Journal of Electrical Engineering and Technology

  • 제7권6호
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  • Pages.1001-1008
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  • 2012
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  • 1975-0102(pISSN)
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  • 2093-7423(eISSN)
대한전기학회 (The Korean Institute of Electrical Engineers)
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Segmentation of Welding Defects using Level Set Methods

  • Mohammed, Halimi (Image and Signal Processing Laboratory. Welding and Control Research Center) ;
  • Naim, Ramou (Image and Signal Processing Laboratory. Welding and Control Research Center)
  • 투고 : 2011.12.26
  • 심사 : 2012.08.07
  • 발행 : 2012.11.01
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초록

Non-destructive testing (NDT) is a technique used in science and industry to evaluate the properties of a material without causing damage. In this paper we propose a method for segmenting radiographic images of welding in order to extract the welding defects which may occur during the welding process. We study different methods of level set and choose the model adapted to our application. The methods presented here take the property of local segmentation geodesic active contours and have the ability to change the topology automatically. The computation time is considerably reduced after taking into account a new level set function which eliminates the re-initialization procedure. Satisfactory results are obtained after applying this algorithm both on synthetic and real images.

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참고문헌

  1. M. Kass, A. Witkin, D. Terzopoulos, Snakes: Active Contour Models, International Journal of Computer Vision 1 (1987), 321-331.
  2. S. Osher, J. A. Sethian, Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations, Journal of Computational Physics 79 (1988), 12-49. https://doi.org/10.1016/0021-9991(88)90002-2
  3. X. Liu, S. Osher, T. Chan, Weighted Essentially Non- Oscillatory Schemes, Journal of Computational Physics 115 (1994), 200-212. https://doi.org/10.1006/jcph.1994.1187
  4. S. Osher, R. Fedkiw, Level set methods and dynamic implicit surfaces, Springer-Verlag, New York (2003).
  5. J. A. Sethian, Level set methods: Evolving interfaces in geometry, fluid mechanics, computer vision, and materials science, Cambridge University Press, Cambridge (1996).
  6. L. Alvarez, P.L. Lions, J.M. Morel, Image selective Smoothing and edge detection by nonlinear diffusion, SIAM J. Numer. Anal., Vol. 29, no.3 (1992), 845-866. https://doi.org/10.1137/0729052
  7. V. Casselles, R. Kimmel and G. Sapiro. Geodesic Active Contours. International Journal of Computer Vision 22(1), (1997), 61-79. https://doi.org/10.1023/A:1007979827043
  8. F. Cao, Geometric Curve Evolution and Image Processing, Springer-Verlag, Berlin (2003).
  9. G. Aubert, P. Kornprobst, Mathematical Problems in Image Processing. Partial Differential Equations and the Calculus of Variations, Second edition Ed. Springer (2006).
  10. G. Jiang,C. Shu, Efficient Implementation of Weighted ENO Schemes, Journal of Computational Physics 126, Art. 0130, (1996), 202-228. https://doi.org/10.1006/jcph.1996.0130
  11. C. Shu, Essentially Non-Oscillatory and Weighted Essentially Non-Oscillatory Schemes for Hyperbolic Conservation Laws, ICASE Report 97-65, NASA/ CR-97-206253, NASA, Langley Research Center (1997).
  12. C. Li, C. Xu, C. Gui, and M.D. Fox, Level set evolution without re-initialization: a new variational formulation, Proc. of the IEEE Computer Society on Computer Vision and Patern Recognition, San Diego, USA (2005).
  13. J. Lie, M. Lysaker, and X.C. Tai, A variant of the level set method and applications to image segmentation, Mathematics of computation 75(255) (2006).
  14. Chunming Li, Chenyang Xu, Changfeng Gui, and Martin D. Fox. Distance Regularized Level Set Evolution and Its Application to Image Segmentation, IEEE transactions on image processing, vol. 19, no.12, december (2010).

피인용 문헌

  1. A Bayesian Mumford–Shah Model for Radiography Image Segmentation 2017, https://doi.org/10.1007/s13369-017-3031-z