Colour sorting of translucent samples






Colour Sorting, Quality Control, Computer Vision, Translucent Samples, Transparency, Mechatronics


Automated quality control and sorting based on computer vision techniques has been a long celebrated practice in industrial processes and production. Among the surface characteristics that guide the decision making in such systems, the colour holds a prominent position. This gets somehow complicated in cases dealing with translucent samples or samples with a significant amount of transparency and yet distinctive colour hues. The scope of this paper is to provide a method to tackle with such cases and presents a successful application to the world-renowned mastiha of Chios, a natural aromatic translucent resin extracted from the mastic tree that grows on the island of Chios, Greece.

Author Biography

George Pavlidis, ATHENA Research Center

Research Director

Head of the Multimedia Research Group

Institute for Language and Speech Processing

'Athena' Research Center


[1] W. Zhong, J. Chen, B. Tian, and Y. Xie, “The research of color sorting algorithm based on gray level co-occurrence matrix," in Proceedings of the 2nd International Conference on Measurements, Information and Control, 2013.

[2] R. Mahendran, G. C. Jayashree, and K. Alagusundaram, “Application of computer vision technique on sorting and grading of fruits and vegetables," Journal of Food Processing and Technology, vol. S1-001, 2012.

[3] D. Lorente, N. Aleixos, J. Gomez-Sanchis, S. Cubero, O. L. Garcia-Navarrete, and J. Blasco, “Recent advances and applications of hyperspectral imaging for fruit and vegetable quality assessment," Food and Bioprocess Technology, vol. 5, no. 4, pp. 1121{1142, 2012.

[4] G. Leiva, G. Mondragon, D. Mery, and J. M. Aguilera, “The automatic sorting using image processing improves postharvest blueberries storage quality," in Proceedings of the International Congress on Engineering and Food, 2011.

[5] D.-W. Sun, Computer vision technology for food quality evaluation. Academic Press, 2011.

[6] F. Kitougia and S. G. Mouroutsos, “Optical recognition for the automatic quality control in food industry: a low-cost color sorting application for greek olives," in Proceedings of the 2nd Panhellenic Robotics Conference, 2010.

[7] T. Pearson, “High-speed sorting of grains by color and surface texture," Applied Engineering in Agriculture, vol. 26, no. 3, pp. 499{505, 2010.

[8] E. Misimi, J. R. Mathiassen, and U. Erikson, “Computer vision-based sorting of atlantic salmon (salmo salar) fillets according to their color level," J Food Sci, vol. 72, no. 1, pp. S030{5, 2007.

[9] C.-J. Du and D.-W. Sun, “Learning techniques used in computer vision for food quality evaluation: a review," Journal of Food Engineering, vol. 72, pp. 39{55, 2006.

[10] Y. Chherawala, R. Lepage, and G. Doyon, “Food grading/sorting based on color appearance trough machine vision: the case of fresh cranberries," in Proceedings of the 2nd International Conference 'Information and Communication Technologies', 2006.

[11] F. Mendoza, P. Dejmek, and J. M. Ahuilera, “Calibrated color measurements of agricultural foods using image analysis," Postharvest Biology and Technology, vol. 41, pp. 285{295, 2006.

[12] G. Feng and C. Qixin, “Study on color imaging processing based intelligent fruit sorting system," in Proceedings of the 5th World Congress on Intelligent Control and Automation, 2004.

[13] K. L. Yam and S. E. Papadakis, “A simple digital imaging method for measuring and analyzing color of food surfaces," Journal of Food Engineering, vol. 61, pp. 137{142, 2004.

[14] T. Brosnan and D.-W. Sun, “Inspection and grading of agricultural and food products by computer vision systems - a review," Computers and Electronics in Agriculture, vol. 36, pp. 193{213, 2002.

[15] M. C. Pasikatan and F. E. Dowell, “Sorting systems based on optical methods for detecting and removing seeds infested internally by insects or fungi: a review," Applied Spectroscopy Reviews, vol. 36, no. 4, pp. 399{416, 2001.

[16] J. A. Abbott, “Quality measurement of fruits and vegetables," Postharvest Biology and Technology, vol. 15, pp. 207{225, 1999.

[17] R. Y.-Y. Chiou, P.-Y. Wu, and Y.-H. Yen, “Color sorting of lightly roasted and deskinned peanut kernels to diminish a atoxin contamination in commercial lots," Journal of Agricultural and Food Chemistry, vol. 42, pp. 2156{2160, 1994.

[18] P. Chen and Z. Sun, “A review of non-destructive methods for quality evaluation and sorting of agricultural products," Journal of Agricultural Engineering Research, vol. 49, pp. 85{98, 1991.

[19] P. Chen, “Use of optical properties for food materials in quality evaluation and materials sorting," Journal of Food Process Engineering, vol. 2, pp. 307{322, 1978.

[20] E. Saldana, R. Siche, M. Lujan, and R. Quevedo, “Review: Computer vision applied to the inspection and quality control of fruits and vegetables," Brazilian Journal of Food Technology, vol. 16, no. 4, pp. 254{272, 2013.

[21] N. Otsu, “A threshold selection method from gray-level histograms," Automatica, vol. 11, no. 285-296, pp. 23{27, 1975.

[22] Gasteratos and I. Andreadis, “Soft mathematical morphology: Extensions, algorithms, and implementations," Advances in imaging and electron physics, vol. 110, pp. 63{99, 1999.

[23] M. Vardavoulia, A. Gasteratos, and I. Andreadis, “Binary, gray-scale, and vector soft mathematical," Aspects of Image Processing and Compression, vol. 119, p. 1, 2001.

[24] N. R. of Intangible Cultural Heritage. (2013) The agriculture of the chios mastiha. [Online]. Available:

[25] R. U. O. C. D. of Tourism. (2014) Mastic. [Online]. Available: gastronomy/local-products

[26] C. M. G. Association. (2014) The chios mastiha. [Online]. Available:

View Full Article: