Research Article
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Year 2019, Volume: 2 Issue: 1, 21 - 26, 30.01.2019

Abstract

References

  • [1]. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Derin Öğrenme. Buzdağı Yayınevi, Ankara.
  • [2]. Şişeci, M., & Cetişli, B. (2012). Traverten plaka taşlarda sınıfların K-ortalamalar ve bulanık C-ortalamalar kümeleme yöntemleri ile belirlenmesi. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 16(3), 238-247.
  • [3]. Martinez-Alajarin, J., Luis-Delgado, J. D., & Tomas-Balibrea, L. M. (2005). Automatic system for quality-based classification of marble textures. IEEE Transactions on Systems, Man, and Cybernetics, Part C, 35(4), 488–497.
  • [4]. Selver, A. M. & Akay, O. (2009). Evaluating clustering methods for classification of marble slabs in an automated industrial marble inspection system. Int. Conf. On Electrical and Electronics Engineering, ELECO, 115-119.
  • [5]. Benavente, N. & Pina, P. (2009). Morphological segmentation and classification of marble textures at macroscopical scale. Computers & Geosciences, 35(6), 1194-1204.
  • [6]. López, M., Martínez, J., Matías, J. M., Taboada, J., & Vilán, J. A. (2010). Functional classification of ornamental stone using machine learning techniques. Journal of Computational and Applied Mathematics, 234(4), 1338-1345.
  • [7]. Şişeci, M., Metlek, S., & Cetişli, B. (2014). Accelerating the image segmentation using sub-block technique and clustering methods. Journal of the Faculty of Engineering and Architecture of Gazi University, 29(4), 655-664.
  • [8]. Turhal, Ü. Ç., Düğüncü, S., & Dener, G. (2015). Mermer plakalarında görüntü işleme teknikleri ile yüzey pürüzlülüğünün değerlendirilmesi. Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi, 2(1), 9-18.
  • [9]. Moghaddam, H. K., Rajaei, A., & Moghaddam, H. K. (2018). Marble slabs classification system based on image processing (ark marble mine in Birjand). Civil Engineering Journal, 4(1), 107-116.
  • [10]. Kemaloglu, N., Aydogan, T., & Metlek, S. (2018). Classification of travertine tiles with supervised and unsupervised classifiers and quality control. International Journal of Engineering and Technology, 10(3), 221-226.
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  • [13]. Ranzato, M. A., Poultney, C., Chopra, S., & Cun, Y. L. (2007). Efficient learning of sparse representations with an energy-based model. In Advances in neural information processing systems, 1137-1144.
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  • [21]. Gonzalez, R. C., & Woods, R. E. (2014). Sayısal Görüntü İşleme. Palme Yayınevi, Ankara.
  • [22]. Espinoza, M. A., Alférez, G. H., & Castillo, J. (2018). Prediction of Glaucoma through Convolutional Neural Networks. Int'l Conf. Health Informatics and Medical Systems.
  • [23]. Mathworks, (2018). The MathWorks, Inc. Erişim tarihi: 01.11.2018. https://www.mathworks.com/help/deeplearning/ug/introduction-to-convolutional-neural-networks.html

Deep Learning in Marble Slabs Classification

Year 2019, Volume: 2 Issue: 1, 21 - 26, 30.01.2019

Abstract

The process of classification of marble slabs has an
important place in terms of construction sector and demands. Despite the
advanced mines and construction equipment in Turkey and the world, the
separation of cut marble process is a problem that has not been solved yet. The
lack of a standard for the classification of marbles and the use of human
factors for this process lead to erroneous and inefficient determinations. In
this study, for the first time the Deep Learning method has been tried on
marbles, and the components obtained from Deep Learning layers have been
examined and the success of classification has measured. Thanks to the
successful results, the basics of the Deep Learning network have been laid for
future marble databases.

References

  • [1]. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Derin Öğrenme. Buzdağı Yayınevi, Ankara.
  • [2]. Şişeci, M., & Cetişli, B. (2012). Traverten plaka taşlarda sınıfların K-ortalamalar ve bulanık C-ortalamalar kümeleme yöntemleri ile belirlenmesi. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 16(3), 238-247.
  • [3]. Martinez-Alajarin, J., Luis-Delgado, J. D., & Tomas-Balibrea, L. M. (2005). Automatic system for quality-based classification of marble textures. IEEE Transactions on Systems, Man, and Cybernetics, Part C, 35(4), 488–497.
  • [4]. Selver, A. M. & Akay, O. (2009). Evaluating clustering methods for classification of marble slabs in an automated industrial marble inspection system. Int. Conf. On Electrical and Electronics Engineering, ELECO, 115-119.
  • [5]. Benavente, N. & Pina, P. (2009). Morphological segmentation and classification of marble textures at macroscopical scale. Computers & Geosciences, 35(6), 1194-1204.
  • [6]. López, M., Martínez, J., Matías, J. M., Taboada, J., & Vilán, J. A. (2010). Functional classification of ornamental stone using machine learning techniques. Journal of Computational and Applied Mathematics, 234(4), 1338-1345.
  • [7]. Şişeci, M., Metlek, S., & Cetişli, B. (2014). Accelerating the image segmentation using sub-block technique and clustering methods. Journal of the Faculty of Engineering and Architecture of Gazi University, 29(4), 655-664.
  • [8]. Turhal, Ü. Ç., Düğüncü, S., & Dener, G. (2015). Mermer plakalarında görüntü işleme teknikleri ile yüzey pürüzlülüğünün değerlendirilmesi. Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi, 2(1), 9-18.
  • [9]. Moghaddam, H. K., Rajaei, A., & Moghaddam, H. K. (2018). Marble slabs classification system based on image processing (ark marble mine in Birjand). Civil Engineering Journal, 4(1), 107-116.
  • [10]. Kemaloglu, N., Aydogan, T., & Metlek, S. (2018). Classification of travertine tiles with supervised and unsupervised classifiers and quality control. International Journal of Engineering and Technology, 10(3), 221-226.
  • [11]. Hinton, G. E., Osindero, S., & Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural computation, 18(7), 1527-1554.
  • [12]. Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2007). Greedy layer-wise training of deep networks. In Advances in neural information processing systems, 153-160.
  • [13]. Ranzato, M. A., Poultney, C., Chopra, S., & Cun, Y. L. (2007). Efficient learning of sparse representations with an energy-based model. In Advances in neural information processing systems, 1137-1144.
  • [14]. Bengio, Y., & LeCun, Y. (2007). Scaling learning algorithms towards AI. Large-scale kernel machines, 34(5), 1-41.
  • [15]. Delalleau, O., & Bengio, Y. (2011). Shallow vs. deep sum-product networks. In Advances in Neural Information Processing Systems, 666-674.
  • [16]. Pascanu, R., Gulcehre, C., Cho, K., & Bengio, Y. (2014). How to construct deep recurrent neural networks. In Proceedings of International Conference on Learning Representations (ICLR).
  • [17]. Montufar, G. F., Pascanu, R., Cho, K., & Bengio, Y. (2014). On the number of linear regions of deep neural networks. In Advances in neural information processing systems, 2924-2932.
  • [18]. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097-1105).
  • [19]. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., ... & Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, 1-9.
  • [20]. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., & Fei-Fei, L. (2015). ImageNet large scale visual recognition challenge. IJCV.
  • [21]. Gonzalez, R. C., & Woods, R. E. (2014). Sayısal Görüntü İşleme. Palme Yayınevi, Ankara.
  • [22]. Espinoza, M. A., Alférez, G. H., & Castillo, J. (2018). Prediction of Glaucoma through Convolutional Neural Networks. Int'l Conf. Health Informatics and Medical Systems.
  • [23]. Mathworks, (2018). The MathWorks, Inc. Erişim tarihi: 01.11.2018. https://www.mathworks.com/help/deeplearning/ug/introduction-to-convolutional-neural-networks.html
There are 23 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Original Research Articles
Authors

İhsan Pençe 0000-0003-0734-3869

Melike Şişeci Çeşmeli 0000-0001-9541-2590

Publication Date January 30, 2019
Acceptance Date January 25, 2019
Published in Issue Year 2019 Volume: 2 Issue: 1

Cite

APA Pençe, İ., & Şişeci Çeşmeli, M. (2019). Deep Learning in Marble Slabs Classification. Scientific Journal of Mehmet Akif Ersoy University, 2(1), 21-26.