Görüntü İşleme ve Gözetimli Öğrenme Yardımıyla Endüstriyel Beyaz Kuvars Taş Sınıflandırması

Abstract

Endüstriyel maden ve taş sınıflandırma uygulamalarına yönelik, görü tabanlı bir sınıflandırma metodu geliştirilmiştir. Görü tabanlı çözümleme taşlara ait görsel parametrelerin çıkartılması için kullanılmış ve makine öğrenme algoritmalarından yararlanılarak renk ve şekil parametrelerine göre sınıflandırılmıştır. Gerçekleştirilen deneylerde, her biri gelişigüzel seçilmiş 10 adet taş içeren 4 grup, toplamda 40 karışık renk ve şekil özellikleri gösteren numune sınıflandırılmıştır. Her bir numunenin 4 farklı açıdan alınan birer görüntüsü, görsel parametrelerin elde edilmesi amacıyla işlenmiştir. Elde edilen verinin %67’si programın eğitimi amacıyla; kalan veri ise test süreçlerinde kullanılmıştır. Geliştirilen yöntem, durgun görüntüleri çekilerek etiketlenen maden numunelerinin %98’e kadar sınıflandırılmasını sağlamaktadır. Yöntemin verimliliğinin daha açık bir şekilde görülmesi ve sonuçların daha iyi değerlendirilmesi amacıyla deneysel verilerden türetilmiş bir karışıklık matrisi kullanılmıştır.

Keywords

• Bilgisayarlı Görü
• Taş Sınıflandırma
• Görüntü İşleme
• En Yakın Komşu

Industrial White Quartz Stone Classification Using Image Processing and Supervised Learning

Abstract

A vision-based stone classifying method was developed for industrial mine stone grading applications. The image-based solution is used to extract visual parameters and stones are classified by their color and shape parameters with the help of the machine learning algorithms. In the experiments, four groups, each including ten arbitrarily selected stones; in total forty stone samples with complex colors and shapes were examined. Four different images are captured under four different angles and processed to extract visual parameters of each stone sample. In training stage 67% of the data were used for training and rest were used for testing process. The method correctly classifies mine stones up to 98% from still images using labeled inputs. A confusion matrix derived from the experimental results is employed in order to emphasize the efficiency of the system more clearly and emphasize the results in a certain manner.

Keywords

• Computer vision
• stone grading
• image processing
• nearest neighbors.

References

1. [1]. Puttemans, S., Goedemé, T., Mian, A., Moeslund, T. B., Gade, R., Special issue on Advanced Machine Vision, Mach. Vis. Appl., 2020, 31 (3).
2. [2]. Masood, H., Trujillo, F. J., Engineering Properties of Foods, Reference Module in Food Science, 2016, Elsevier.
3. [3]. Deniz, C., A Newton-Raphson Based Root Finding Algorithm Design and its Applications to Circular Waveguides, El-Cezerî Journal of Science and Engineering, 2017, 4(1); 32-45.
4. [4]. Sadrnia, H., Rajabipour, A., Jafary, A., Javadi, A., Mostofi, Y., Classification and analysis of fruit shapes in long type watermelon using image processing, Int. J. Agric. Biol., 2007, 1 (9), 68–70.
5. [5]. Patel, V. C., McClendon, R. W., Goodrum, J. W., Development and evaluation of an expert system for egg sorting, Comput. Electron. Agric., 1998, 20 (2), 97–116.
6. [6]. Bhowmik, P., Pantho, M. J. H., Bobda, C., Hierarchical Design of a Secure Image Sensor with Dynamic Reconfiguration, J. Signal Process. Syst., 2020, 92 (9), 999–1015.
7. [7]. Liesch, N., The BMP File Format, Dr. Dobb’s Journal of Software Tools, 1994, 19 (10), 18-22, 82-85.
8. [8]. Pulli, K., Baksheev, A., Kornyakov, K., Eruhimov, V., Real-time computer vision with OpenCV, Commun. ACM., 2012, 55 (6), 61.

9. [9]. Kilic, K., Boyaci, I. H., Koksel, H., Kusmenoglu, I., A classification system for beans using computer vision system and artificial neural networks, J. Food Eng., 2007, 78 (3), 897–904.
10. [10]. Brosnan T. and Sun, D.-W., Inspection and grading of agricultural and food products by computer vision systems—a review, Comput. Electron. Agric., 2002, 36, (2–3), 193–213.
11. [11]. Wu D., Sun, D.-W., Colour measurements by computer vision for food quality control – A review, Trends Food Sci. Technol., 2013, 29 (1) 5–20.
12. [12]. Hecht, V., Rönner, K., Pirsch, P., A defect-tolerant systolic array implementation for real time image processing, VLSI Signal Process, 1993, 5(1): 37-47.
13. [13]. Bradski G., Kaehler, A., Learning OpenCV, Computer Vision with OpenCV Library, O'Reilly Media, 2008, Sebastopol, ISBN: 978-0596516130.
14. [14]. Güvenoğlu, E., Bağırgan, M., Real-time Error Detection on Jeans Fabrics Using Shearlet Transform and Image Processing Techniques, El-Cezerî Journal of Science and Engineering, 2019, 6(3); 491-502.
15. [15]. Reis G.D., Stroustrup, B., A Principled, Complete, and Efficient Representation of C++, Math. Comput. Sci., 2011, 5 (3), 335–356.
16. [16]. Stroustrup, B., The C++ Programming Language 3rd Edition, Addison-Wesley Professional, 1989, USA, ISBN: 978-0201700732.
17. [17]. Munoz, D., Bouchereau, F., Vargas, C., Enriquez, R., The Position Location Problem, Position Location Techniques and Applications, 2009, Ch 1, pp: 1-22, Elsevier.
18. [18]. Hsieh, T. W., Taur, J. S., Kung, S. Y., A KNN-Scoring Based Core-Growing Approach to Cluster Analysis, J. Signal Process. Syst., 2010, 60 (1), 105–114.
19. [19]. Kaya D. Turk M., Kaya T., Examining the Effect of Dimension Reduction on EEG Signals by K-Nearest Neighbors Algorithm, El-Cezerî Journal of Science and Engineering, 2018, 5(2); 591-595.
20. [20]. Xu, L., Zhao, J., Yao, Z., Shi, A., Chen, Z., Density Peak Clustering Based on Cumulative Nearest Neighbors Degree and Micro Cluster Merging, J. Signal Process. Syst., 2019, 91 (10), 1219–1236.
21. [21]. Przybył, L., Gawałek, K., Koszela, J., Wawrzyniak, K., Gierz, J., Artificial neural networks and electron microscopy to evaluate the quality of fruit and vegetable spray-dried powders. Case study: strawberry powder, Comput. Electron. Agric., 2018, 155, 314–323.
22. [22]. İban, M.C., Şentürk, E., The Prediction of Ionospheric Parameters Using Multi-layer Perceptrons, El-Cezerî Journal of Science and Engineering, 2021, 8(3); 1480-1494.
23. [23]. Vapnik, V., The nature of statistical learning theory, Springer-Verlag, New York, 2013, ISBN: 978-1-4419-3160-3.
24. [24]. Foody, A., Mathur, G.M., Toward intelligent training of supervised image classifications: directing training data acquisition for SVM classification, Remote Sens. Environ., 2004, 93, 107–117.
25. [25]. Turkoglu Elitas, M. N., Ersoz, F., Investigation the Biomass in OECD Countries and Turkey: Comparative Analysis with Classification Algorithms, El-Cezerî Journal of Science and Engineering 2021, 8 (3); 1170-1183.
26. [26]. Kotsiantis, P., Zaharakis, S.B., Pintelas, I., Supervised machine learning: a review of classification techniques, Emerg. Artif. Intell. Appl. Comput. Eng., 2007, 160, 3–24.
27. [27]. Alpaydın, E., Introduction to Machine Learning, The MIT Press, 2009, ISBN: 0-262-01211-1.
28. [28]. Lin, C. S., Yeh, P. T., Chen, D. C., Chiou, Y. C., Lee, C. H., The identification and filtering of fertilized eggs with a thermal imaging system, Comput. Electron. Agric., 2013, 91, 94–105.
29. [29]. Zhang, Y., Wang, S., Ji, G., Phillips, P., Fruit classification using computer vision and feedforward neural network, J. Food Eng., 2014, 143, 167-177.
30. [30]. Golnabi H., Asadpour, A., Design and application of industrial machine vision systems”. Robot. Comput. Integr. Manuf., 2007, 23 (6), 630–637.