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Shape Prior Embedded Geodesic Distance Transform for Image Segmentation

  • Junqiu Wang
  • Yasushi Yagi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6469)

Abstract

Image segmentation is able to provides elements for enhancing a physical real-world environment. Although many existing segmentation methods have achieved impressive performances, they face problems where multiple similar objects are in close proximity to one another. We improve geodesic distance transform and define a symmetric morphology filter for segmentation. We embed shape prior knowledge into this geodesic distance transform filter. The proposed geodesic distance transform filter considers three factors simultaneously: the geometric distance, weighted gradients, and the distance to the boundary of the shape priors. As a result, it provides segmentation in line with the real shape of a particular kind of object. Positive results are demonstrated for several images and video sequences.

Keywords

Image Segmentation Augmented Reality Segmentation Result Geodesic Distance Image Gradient 
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.

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References

  1. 1.
    Levin, A., Lischinski, D., Weiss, Y.: Colorization using optimization. ACM Transactions on Graphics 8, 11–19 (2004)Google Scholar
  2. 2.
    Protiere, A., Sapiro, G.: Interactive image segmentation via adaptive weighted distances. IEEE Trans. on Image Processing 16(4), 1046–1057 (2007)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Bai, X., Sapiro, G.: A geodesic framework for fast interactive image and video segmentation and matting. In: Proc. of ICCV, pp. 1–8 (2007)Google Scholar
  4. 4.
    Criminisi, A., Sharp, T., Blake, A.: GeoS: Geodesic image segmentation. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 99–112. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  5. 5.
    Wang, J., Yagi, Y.: Integrating color and shape-texture features for adaptive real-time tracking. IEEE Trans. on Image Processing 17(2), 235–240 (2008)CrossRefGoogle Scholar
  6. 6.
    Wang, J., Yagi, Y.: Integrating shape and color features for adaptive real-time object tracking. In: Proc. of Conf. on Robotics and Biomimetrics, pp. 1–6 (2006)Google Scholar
  7. 7.
    Cremers, D., Kohlberger, T., Schnorr, C.: Shape statistics in kernel space for variational image segmentation. Pattern Recognition 36, 1929–1943 (2003)CrossRefzbMATHGoogle Scholar
  8. 8.
    Boykov, Y., Jolly, M.P.: Interactive graph cuts for optimal boundary and region segmentation of objects in n-d images. In: Proc. of ICCV, pp. 105–112 (2001)Google Scholar
  9. 9.
    Freedman, D., Zhang, T.: Interactive graph cut based segmentation with shape priors. In: Proc. of CVPR, pp. 755–762 (2004)Google Scholar
  10. 10.
    Bray, M., Kohli, P., Torr, P.: poseCut: Simultaneous segmentation and 3D pose estimation of humans using dynamic graph-cuts. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 642–655. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  11. 11.
    Wang, J., Makihara, Y., Yagi, Y.: Human tracking and segmentation supported by silhouette-based gait recognition. In: Proc. of IEEE Int. Conf. on Robotics and Automation (2008)Google Scholar
  12. 12.
    Besbes, A., Komodakis, N., Langs, G., Paragios, N.: Shape priors and discrete mrfs for knowledge-based segmentation. In: Proc. CVPR, pp. 1295–1302 (2009)Google Scholar
  13. 13.
    Wang, J., Yagi, Y., Makihara, Y.: People tracking and segmentation using efficient shape sequences matching. In: Zha, H., Taniguchi, R.-i., Maybank, S. (eds.) ACCV 2009. LNCS, vol. 5995, pp. 204–213. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  14. 14.
    Leventon, M., Grimson, W., Faugeras, O.: Statistical shape influence in geodesic active contours. In: Proc. CVPR, pp. 316–323 (2000)Google Scholar
  15. 15.
    Chen, Y., Thiruvenkadam, S., Tagare, H., Huang, F., Wilson, D., Geiser, E.: On the incorporation of shape priors into geometric active contours. In: Proc. of IEEE Workshop on Variational and Level Set Methods, pp. 145–152 (2001)Google Scholar
  16. 16.
    Nguyen, H., Ji, Q.: Improved watershed segmentation using water diffusion and local shape priors. In: Proc. CVPR, pp. 985–992 (2006)Google Scholar
  17. 17.
    Teboul, O., Simon, L., Koutsourakis, P., Paragios, N.: Segmentation of building facades using procedural shape priors. In: Proc. CVPR (2010)Google Scholar
  18. 18.
    Mumford, D., Shah, J.: Optimal approximations by piecewise smooth functions and associated variational problems. Comm. Pure Appl. Math. 42(5), 577–685 (1989)MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Cremers, D., Tischhäuser, F., Weickert, J., Schnörr, C.: Diffusion snakes: Introducing statistical shape knowledge into the mumford-shah functional. International Journal of Computer Vision 50(3), 295–313 (2002)CrossRefzbMATHGoogle Scholar
  20. 20.
    Trobin, W., Pock, T., Cremers, D., Bischof, H.: An unbiased second-order prior for high-accuracy motion estimation. In: Rigoll, G. (ed.) DAGM 2008. LNCS, vol. 5096, pp. 396–405. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  21. 21.
    Werman, M., Weinshall, D.: Similarity and affine invariant distances between 2d point sets. IEEE Trans. on Patt. Anal. and Mach. Intell. 17(8), 810–814 (1995)CrossRefGoogle Scholar
  22. 22.
    Ho, J., Peter, A., Ranganranjan, A., Yang, M.H.: An algebraic approach to affine registration of point sets. In: Proc. of ICCV, pp. 1335–1340 (2009)Google Scholar
  23. 23.
    Serra, J.: Image analysis and mathematical morphology. Academic Press, London (1982)zbMATHGoogle Scholar
  24. 24.
    Rother, C., Kolmogorov, V., Blake, A.: Grabcut: interactive foreground extraction using iterated graph cuts. ACM Trans. on Graphics 23(3), 309–314 (2004)CrossRefGoogle Scholar
  25. 25.
    Toyama, K., Blake, A.: Probabilistic tracking with exemplars in a metric space. International Journal of Computer Vision 48(1), 9–19 (2001)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Junqiu Wang
    • 1
  • Yasushi Yagi
    • 1
  1. 1.The Institute of Scientific and Industrial ResearchOsaka UniversityIbarakiJapan

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