THE SYSTEM RECOGNIZES SURFACE DEFECTS OF MARBLE SLABS BASED ON SEGMENTATION METHODS | Sipko | Scientific Journal of Astana IT University

THE SYSTEM RECOGNIZES SURFACE DEFECTS OF MARBLE SLABS BASED ON SEGMENTATION METHODS

E. Sipko, O. Kravchenko, A. Karapetyan, Zh. Plakasova, M. Gladka

Аннотация


A system for recognizing surface defects in marble slabs is proposed. The pattern recognition method based on segmentation methods was further developed. The algorithm of the recognition system. The article describes methods for determining damage from digital images on various hard surfaces. Research in this field is relevant for a wide range of industrial enterprises that specialize in the production of various kinds of materials: parts, marble slabs, building materials, etc. To solve this problem, it is proposed to use the k-means clustering method. It has been experimentally established that Gaussian blurring algorithms, the Hough transform, and the Kenny algorithm are best suited for recognizing defects on the surface of a marble slab. The developed complex method based on the theory of pattern recognition allows you to quickly identify defects and damage on the surfaces of marble slabs. On the basis of the method, a system for understanding defects is implemented in software. The main stages of the system are described in the article. The results of the analysis of the image of the surface of the marble slab on a specific example are presented. The developed complex method based on the theory of pattern recognition allows you to quickly identify defects and damage on the surfaces of marble slabs. On the basis of the method, a system for understanding defects is implemented in software. The main stages of the system are described in the article. The results of the analysis of the image of the surface of the marble slab on a specific example are presented.

Ключевые слова


vision system, segmentation, adaptation, method, recognition, digital image.

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Литература


Gavil’an, M. (2011). Adaptive road crack detection system by pavement classification / M.

Gavil’an, D. Balcones, O. Marcos et al. // Sensors, 10, 9628–9657; DOI 10.3390/s111009628

Konovalenko, I., Maruschak, Menou, P., Karuskevich, A., Ignatovich, S. (2013). A novel algorithm for damage analysis of fatigue sensor by surface deformation relief parameters.

In.: Proc. of International Symposium “Operational research and applications”, Marrakech,

Morocco, May 08-10, 2013, p. 678-684.

Maruschak, P.O., Panin, S.V., Ignatovich, S.R., Zakiev, I.M., Konovalenko, I.V., Lytvynenko, I.V.,

and Sergeev, V.P. (2012). Influence of deformation process in material at multiple cracking

and fragmentation of nanocoat ing, Theor. Appl. Fract. Mechan., vol. 57, pp. 43-48.

Smelyakov K., Romanenko I., Ruban I. (2010). Methods of segmentation of images of irregular-looking objects, peculiarities of their application and prospects for development.

Proceedings of Kharkiv University of the Air Force. – Issue, 2 (24). – pp. 92-97.

Bartalev S., Khovratovich T. (2011). Analiz vozmozhnostei primeneniya metodov segmentatsii

dlya vyyavleniya izmenenii v lesakh [Analysis of the possibilities of applying satellite

image segmentation techniques to detect changes in forests]. Sovremennyye problemy

distantsionnogo zondirovaniya Zemli iz kosmosa – Modern problems of remote sensing of

the Earth from space. (Vol. 8), (pp. 44-62). [In Russian]

Samoylenko D.Ye. (2004). Strukturnaya segmentatsiya izobrazheniy [Structural Image

Segmentation]. // Shtuchnyy intelekt – Artificial Intelligence, 4, 521-528. [in Russian]

Whitey, D.J. & Koles, Z.J. (2008). A review of Medical Image segmentation: Methods and

available software. International Journal of Bioelectromagnetism (Vol. 10, 3), pp. 125-148.

Duda R., Khart P. (2013). Raspoznavaniye obrazov i analiz stsen [Pattern Recognition and

Scene Analysis]. Moscow: Kniga po Trebovaniyu. [in Russian]

Jain, A.K. (2010). Data clustering: 50 years beyond K-means. Pattern recognition letters.

(Vol. 31, 8), pp. 651-666.

Xu, R. & Wunsch D. (2005). Survey of clustering algorithms. IEEE Transactions, Neural

Networks. (Vol. 16, 3), pp. 645-678.

Kashef, R. & Kamel, M.S. (2010). Cooperative clustering. Pattern Recognition. (Vol. 43, 6),

pp. 2315-2329.

Oliveira, H. and Correia, P.L. (2013). Automatic road crack detection and charac-terization.

IEEE Trans. Intell. Transp. Syst., vol. 14, no. 1, pp. 155-168, Mar. 2013.

Avila, S. Begot, F. Duculty, and T. S. Nguyen. (2014). 2D imagebased road pavement crack

detection by calculating minimal pathsand dynamic programming. In Proc. Int. Conf. Image

Process., pp. 783-787.

Delling, D., Sanders, P., Schultes, D. and Wagner, D. (2009). Engineering routeplanning

algorithms. In Algorithmics of Large and Complex Networks, ser. Lecture Notes in Computer

Science, vol. 5515. Berlin, Germany:Springer-Verlag, pp. 117-139.

Belongie S., Mori G. & Malik J. (2006). Matching with shape contexts. In Statistics and

Analysis of Shapes. 105.

Frucci, M., Sanniti di Baja, G. (2008). From Segmentation to Binarization of Gray-level

Images. Journal of Pattern Recognition Research. 3 (1): 1-13. DOI:10.13176/11.54




DOI: http://dx.doi.org/10.37943/AITU.2020.1.63643

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