Machine Vision and Applications

, Volume 24, Issue 6, pp 1183–1196 | Cite as

Active contours methods with respect to Vickers indentations

Original Paper

Abstract

We investigate different Vickers indentation segmentation methods and especially concentrate on active contours approaches as these techniques are known to be precise state of the art segmentation methods. Particularly, different kinds of level set-based methods which are improvements of the traditional active contours are analyzed. In order to circumvent the initialization problem of active contours, we separate the segmentation process into two stages. For the first stage, we introduce an approach which approximately locates the indentations with a high certainty. The results achieved with this method serve as initializations for the precise active contours (second stage). This two-stage approach delivers highly precise results for most real world indentation images. However, there are images, which are very difficult to segment. To handle even such images, our segmentation method is incorporated with the Shape from Focus approach, by including 3D information. In order to decrease the overall runtime, moreover, a gradual enhancement approach based on unfocused images is introduced. With three different databases, we compare the proposed methods and we show that the segmentation accuracy of these methods is highly competitive compared with other approaches in the literature.

Keywords

Vickers Active contours Focus Shape Prior 

Notes

Acknowledgments

This work has been partially supported by the Austrian Federal Ministry for Transport, Innovation and Technology (FFG Bridge 2 project no. 822682).

References

  1. 1.
    ASTM Standard E384 (2010e2): Standard test method for Knoop and Vickers hardness of materials. In: ASTM Standards, ASTM International, West Conshohocken, PA (2010). doi: 10.1520/E0384-10E02. http://www.astm.org
  2. 2.
    Liming, W., Qu, Z., Yaohua, D., Miaoxian, Z.: Automatically analyzing the impress image of Vickers hardness test using wavelet. China Mech. Eng. 15 (2006)Google Scholar
  3. 3.
    Qu, Z., Guozheng, Y., Yi, Z.: A new method for quickly and automatically analysis of the image of Vickers hardness using wavelet theory. Acta Metrologica Sinica 26, 245–248 (July 2005)Google Scholar
  4. 4.
    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’09), pp. 1–4 (2009)Google Scholar
  5. 5.
    Macedo, M., Mendes, V., Conci, A., Leta, F.: 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’06), 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, pp. 992–995 (2003)Google Scholar
  7. 7.
    Sugimoto, T., Kawaguchi, T.: Development of an automatic Vickers hardness testing system using image processing technology. IEEE Trans. Ind. Electr. 44, 696–702 (Oct. 1997)Google Scholar
  8. 8.
    Yao, L., Fang, C.-H.: A hardness measuring method based on hough fuzzy vertex detection algorithm. IEEE Trans. Ind. Electr. 53(3), 963–973 (2006)CrossRefGoogle Scholar
  9. 9.
    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), pp. 295–300 September (2011)Google Scholar
  10. 10.
    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.) 10th International Conference on Quality Control for Artificial Vision (QCAV’11), Proceedings of SPIE, 80000M–1 - 80000M–10, SPIE, St. Etienne, France June (2011)Google Scholar
  11. 11.
    Gadermayr, M., Maier, A., Uhl, A.: A robust algorithm for automated microindentation measurement in vickers hardness testing. J. Electr. Imaging (2012)Google Scholar
  12. 12.
    Gadermayr, M., Uhl, A.: Dual-resolution active contours segmentation of Vickers indentation images. In: International Conference on Image and, Signal Processing June (2012)Google Scholar
  13. 13.
    Gadermayr, M., Maier, A., Uhl, A.: Image segmentation of Vickers indentations using shape from focus. In: International Conference on Image Analysis and Recognition June (2012)Google Scholar
  14. 14.
    Gadermayr, M., Maier, A., Uhl, A.: The impact of unfocused Vickers indentation images on the segmentation performance. In: Bebis, G.e.a. (ed) Proceedings of the 8th International Symposium on Visual Computing (ISVC’12), Springer LNCS 7432, pp. 368–378 July (2012)Google Scholar
  15. 15.
    Nayar, S. K.: Shape from focus system. In: Computer vision and pattern recognition proceedings CVPR ’92, pp. 302–308 (1992)Google Scholar
  16. 16.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Int. J. Comput. Vis. 1(4), 321–331 (1988)CrossRefGoogle Scholar
  17. 17.
    Osher, S., Sethian, J.A.: Fronts propagating with curvature-dependent speed: algorithms based on Hamilton–Jacobi formulations. J. Comput. Phys. 79, 12–49 (November 1988)Google Scholar
  18. 18.
    Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. Int. J. Comput. Vis. 22(1), 61–79 (1997)MATHCrossRefGoogle Scholar
  19. 19.
    Chan, T., Vese, A.: Active contours without edges. IEEE Trans. Image Process. 10, 266–277 (February 2001)Google Scholar
  20. 20.
    Cremers, D., Rousson, M., Deriche, R.: A review of statistical approaches to level set segmentation: integrating color, texture, motion and shape. Int. J. Comput. Vis. 72(2), 195–215 (2006)CrossRefGoogle Scholar
  21. 21.
    Xu, C., Prince, J.L.: Gradient vector flow: a new external force for snakes. In: IEEE Proceedings, Conference on Computer Vision and, Pattern Recognition, pp. 66–71 (1997)Google Scholar
  22. 22.
    Kiser, C., Musial, C., Sen, P.: Accelerating active contour algorithms with the gradient diffusion field. In: 19th International Conference on Pattern Recognition, pp. 1–4 (2008)Google Scholar
  23. 23.
    Chan, T., Zhu, W.: Level set based shape prior segmentation. In: IEEE Computer Society Conference on Computer Vision and, Pattern Recognition, pp. 1164–1170 (2005)Google Scholar
  24. 24.
    Zhu, G.P., Zeng, Q.S., Wang, C.H.: Robust shape based active contour for circle detection. Imaging Sci. J. 56(3), 175–178 (2008)CrossRefGoogle Scholar
  25. 25.
    Cohen, L.: On active contour models and balloons. CVGIP Graph. Models Image Process. 53, 211–218 (1991)MATHCrossRefGoogle Scholar
  26. 26.
    Maier, A., Niederbrucker, G., Uhl, A.: Measuring image sharpness for a computer vision-based Vickers hardness measurement system. In: Pinoli, J.-C., Debayle, J., Gavet, Y., Cruy, F., Lambert, C. (eds.) 10th International Conference on Quality Control for Artificial Vision (QCAV’11), Proceedings of SPIE, 80000N–1 - 80000N–10, SPIE, St. Etienne, France June (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of Computer SciencesUniversity of SalzburgSalzburgAustria

Personalised recommendations