Retrieval of 3D Polygonal Objects Based on Multiresolution Signatures

  • Roberto Lam
  • J. M. Hans du Buf
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6939)

Abstract

In this paper we present a method for retrieving 3D polygonal objects by using two sets of multiresolution signatures. Both sets are based on the progressive elimination of object’s details by iterative processing of the 3D meshes. The first set, with five parameters, is based on mesh smoothing. This mainly affects an object’s surface. The second set, with three parameters, is based on difference volumes after successive mesh erosions and dilations. Characteristic feature vectors are constructed by combining the features at three mesh resolutions of each object. In addition to being invariant to mesh resolution, the feature vectors are invariant to translation, rotation and size of the objects. The method was tested on a set of 40 complex objects with mesh resolutions different from those used in constructing the feature vectors. By using all eight features, the average ranking rate obtained was 1.075: 37 objects were ranked first and only 3 objects were ranked second. Additional tests were carried out to determine the significance of individual features and all combinations. The same ranking rate of 1.075 can be obtained by using some combinations of only three features.

Keywords

Mathematical Morphology Mesh Resolution Mesh Erosion Polygonal Mesh Ranking Rate 
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.
This is a preview of subscription content, log in to check access

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bustos, B., Keim, D.A., Saupe, D., Schreck, T., Vranic, D.: Feature-based similarity search in 3D object databases. ACM Computing Surveys 37, 345–387 (2005)CrossRefGoogle Scholar
  2. 2.
    Tangelder, J.W., Veltkamp, R.C.: A survey of content based 3D shape retrieval methods. Multimedia Tools Appl. 39, 441–471 (2008)CrossRefGoogle Scholar
  3. 3.
    Saupe, D., Vranic, D.V.: 3D model retrieval with spherical harmonics and moments. In: Radig, B., Florczyk, S. (eds.) DAGM 2001. LNCS, vol. 2191, pp. 392–397. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  4. 4.
    Pang, M.-Y., Dai, W., Wu, G., Zhang, F.: On volume distribution features based 3D model retrieval. In: Pan, Z., Cheok, D.A.D., Haller, M., Lau, R., Saito, H., Liang, R. (eds.) ICAT 2006. LNCS, vol. 4282, pp. 928–937. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  5. 5.
    Sijbers, J., Dyck, D.V.: Efficient algorithm for the computation of 3D fourier descriptors. In: Proc. Int. Symp. on 3D Data Processing Visualization and Transmission, p. 640 (2002)Google Scholar
  6. 6.
    Assfalg, J., Bimbo, A.D., Pala, P.: Content-based retrieval of 3D models through curvature maps: a CBR approach exploiting media conversion. Multimedia Tools and Applications 31, 29–50 (2006)CrossRefGoogle Scholar
  7. 7.
    Matheron, G.: Random sets and integral geometry. John Wiley & Sons, New York (1975)MATHGoogle Scholar
  8. 8.
    Serra, J.: Introduction to mathematical morphology. Comput. Vision, Graphics and Image Processing 35, 283–305 (1986)CrossRefMATHGoogle Scholar
  9. 9.
    Jackway, P.T.: Morphological Scale-Space with Application to Three-Dimensional Object Recognition. PhD thesis, Queensland University of Technology (Australia), Supervisor-Boles, W. W. (1995)Google Scholar
  10. 10.
    Lee, J., Smith, M., Smith, L., Midha, P.: A mathematical morphology approach to image based 3D particle shape analysis. Machine Vision and Applications 16, 282–288 (2005)CrossRefGoogle Scholar
  11. 11.
    AIM@SHAPE (2008), http://www.aimatshape.net
  12. 12.
    Vranic, D.: 3D Model Retrieval. PhD thesis, University of Leipzig (2004)Google Scholar
  13. 13.
    Glendinning, R.H., Herbert, R.A.: Shape classification using smooth principal components. Pattern Recognition Letters 24(12), 2021–2030 (2003)CrossRefGoogle Scholar
  14. 14.
    Lam, R., Loke, R., du Buf, H.: Smoothing and reduction of triangle meshes. In: Proc. 10th Portuguese Computer Graphics Meeting, pp. 97–107 (2001)Google Scholar
  15. 15.
    Lam, R., du Buf, J.M.H.: Invariant categorisation of polygonal objects using multi-resolution signatures. In: Proc. KDIR, pp. 168–173 (2009)Google Scholar
  16. 16.
    Lam, R., Hans du Buf, J.M.: Using mathematical morphology for similarity search of 3D objects. In: Vitrià, J., Sanches, J.M., Hernández, M. (eds.) IbPRIA 2011. LNCS, vol. 6669, pp. 411–419. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  17. 17.
    Campbell, R., Flynn, P.: A survey of free-form object representation and recognition techniques. Computer Vision and Image Understanding 81, 166–210 (2001)CrossRefMATHGoogle Scholar
  18. 18.
    Shih, F.: Object representation and recognition using mathematical morphology model. Journal of Systems Integration 1, 235–256 (1991)CrossRefGoogle Scholar
  19. 19.
    Zaharescu, A., Boyer, E., Horaud, R.: TransforMesh: A topology-adaptive mesh-based approach to surface evolution. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds.) ACCV 2007, Part II. LNCS, vol. 4844, pp. 166–175. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  20. 20.
    Cignoni, P., Corsini, M., Ranzuglia, G.: Meshlab: an open-source 3D mesh processing system. ERCIM News, 45–46 (2008)Google Scholar
  21. 21.
    Veltkamp, R.C., Giezeman, G.J., Bast, H., Baumbach, T., Furuya, T., Giesen, J., Godil, A., Lian, Z., Ohbuchi, R., Saleem, W.: Shrec 2010 track: Large scale retrieval. In: Proc. of the Eurographics/ACM SIGGRAPH Symp. on 3D Object Retrieval, pp. 63–69 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Roberto Lam
    • 1
  • J. M. Hans du Buf
    • 1
  1. 1.Institute for Systems and Robotics (ISR), Vision LaboratoryUniversity of the Algarve (ISE and FCT)FaroPortugal

Personalised recommendations