Skip to main content
SpringerLink
Log in

Using surface variability characteristics for segmentation of deformable 3D objects with application to piecewise statistical deformable model

  • Original Article
  • Published:
The Visual Computer Aims and scope Submit manuscript

Abstract

To cope with the small sample size problem in the construction of Statistical Deformable Models (SDM), this paper proposes two novel measures that quantify the similarity of the variability characteristics among deforming 3D meshes. These measures are used as the basis of our proposed technique for partitioning a 3D mesh for the construction of piecewise SDM in a divide-and-conquer strategy. Specifically, the surface variability information is extracted by performing a global principal component analysis on the set of sample meshes. An iterative face clustering algorithm is developed for segmenting a mesh that favors grouping triangular faces having similar variability characteristics into a same mesh component. We apply the proposed mesh segmentation algorithm to the construction of piecewise SDM and evaluate the representational ability of the resulting piecewise SDM through the reconstruction of unseen meshes. Experimental results show that our approach outperforms several state-of-the-art methods in terms of the representational ability of the resulting piecewise SDM as evaluated by the reconstruction accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models—their training and application. Comput. Vis. Image Underst. 61(1), 38–59 (1995)

    Article  Google Scholar 

  2. Lanitis, A., Taylor, C.J., Cootes, T.F.: Automatic interpretation and coding of face images using flexible models. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 743–756 (1997)

    Article  Google Scholar 

  3. Kervrann, C., Heitz, F.: Statistical deformable model-based segmentation of image motion. IEEE Trans. Image Process. 8(4), 583–588 (1999)

    Article  Google Scholar 

  4. Zhao, Z.E., Aylward, S.R., Teoh, E.K.: A novel 3d partitioned active shape model for segmentation of brain MR images. In: Proc. MICCAI 2005. LNCS, vol. 3749, pp. 221–228 (2005)

    Google Scholar 

  5. Okada, T., Shimada, R., Sato, Y., Hori, M., Yokota, K., Nakamoto, M., Chen, Y.W., Nakamura, H.: Automated segmentation of the liver from 3d CT images using probabilistic atlas and multi-level statistical shape model. In: Proc. MICCAI 2007. LNCS, vol. 4791, pp. 86–93 (2007)

    Google Scholar 

  6. Feng, J., Du, P., Ip, H.H.S.: Statistical piecewise assembled model (SPAM) for the representation of highly deformable medical organs. In: Proc. International Workshop on Medical Imaging and Augmented Reality. LNCS, vol. 5128, pp. 168–176 (2008)

    Chapter  Google Scholar 

  7. Karni, Z., Gotsman, C.: Spectral compression of mesh geometry. In: SIGGRAPH ’00: Proceedings of the 27th Annual Conference on Computer Graphics and Interactive Techniques, pp. 279–286. ACM/Addison-Wesley, New York (2000)

    Chapter  Google Scholar 

  8. Katz, S., Leifman, G., Tal, A.: Mesh segmentation using feature point and core extraction. Vis. Comput. 21, 649–658 (2005)

    Article  Google Scholar 

  9. Garland, M., Willmott, A., Heckbert, P.S.: Hierarchical face clustering on polygonal surfaces. In: SI3D ’01: Proceedings of the 2001 Symposium on Interactive 3D Graphics, pp. 49–58. ACM Press, New York (2001)

    Chapter  Google Scholar 

  10. Julius, D., Kraevoy, V., Sheffer, A.: D-charts: Quasi-developable mesh segmentation. In: Computer Graphics Forum, Proc. Eurographics 2005, vol. 24, pp. 581–590 (2005)

    Google Scholar 

  11. Lévy, B., Petitjean, S., Ray, N., Maillot, J.: Least squares conformal maps for automatic texture atlas generation. In: SIGGRAPH ’02: Proceedings of the 29th Annual Conference on Computer Graphics and Interactive Techniques, pp. 362–371. ACM Press, New York (2002)

    Chapter  Google Scholar 

  12. Lien, J.M., Amato, N.M.: Approximate convex decomposition. In: Proc. the 20th Annual Symposium on Computational Geometry, pp. 457–458. ACM, New York (2004)

    Google Scholar 

  13. Shlafman, S., Tal, A., Katz, S.: Metamorphosis of polyhedral surfaces using decomposition. Comput. Graph. Forum 21, 3 (2002)

    Article  Google Scholar 

  14. Katz, S., Tal, A.: Hierarchical mesh decomposition using fuzzy clustering and cuts. ACM Trans. Graph. 22(3), 954–961 (2003)

    Article  Google Scholar 

  15. Zuckerberger, E., Tak, A., Shlafman, S.: Polyhedral surface decomposition with applications. Comput. Graph. 26(5), 733–743 (2002)

    Article  Google Scholar 

  16. Kalvin, A., Taylor, R.: Superfaces: polygonal mesh simplification with bounded error. IEEE Comput. Graph. Appl. 16(3), 64–77 (1996)

    Article  Google Scholar 

  17. Garland, M., Willmott, A., Heckbert, P.: Hierarchical face clustering on polygonal surfaces. In: Proc. ACM Symposium on Interactive 3D Graphics, pp. 49–58 (2001)

    Chapter  Google Scholar 

  18. Attene, M., Falcidieno, B., Spagnuolo, M.: Hierarchical mesh segmentation based on fitting primitives. Vis. Comput. 22, 181–193 (2006)

    Article  Google Scholar 

  19. Cohen-Steiner, D., Alliez, P., Desbrun, M.: Variational shape approximation. ACM Trans. Graph. 23(3), 905–914 (2004)

    Article  Google Scholar 

  20. Liu, R., Zhang, H.: Segmentation of 3D meshes through spectral clustering. In: Proc. 12th Pacific Conference on Computer Graphics and Applications (PG’04), pp. 298–305 (2004)

    Google Scholar 

  21. Attene, M., Katz, S., Mortara, M., Patane, G., Spagnuolo, M.: Mesh segmentation – a comparative study. In: Proc. IEEE International Conference on Shape Modeling and Applications (SMI’06) (2006)

    Google Scholar 

  22. Shamir, A.: A survey on mesh segmentation techniques. Comput. Graph. Forum 27(6), 1539–1556 (2008)

    Article  MATH  Google Scholar 

  23. Shamir, A.: A formulation of boundary mesh segmentation. In: Proc. the 2nd International Symposium on 3D Data Processing, Visualization, and Transmission, pp. 82–89 (2004)

    Chapter  Google Scholar 

  24. Lee, T.Y., Wang, Y.S., Chen, T.G.: Segmenting a deforming mesh into near-rigid components. Vis. Comput. 22, 729–739 (2006)

    Article  Google Scholar 

  25. Theobalt, C., Rössl, C., Aguiar, E., Seidel, H.P.: Animation collage. In: Proc. Eurographics/ACM SIGGRAPH Symposium on Computer Animation, pp. 271–280 (2007)

    Google Scholar 

  26. James, D.L., Twigg, C.D.: Skinning mesh animations. ACM Trans. Graph. 24(3), 399–407 (2005)

    Article  Google Scholar 

  27. Wuhrer, S., Brunton, A.: Segmenting animated objects into near-rigid components. Vis. Comput. 26, 147–155 (2010)

    Article  Google Scholar 

  28. Sattler, M., Sarlette, R., Klein, R.: Simple and efficient compression of animation sequences. In: Proc. Eurographics/ACM SIGGRAPH Symposium on Computer Animation, pp. 209–217 (2005)

    Chapter  Google Scholar 

  29. Lee, T.Y., Lin, P.H., Yan, S.U., Lin, C.H.: Mesh decomposition using motion information from animation sequence. Comput. Animat. Virtual Worlds 16, 519–529 (2005)

    Article  Google Scholar 

  30. Amjoun, R., Sondershaus, R., Straßer, W.: Compression of complex animated meshes. In: Proc. CGI 2006. LNCS, vol. 4035, pp. 606–613 (2006)

    Google Scholar 

  31. Amjoun, R., Straßer, W.: Efficient compression of 3d dynamic mesh sequences. In: Proc. the 15th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (2007)

    Google Scholar 

  32. Feng, J., Ip, H.H.S., Lai, L.Y., Linney, A.: Robust point correspondence matching and similarity measuring for 3D models by relative angle-context distributions. Image Vis. Comput. 26, 761–775 (2008)

    Article  Google Scholar 

  33. Borrel, P., Rappoport, A.: Simple constrained deformations for geometric modelling and interactive design. ACM Trans. Graph. 13, 137–155 (1994)

    Article  MATH  Google Scholar 

  34. Yan, H.B., Hu, S.M., Martin, R.R., Yang, Y.L.: Shape deformation using a skeleton to drive simplex transformations. IEEE Trans. Vis. Comput. Graph. 14(3), 693–706 (2008)

    Article  Google Scholar 

  35. Ruiz-Correa, S., Shapiro, L.G., Melia, M.: A new signature-based method for efficient 3-D object recognition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 769–776 (2001)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, City University of Hong Kong and USTC-CityU Joint Advanced Research Centre, Suzhou, P.R. China

    Peng Du & Horace H. S. Ip

  2. School of Computer Science and Technology, University of Science and Technology of China and USTC-CityU Joint Advanced Research Centre, Suzhou, P.R. China

    Peng Du & Bei Hua

  3. School of Information Technology, Northwest University, Xi’an, P.R. China

    Jun Feng

Authors
  1. Peng Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Horace H. S. Ip
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bei Hua
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jun Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Peng Du.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Du, P., Ip, H.H.S., Hua, B. et al. Using surface variability characteristics for segmentation of deformable 3D objects with application to piecewise statistical deformable model. Vis Comput 28, 493–509 (2012). https://doi.org/10.1007/s00371-011-0646-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00371-011-0646-z

Keywords

Search

Navigation