Colour sorting of translucent samples
DOI:
https://doi.org/10.14419/jacst.v4i2.5310Keywords:
Colour Sorting, Quality Control, Computer Vision, Translucent Samples, Transparency, MechatronicsAbstract
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.
References
[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: http://ayla.culture.gr
[25] R. U. O. C. D. of Tourism. (2014) Mastic. [Online]. Available: http://www.chios.gr/en/things-to-do/ gastronomy/local-products
[26] C. M. G. Association. (2014) The chios mastiha. [Online]. Available: http://www.gummastic.gr/
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Received date: September 11, 2015
Accepted date: October 9, 2015
Published date: October 29, 2015