Abstract
This paper demonstrates significant improvement in the performance of a computer vision system by incorporating the results of an experiment on human visual perception. This system was designed to solve a problem existing in the cork industry: the automatic classification of cork samples according to their quality. This is a difficult problem because cork is a natural and heterogeneous material. An eye-tracker was used to analyze the gaze patterns of a human expert trained in cork classification, and the results identified visual features of cork samples used by the expert in making decisions. Variations in lightness of the cork surface proved to be a key feature, and this finding was used to select the features included in the final system: defects in the sample (thresholding), size of the biggest defect (morphological operations), and four Laws textural features, all working on a Neuro-Fuzzy classifier. The results obtained from the final system show lower error rates than previous systems designed for this application.
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ICMC: Instituto del Corcho, Madera y Carbón Vegetal, Instituto de Promoción del Corcho (ICMC-IPROCOR) (2009), http://www.iprocor.org
Gonzalez-Adrados, J.R., Lopes, F., Pereira, H.: The Quality Grading of Cork Planks with Classification Models based on Defect Characterization. Holz als Rohund Werkstoff 58(1-2), 39–45 (2000)
Vega-Rodriguez, M.A., Sanchez-Perez, J.M., Gomez-Pulido, J.A.: Using Computer Vision and FPGAs in the Cork Industry. In: Proceedings of the IEEE Mechatronics and Robotics 2004 (2004)
Chang, J., Han, G., Valverde, J.M., Griswold, N.C., Duque-Carrillo, J.F., Sánchez-Sinencio, E.: Cork Quality Classification System using a Unified Image Processing and Fuzzy-Neural Network Methodology. IEEE Transactions on Neural Networks 8(4), 964–974 (1997)
Radeva, P., Bressan, M., Tobar, A., Vitrià, J.: Real-time Inspection of Cork Stoppers using Parametric Methods in High Dimensional Spaces. In: The IASTED Conference on Signal and Image Processing SIP (2002)
Paniagua-Paniagua, B., Vega-Rodríguez, M.A., Gómez-Pulido, J.A., Sánchez-Pérez, J.M.: Comparative Study of second-order gray level Texture Statistics to evaluate Cork Quality. In: Visualization, imaging, and image processing (VIIP 2006), vol. I, pp. 447–452 (2006)
Paniagua-Paniagua, B., Vega-Rodríguez, M.A., Bustos-García, P., Gómez-Pulido, J.A., Sánchez-Pérez, J.M.: Advanced Texture Analysis in Cork Quality Detection. In: 5th IEEE International Conference on Industrial Informatics (INDIN 2007), vol. II, pp. 311–315 (2007)
Paniagua-Paniagua, B., Vega-Rodríguez, M.A., Gómez-Pulido, J.A., Sánchez-Pérez, J.M.: Comparative Study of thresholding techniques to evaluate Cork Quality. In: Visualization, imaging, and image processing (VIIP 2006), vol. I, pp. 441–446 (2006)
Paniagua-Paniagua, B., Vega-Rodríguez, M.A., Nagahashi, H., Gómez-Pulido, J.A., Sánchez-Pérez, J.M.: Using 3D Features to Evaluate Cork Quality. In: International Conference on Signal Processing and Multimedia Applications (SIGMAP 2007), vol. I, pp. 79–84 (2007)
Paniagua-Paniagua, B., Vega-Rodríguez, M.A., Sánchez-Pérez, J.M., Gómez-Pulido, J.A.: Image Processing and Neuro-Fuzzy Computing for Cork Quality Classification. In: 5th IEEE International Conference on Industrial Informatics (INDIN 2007), vol. II, pp. 657–661 (2007)
Long, H., Leow, W.K.: Perceptual texture space improves perceptual consistency of computational features. In: IJCAI 2001, pp. 1391–1396 (2001)
Laws, K.I.: Rapid Texture Identification. In: SPIE Image Processing for Missile Guidance, vol. 238, pp. 376–380 (1980)
Santella, A., DeCarlo, D.: Abstracted Painterly Renderings Using Eye-Tracking Data. In: International Symposium on Non Photorealistic Animation and Rendering (NPAR 2002), pp. 75–82 (2002)
Rele, R.S., Duchowski, A.T.: Using Eye Tracking to Evaluate Alternative Search Results Interfaces. In: Proceedings of the Human Factors and Ergonomics Society, pp. 26–30 (2005)
Bruce, V., Green, P.R., Georgeson, M.: Visual Perception: Physiology, Psychology and Ecology, 4th edn. Psychology Press, Hove (2003)
Tobii Inc., Tobii X50 Series Manual (2004)
Tobii Inc. official website, http://www.tobii.com
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Paniagua, B., Green, P., Chantler, M., Vega-Rodríguez, M.A., Gómez-Pulido, J.A., Sánchez-Pérez, J.M. (2009). Perceptually Relevant Pattern Recognition Applied to Cork Quality Detection. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2009. Lecture Notes in Computer Science, vol 5627. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02611-9_91
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DOI: https://doi.org/10.1007/978-3-642-02611-9_91
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