Search results

Filters

  • Journals
  • Authors
  • Keywords
  • Date
  • Type

Search results

Number of results: 1
items per page: 25 50 75
Sort by:
Download PDF Download RIS Download Bibtex

Abstract

In the ceramic industry, quality control is performed using visual inspection in three different product stages: green, biscuit, and the final ceramic tile. To develop a real-time computer visual inspection system, the necessary step is successful tile segmentation from its background. In this paper, a new statistical multi-line signal change detection (MLSCD) segmentation method based on signal change detection (SCD) method is presented. Through experimental results on seven different ceramic tile image sets, MLSCD performance is analyzed and compared with the SCD method. Finally, recommended parameters are proposed for optimal performance of the MLSCD method.
Go to article

Bibliography

  1.  G.M.A. Rahaman and M. Hossain, “Automatic Defect Detection and Classification Technique from Image: A Special Case Using Ceramic”, Int. J. Comput. Sci Inf. Secur. 1(1), 9 (2009).
  2.  M. Villalon-Hernandez, D. Almanza-Ojeda, and M. Ibarra- Manzano, “Color-Texture Image Analysis for Automatic Failure Detection in Tiles”, in Pattern Recognition, MCPR 2017. Lecture Notes in Computer Science, vol. 10267, pp. 159–168, eds. J.A. Carrasco-Ochoa, J.F. Martínez-Trinidad, and J.A. Olvera-López, Springer International Publishing, Cham, 2017.
  3.  M.H. Karimi and D. Asemani, “Surface defect detection in tiling industries using digital image processing methods: Analysis and evaluation”, ISA Trans. 53(3), 834‒844 (2014).
  4.  F.S. Najafabadi and H. Pourghassem, “Corner defect detection based on dot product in ceramic tile images”, In 2011 IEEE 7th International Colloquium on Signal Processing and its Applications, Penang, Malaysia, 2011, pp. 293–297.
  5.  R. Mishra, C.L. Chandrakar, and R. Mishra, “Surface Defects Detection for Cermaic Tiles Using Image Processing and Morphological Techniques”, Appl. Sci. 2(2), 17 (2012).
  6.  T. Czimmermann et al., “Visual-Based Defect Detection and Classification Approaches for Industrial Applications – A SURVEY”, Sensors 20(5), 1459, (2020).
  7.  V. Lebedev and V. Lempitsky, “Speeding-up convolutional neural networks: A survey”, Bull. Pol. Acad. Sci. Tech. Sci. 66(6), 799–810 (2018).
  8.  N. Wang, X. Zhao, Z. Zou, P. Zhao, and F. Qi, “Autonomous damage segmentation and measurement of glazed tiles in historic buildings via deep learning”, Comput.-Aided Civil Infrastruct. Eng. 35(3), 277–291 (2020).
  9.  X. Gu and Y. Sun, “Image analysis of ceramic burning based on cellular automata”, EURASIP J. Image Video Process. 2018(1), 110 (2018).
  10.  T. Matić, I. Vidović, and Ž. Hocenski, “Real Time Contour Based Ceramic Tile Edge and Corner Defects Detection”, Teh. Vjesn.-Technical Gazette 20(6), 8 (2013).
  11.  Ž. Hocenski, T. Keser, and A. Baumgartner, “A Simple and Efficient Method for Ceramic Tile Surface Defects Detection”, In 2007 IEEE International Symposium on Industrial Electronics, Vigo, Spain, 2007, pp. 1606–1611.
  12.  T. Matić, I. Aleksi, and Ž. Hocenski, “CPU, GPU and FPGA Implementations of MALD: Ceramic Tile Surface Defects Detection Algorithm”, Automatika 55(1), 9–21 (2014).
  13.  J. Zhuang, L. Yang, and J. Li, “An Improved Segmentation Algorithm Based on Superpixel for Typical Industrial Applications”, In 2018 11th International Symposium on Computational Intelligence and Design (ISCID), Hangzhou, China, 2018, pp. 366–370.
  14.  X. Zhou, Y. Wang, Q. Zhu, J. Mao, C. Xiao, X. Lu, and H. Zhang, “A Surface Defect Detection Framework for Glass Bottle Bottom Using Visual Attention Model and Wavelet Transform”, IEEE Trans. Ind. Inform. 16(4), 2189–2201 (2020).
  15.  X. Yan, L. Wen, and L. Gao, “A Fast and Effective Image Preprocessing Method for Hot Round Steel Surface”, Math. Probl. Eng., 2019, 1–14 (2019).
  16.  R. Cunha et al., Applying Non-destructive Testing and Machine Learning to Ceramic Tile Quality Control”, In 2018 VIII Brazilian Symposium on Computing Systems Engineering (SBESC), Salvador, Brazil, 2018, pp. 54–61.
  17.  T. Matić, I. Aleksi, Ž. Hocenski, and D. Kraus, “Real-time biscuit tile image segmentation method based on edge detection”, ISA Transactions 76, 246–254 (2018).
  18.  S.M. Kay, Fundamentals of Statistical Signal Processing: Practical Algorithm Development, Prentice-Hall PTR, 2013.
  19.  L.G. Shapiro and R.M. Haralick, Computer and Robot Vision, Addison-Wesley Publishing Company, 1992.
  20.  Z. Hocenski and T. Keser, “Failure detection and isolation in ceramic tile edges based on contour descriptor analysis”, In 2007 Mediterranean Conference on Control & Automation, Athens, Greece, 2007, pp. 1–6.
  21.  R.C. Gonzalez and R.E.Woods, Digital Image Processing, Pearson, 2018.
  22.  Ž. Hocenski, T. Matić, and I. Vidović, “Technology transfer of computer vision defect detection to ceramic tiles industry”, In 2016 International Conference on Smart Systems and Technologies (SST), Osijek, Croatia, 2016, pp. 301–305.
  23.  M. Montorsi, C. Mugoni, A. Passalacqua, A. Annovi, F. Marani, L. Fossa, R. Capitani, and T. Manfredini, “Improvement of color quality and reduction of defects in the ink jet-printing technology for ceramic tiles production: A design of experiments study”, Ceram. Int. 42(1, Part B), 1459–1469 (2016).
  24.  The GIMP Development Team. GIMP, 2019.
  25.  A.Z. Arifin and A. Asano, “Image Segmentation by Histogram Thresholding Using Hierarchical Cluster Analysis”, Pattern Recognit. Lett., 27(13), 1515–1521 (2006).
Go to article

Authors and Affiliations

Filip Sušac
1
Tomislav Matić
1
Ivan Aleksi
1
Tomislav Keser
1

  1. J. J. Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Kneza Trpimira 2B, 31000 Osijek, Croatia

This page uses 'cookies'. Learn more