and fuzzy-neural Network methodology. IEEE Transactions on Neural Networks, 8(4), 964–974. doi:10.1109/72.595897.

    Article  Google Scholar 

  • Cheu, E. Y., Quek, C., & Ng, S. K. (2008). TNFIS: Tree based neural fuzzy inference system. In IEEE international joint conference on neuronal networks, IJCNN 2008 (pp. 398–405).

  • Chow, C. K., & Kaneko, T. (1972). Automatic boundary detection of left ventricle from cineangiograms. Computers and Biomedical Research, 5, 338–410.

    Article  Google Scholar 

  • CorkQC (The Natural Cork Quality Council). (2008). Industry statistics. http://www.corkqc.com. Acceded 3 March 2008.

  • Corona, P., Dettori, S., Filigheddu, M. R., Maetzke, F., & Scotti, R. (2005). Site quality evaluation by classification tree: An application to cork quality in Sardinia. European Journal of Forest Research, 124, 37–46.

    Article  Google Scholar 

  • Costa, A., & Pereira, H. (2006). Decision rules for computer-vision quality classification of wine natural cork stoppers. American Journal of Enology and Viticulture, 57, 210–219.

    Google Scholar 

  • Dragomir, O., Gouriveau, R., & Zerhouni, N. (2008). Adaptive Neuro-Fuzzy inference system for midterm prognostic error stabilization. In International conference on computers, communications and control, ICCCC08 (pp. 1–6), Baile Felix, Oradea, Romania.

  • Durán, M. L., Cernadas, E., Caro, A., & Antequera, T. (2001). Clasificación de Distintos Tipos de Jamón Ibérico Utilizando Análisis de Texturas. Revista Electrónica de Visión por Computador, 5, 1–11. REVC.

    Google Scholar 

  • Fisher, R., Perkins, S., Walker, A., & Wolfart, E. (2004) HIPR2: Image processing learning resources. http://homepages.inf.ed.ac.uk/rbf/HIPR2. Acceded 20 September 2005.

  • Fortes, M. A. (1993). Cork and corks. European Review (Chichester, England), 1, 189–195.

    Google Scholar 

  • Gonzalez-Adrados, J. R., Lopes, F., & Pereira, H. (2000). The quality grading of cork planks with classification models based on defect characterization. Holz als Roh- und Werkstoff, 58, 39–45.

    Article  Google Scholar 

  • Habib, H. A., Yousaf, M. H., & Mohibullah, M. (2004). Modified laws energy descriptor for inspection of ceramic tiles. In National conference on emerging technologies (pp. 137–140), Pakistan.

  • Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, 3, 610–621. doi:10.1109/TSMC.1973.4309314.

    Article  Google Scholar 

  • ICMC (Instituto del Corcho.Madera y Carbón Vegetal, Instituto de Promoción del Corcho, ICMC-IPROCOR). (2008). http://www.iprocor.org. Acceded 3 March 2008.

  • Jang, J.-S. R., Sun, C. T., & Mizutani, E. (1997). Neuro-Fuzzy and soft computing. New Jersey: Prentice Hall.

    Google Scholar 

  • Johannsen, G., & Bille, J. (1982). A thresholding selection method using information measures. Proc. 6th international conference on pattern recognition (pp. 140–143).

  • 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. Computer Vision Graphics and Image Processing, 29, 273–285. doi:10.1016/0734-189X(85)90125-2.

    Article  Google Scholar 

  • Kumar, S., Kumar, S., Prakash, Shankar, R., Tiwari, M. K., & Kumar, S. B. (2007). Prediction of flow stress for carbon steels using recurrent self-organizing neuro fuzzy networks. Expert Systems with Applications: An International Journal, 32(3), 777–788. doi:10.1016/j.eswa.2006.01.041.

    Article  Google Scholar 

  • Laws, K. I. (1980). Rapid texture identification. SPIE Image Processing for Missile Guidance, 238, 376–380.

    Google Scholar 

  • Lima, J. L., & Gomes-Costa, P. (2005). A modular approach to real-time cork classification using image processing. In 10th IEEE conference on emerging technologies and factory automation (Vol. 2, pp. 361–368).

  • Lin, C. J., Chen, C., & Lee, C. (2008). Classification and medical diagnosis using wavelet-based fuzzy neural networks. International Journal of Innovative Computing. Information and Control, 4(3), 735–748. ICIC International.

    Google Scholar 

  • Marpu, P. R., Wijaya, A., & Gloaguen, R. (2008). Soft classification and assessment of kalman filter neural network for complex landcover of tropical rainforests. In International geoscience and remote sensing symposium (IGARSS 2008), Boston, USA.

  • Maxwell, B. A., & Brubaker, S. J. (2003). Texture edge detection using the compass operator. In British machine vision conference (Vol. 2, pp. 549–558), Norwich.

  • Monzon, J. E., & Pisarello, M. I. (2004). Identificación de Latidos Cardíacos Anómalos con Redes Neuronales Difusas. Comunicaciones Científicas y Tecnológicas, E-038 [in Spanish]. Chaco-Corrientes, Argentina: Universidad Nacional del Nordeste.

  • Niemistö, A. (2004). A comparison of nonparametric histogram-based thresholding algorithms, Technical report. Finland: Tampere University of Technology.

  • Otsu, N. (1978). A threshold selection method from gray-level histogram. IEEE Transactions on Systems, Man, and Cybernetics, SMC-8, 62–66.

    Google Scholar 

  • Paniagua-Paniagua, B., Vega-Rodríguez, M. A., Gómez Pulido, J. A., & Sánchez Pérez, J. M. (2006a). Comparative study of second-order grey level texture statistics to evaluate cork quality. In Visualization, imaging, and image processing (VIIP 2006) (Vol. 1, pp. 447–452).

  • Paniagua-Paniagua, B., Vega-Rodríguez, M. A., Gómez Pulido, J. A., & Sánchez Pérez, J. M. (2006b). Comparative study of thresholding techniques to evaluate cork quality. In Visualization, imaging, and image processing (VIIP 2006) (Vol. 1, pp. 447–452).

  • Pereira, H., Melo, B., & Pinto, R. (1994). Yield and quality in the production of cork stoppers. Ausbeute und Qualität bei der Herstellung von Korkstopfen. Holz als Roh- und Werkstoff, 52, 211–214.

    Article  Google Scholar 

  • Pun, T. (1980). A new method for gray-level picture thresholding using the entropy of the histogram. Signal Processing, 2, 223–237. doi:10.1016/0165-1684(80)90020-1.

    Article  Google Scholar 

  • Pun, T. (1981). Entropic thresholding: A new approach. Computer Vision Graphics and Image Processing, 16, 210–239. doi:10.1016/0146-664X(81)90038-1.

    Article  Google Scholar 

  • Radeva, P., Bressan, M., Tobar, A., & Vitrià, J. (2002). Real-time inspection of cork stoppers using parametric methods in high dimensional spaces. In The IASTED conference on signal and image processing.

  • Rocha, S., Delgadillo, I., Ferrer Correia, A. J., Barros, A., & Wells, P. (1998). Application of an electronic aroma sensing system to cork stopper quality control. Journal of Agricultural and Food Chemistry, 46, 145–151. doi:10.1021/jf970259+.

    Article  Google Scholar 

  • Rosenfeld, P. (1983). Histogram concavity analysis as an aid in threshold selection. IEEE Transactions on Systems, Man, and Cybernetics, SMC-13, 231–235.

    Google Scholar 

  • Sahoo, P. K., Soltani, S., Wong, A. K. C., & Chen, Y. C. (1988). A survey of thresholding techniques. Computer Vision Graphics and Image Processing, 41, 233–260. CVGIP. doi:10.1016/0734-189X(88)90022-9.

  • Schmid, C. (2001). Constructing models for content-based image retrieval. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2, 39–45.

    Google Scholar 

  • Shah, S. K., & Gandhi, V. (2004). Image classification based on textural features using artificial neural network (ANN). IE(I). Journal-ET, 84, 72–77.

  • Shapiro, L. G., & Stockman, G. C. (2001). Computer vision. New Jersey: Prentice Hall.

    Google Scholar 

  • Sonka, M., Hlavac, V., & Boyle, R. (1998). Image processing, analysis and machine vision (2nd ed.). USA: PWS Publishing.

    Google Scholar 

  • Tsai, W. (1985). Moment-preserving thresholding, a new approach. Computer Vision Graphics and Image Processing, 29, 377–393. doi:10.1016/0734-189X(85)90133-1.

    Article  Google Scholar 

  • Varma, M., & Zisserman, A. (2005). A statistical approach to texture classification from single images. International Journal of Computer Vision, 62, 61–81.

    Google Scholar 

  • Vega-Rodríguez, M. A., Sánchez-Pérez, J. M., & Gómez-Pulido, J. A. (2004). Using computer vision and FPGAs in the cork industry. Proceedings of the IEEE mechatronics and robotics (pp. 265–270).

  • Wood, E. J., Siew, L. H., & Hodgson, R. M. (1988). Texture measures for carpet wear assessment. IEEE Transactions on Pattern Analysis and Machine Intelligence, 10, 92–105. doi:10.1109/34.3870.

    Article  Google Scholar 

  • Woodford, B. J., Deng, D., & Benwell, G. L. (2004). A wavelet-based Neuro-Fuzzy system for data mining small image sets. In M. Purvis (Ed.), Proc. Australasian workshop on data mining and web intelligence (DMWI2004) (Vol. 32, pp. 139–143). Dunedin, New Zealand: CRPIT, ACS.

  • Wu, C. M., Chen, Y. C., & Hsieh, K. S. (1992). Texture features for classification of ultrasonic liver images. IEEE Transactions on Medical Imaging, 11, 141–152. doi:10.1109/42.141636.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Technologies of Computers and Communications, University of Extremadura, Escuela Politécnica. Campus Universitario s/n, 10071, Cáceres, Spain

    Beatriz Paniagua, Miguel A. Vega-Rodríguez, Juan A. Gomez-Pulido & Juan M. Sanchez-Perez

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  1. Beatriz Paniagua
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  2. Miguel A. Vega-Rodríguez
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  3. Juan A. Gomez-Pulido
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  4. Juan M. Sanchez-Perez
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Correspondence to Beatriz Paniagua.

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Paniagua, B., Vega-Rodríguez, M.A., Gomez-Pulido, J.A. et al. Improving the industrial classification of cork stoppers by using image processing and Neuro-Fuzzy computing. J Intell Manuf 21, 745–760 (2010). https://doi.org/10.1007/s10845-009-0251-4

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