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Dual-Resolution Active Contours Segmentation of Vickers Indentation Images with Shape Prior Initialization

  • Michael Gadermayr
  • Andreas Uhl
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7340)

Abstract

Vickers microindentation imagery is segmented using the Chan-Vese level-set approach. In order to find a suitable initialization, we propose to apply a Shape-Prior gradient descent approach to a significantly resolution-reduced image. Subsequent local Hough transform leads to a very high accuracy of the overall approach.

Keywords

Gradient Descent Vickers Hardness Active Contour Template Match Active Contour Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Gadermayr, M., Maier, A., Uhl, A.: Algorithms for microindentation measurement in automated Vickers hardness testing. In: Pinoli, J.C., Debayle, J., Gavet, Y., Cruy, F., Lambert, C. (eds.) Tenth International Conference on Quality Control for Artificial Vision (QCAV 2011). Proceedings of SPIE, vol. 8000, pp. 80000M–1–80000M–10. SPIE, St. Etienne (2011)Google Scholar
  2. 2.
    Maier, A., Uhl, A.: Robust automatic indentation localisation and size approximation for vickers microindentation hardness indentations. In: Proceedings of the 7th International Symposium on Image and Signal Processing (ISPA 2011), Dubrovnik, Croatia, pp. 295–300 (September 2011)Google Scholar
  3. 3.
    Ji, Y., Xu, A.: A new method for automatically measurement of vickers hardness using thick line hough transform and least square method. In: Proceedings of the 2nd International Congress on Image and Signal Processing (CISP 2009), pp. 1–4 (2009)Google Scholar
  4. 4.
    Yao, L., Fang, C.-H.: A hardness measuring method based on hough fuzzy vertex detection algorithm. IEEE Trans. on Industrial Electronics 53(3), 963–973 (2006)CrossRefGoogle Scholar
  5. 5.
    Macedo, M., Mendes, V.B., Conci, A., Leta, F.R.: Using hough transform as an auxiliary technique for vickers hardness measurement. In: Proceedings of the 13th International Conference on Systems, Signals and Image Processing (IWSSIP 2006), pp. 287–290 (2006)Google Scholar
  6. 6.
    Mendes, V., Leta, F.: Automatic measurement of Brinell and Vickers hardness using computer vision techniques. In: Proceedings of the XVII IMEKO World Congress, Dubrovnik, Croatia, pp. 992–995 (June 2003)Google Scholar
  7. 7.
    Sugimoto, T., Kawaguchi, T.: Development of an automatic Vickers hardness testing system using image processing technology. IEEE Transactions on Industrial Electronics 44(5), 696–702 (1997)CrossRefGoogle Scholar
  8. 8.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. International Journal of Computer Vision 1(4), 321–331 (1988)CrossRefGoogle Scholar
  9. 9.
    Osher, S., Sethian, J.A.: Fronts propagating with curvature-dependent speed: Algorithms based on hamilton-jacobi formulations. Journal of Computational Physics 79(1), 12–49 (1988)MathSciNetzbMATHCrossRefGoogle Scholar
  10. 10.
    Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. International Journal of Computer Vision 22(1), 61–79 (1997)zbMATHCrossRefGoogle Scholar
  11. 11.
    Chan, T., Vese, A.: Active contours without edges. IEEE Transactions on Image Processing 10(2), 266–277 (2001)zbMATHCrossRefGoogle Scholar
  12. 12.
    Cremers, D., Rousson, M., Deriche, R.: A review of statistical approaches to level set segmentation: integrating color, texture, motion and shape. International Journal of Computer Vision 72(2), 195–215 (2006)CrossRefGoogle Scholar
  13. 13.
    Cohen, L.: On active contour models and balloons. CVGIP: Graphical Models and Image Processing 53(2), 211–218 (1991)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Michael Gadermayr
    • 1
  • Andreas Uhl
    • 1
  1. 1.Department of Computer SciencesSalzburg UniversityAustria

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