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Real-time image texture analysis in quality management using grid computing: an application to the MDF manufacturing industry

  • A. J. Sánchez Santiago
  • A. J. Yuste
  • J. E. Muñoz Expósito
  • Sebastian García GalánEmail author
  • R. P. Prado
  • J. M. Maqueira
  • S. Bruque
Original Article

Abstract

Computer vision has arisen as one of the most important application areas in manufacturing processes. This work shows a new real-time texture analysis for medium-density fiberboard with melamine paper, using edge detection techniques and threshold detection methods. To minimize the time of identification of defects, the images of fiberboard are sent to a grid system. In a first phase, several tests are carried out using different image resolutions and sizes. In a second phase, to optimize the system, with the best resolution obtained and using a grid system, our aim is to minimize the time of detection of possible defects without jeopardizing the performance of the quality control system. Results show that, using accurate resolutions, the error detection process is quicker and the defect identification rate significantly improves.

Keywords

Grid computing Computer vision Texture analysis Edge detection 

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References

  1. 1.
    Sonka M, Hlavac V, Boyle R (1999) Image processing analysis and machine vision. Chapman & Hall, LondonGoogle Scholar
  2. 2.
    Deng JD, Gleeson MT (2007) Automatic sapstain detection in processed timber. Lect Notes Comput Sci 4830/2007:637–641CrossRefGoogle Scholar
  3. 3.
    Huang CY, Holt A, Monk J, Cheng K (2007) The application of dependency management in an integrated manufacturing network framework. Int J Adv Manuf Technol 33(3):354–364CrossRefGoogle Scholar
  4. 4.
    Foster I, Kesselman C (2003) The grid 2: blueprint for a new computing infrastructure. Morgan Kaufmann, San MateoGoogle Scholar
  5. 5.
    Anderson D (2004) Boinc: a system for public-resource computing and storage. In: Fifth IEEE/ACM international workshop on grid computing, pp 4–10Google Scholar
  6. 6.
    Allen M (1999) Do-it-yourself climate prediction. Nature 401(6754):642CrossRefGoogle Scholar
  7. 7.
    Sintes AM (2006) Recent results on the search for continuous sources with LIGO and GEO600. J Phys 39:36–38CrossRefGoogle Scholar
  8. 8.
    Fan J, Yau DKY, Elmagarmid AK, Aref WG (2001) Automatic image segmentation by integrating color-edge extraction and seeded region growing. IEEE Trans Image Process 10:1454–1466zbMATHCrossRefGoogle Scholar
  9. 9.
    Niskanen M, Silvn O, Kauppinen H (2001) Color and texture based wood inspection with non-supervised clustering. In: Proceedings of the 12th Scandinavian conference on image analysis (SCIA2001), pp 336–342Google Scholar
  10. 10.
    Bharati MH, MacGregor JF, Tropper W (2003) Softwood lumber grading through on-line multivariate image analysis techniques. Ind Eng Chem Res 42(21):5345–5353CrossRefGoogle Scholar
  11. 11.
    Silvén O, Niskanen M, Kauppinen H (2003) Wood inspection with non-supervised clustering. Mach Vision Appl 13(5–6):275–285CrossRefGoogle Scholar
  12. 12.
    Killing J, Surgenorv B, Mechefske C (2009) A machine vision system for the detection of missing fasteners on steel stampings. Int J Adv Manuf Technol 41(7–8):808–819CrossRefGoogle Scholar
  13. 13.
    Pernkopf F (2004) Detection of surface defects on raw steel blocks using Bayesian network classifiers. Pattern Anal Appl 7(3):333–342MathSciNetGoogle Scholar
  14. 14.
    Murino V, Bicego M, Rossi I (2004) Statistical classification of raw textile defects. In: Proceedings of the 17th international conference on pattern recognition, 2004. ICPR 2004, vol 4, pp 311–314Google Scholar
  15. 15.
    Zhai M, Jing Z, Fu S, Luo X (2009) Defect detection in aluminum foil by input-estimate-based chi-square detector. Opt Eng 48(11):119–129CrossRefGoogle Scholar
  16. 16.
    Smith ML, Stamp RJ (2000) Automated inspection of textured ceramic tiles. Comput Ind 43(1):73–82CrossRefGoogle Scholar
  17. 17.
    Chen X, Jing H, Tao Y, Cheng X (2005) Real-time image analysis for nondestructive detection of metal slivers in packed food. In: Chen Y-R, Meyer GE, Tu S-I (eds) Optical sensors and sensing systems for natural resources and food safety and quality. Proceedings of the SPIE, volume 5996. SPIE, Bellingham, pp 120–129Google Scholar
  18. 18.
    Patel VC, McClendon RW, Goodrum JW (1998) Crack detection in eggs using computer vision and neural networks. Artif Intell Rev 8(2):21–31Google Scholar
  19. 19.
    Neubauer C (1997) Intelligent x-ray inspection for quality control of solder joints. IEEE Trans Compon Packaging Manuf Technol Part C 20(2):111–120CrossRefGoogle Scholar
  20. 20.
    Prasad BS, Sarcar MMM (2009) Experimental investigation to predict the condition of cutting tool by surface texture analysis of images of machined surfaces based on amplitude parameters. Int J Mach Machinability Mater 4(2–3):217–236Google Scholar
  21. 21.
    Yin Y, Lei J (2009) Prototype system of textile flaw detection based on wavelet reconstructions. J Inf Comput Sci 5(1):207–214Google Scholar
  22. 22.
    Ma L, Tan T, Wang Y, Zhang D (2003) Personal identification based on iris texture analysis. IEEE Trans Pattern Anal Mach Intell 25(12):1519–1533CrossRefGoogle Scholar
  23. 23.
    Leban J, Pizzi A, Wieland S, Zanetti M, Properzi, Pichelin F (2004) X-ray microdensitometry analysis of vibration-welded wood. J Adhes Sci Technol 18(6):673–685CrossRefGoogle Scholar
  24. 24.
    Simonaho SP, Palviainen J, Tolonen Y, Silvennoinen R (2004) Determination of wood grain direction from laser light scattering pattern. Opt Lasers Eng 41(1):95–103CrossRefGoogle Scholar
  25. 25.
    Lu R, Tian GY, Gledhill D, Ward S (2006) Grinding surface roughness measurement based on the co-occurrence matrix of speckle pattern texture. Appl Opt 45(35):8839–8847CrossRefGoogle Scholar
  26. 26.
    Ke J, Zhan Y, Chen X, Wang M (2009) Pseudo invariant line moment to detect the target region of moving vessels. Lect Notes Comput Sci 5754/2009:615–624CrossRefGoogle Scholar
  27. 27.
    Qu Z, Qiu G, Huang J (2009) Detect digital image splicing with visual cues. Lect Notes Comput Sci 5806/2009:247–261CrossRefGoogle Scholar
  28. 28.
    Ortalana V, Herrera M, Morgan D.G, Browning N.D (2009) Application of image processing to stem tomography of low-contrast materials. Ultramicroscopy 110(1):67–81CrossRefGoogle Scholar
  29. 29.
    Lau KK, Roberts S, Biro D, Freeman R, Meade J, Guilford T (2006) An edge-detection approach to investigating pigeon navigation. J Theor Biol 239(1):71–78MathSciNetCrossRefGoogle Scholar
  30. 30.
    Castellani M, Rowlands H (2009) Evolutionary artificial neural network design and training for wood veneer classification. Eng Appl Artif Intell 22(4-5):732–741CrossRefGoogle Scholar
  31. 31.
    Gu IYH, Andersson H, Vicen R (2009) Automatic classification of wood defects using support vector machines. Comput Vis Graph 5337/2009:356–367CrossRefGoogle Scholar
  32. 32.
    Thakur LS, Jain VK (2008) Advanced manufacturing techniques and information technology adoption in India: a current perspective and some comparisons. Int J Adv Manuf Technol 36(5):618–631CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  • A. J. Sánchez Santiago
    • 1
  • A. J. Yuste
    • 1
  • J. E. Muñoz Expósito
    • 1
  • Sebastian García Galán
    • 1
    Email author
  • R. P. Prado
    • 1
  • J. M. Maqueira
    • 1
  • S. Bruque
    • 1
  1. 1.Telecommunication Engineering DepartmentUniversidad de JaénLinaresSpain

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