Advertisement

Shape Representation and Classification Using Boundary Radius Function

  • Hamidreza Zaboli
  • Mohammad Rahmati
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4844)

Abstract

In this paper, a new method for the problem of shape representation and classification is proposed. In this method, we define a radius function on the contour of the shape which captures for each point of the boundary, attributes of its related internal part of the shape. We call these attributes as “depth” of the point. Depths of boundary points generate a descriptor sequence which represents the shape. Matching of sequences is performed using dynamic programming method and a distance measure is acquired. At last, different classes of shapes are classified using a hierarchical clustering method and the distance measure.

The proposed method can analyze features of each part of the shape locally which this leads to the ability of part analysis and insensitivity to local deformations such as articulation, occlusion and missing parts. We show high efficiency of the proposed method by evaluating it for shape matching and classification of standard shape datasets.

Keywords

Computer vision shape matching shape classification boundary radius function 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Zhang, D., Lu, G.: Review of shape representation and description techniques. Pattern Regocnition 37, 1–19 (2004)zbMATHCrossRefGoogle Scholar
  2. 2.
    Blum, H.: A Transformation for extracting new descriptors of Shape. In: Whaten-Dunn, W. (ed.) Models for the perception of Speetch and Visual Forms, pp. 362–380. MIT Press, Cambridge (1967)Google Scholar
  3. 3.
    Siddiqi, K., Shokoufandehs, A., Dickinsons, S.J., Zucker, S.W.: Shock Graphs and Shape Matching. International Journal of Computer Vision 35(1), 13–32 (1999)CrossRefGoogle Scholar
  4. 4.
    Sebastian, T.B., Klein, P.N., Kimia, B.B.: Recognition of Shapes by Editing Shock Graphs. In: ICCV 2001, pp. 755–762 (2001)Google Scholar
  5. 5.
    Bernier, T., Landry, J.-A.: A New Method for Representing and Matching Shapes of Natural Objects. Pattern Recognition 36, 1711–1723 (2003)CrossRefGoogle Scholar
  6. 6.
    Kang, S.K., Ahmad, M.B., Chun, J.H., Kim, P.K., Park, J.A.: Modified Radius-Vector Function for Shape Contour Description. In: Laganà, A., Gavrilova, M., Kumar, V., Mun, Y., Tan, C.J.K., Gervasi, O. (eds.) ICCSA 2004. LNCS, vol. 3046, pp. 940–947. Springer, Heidelberg (2004)Google Scholar
  7. 7.
    Belongie, S., Malik, J., Puzicha, J.: Shape Matching and Object Recognition using Shape Contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(24), 509–522 (2002)CrossRefGoogle Scholar
  8. 8.
    Thayananthan, A., Stenger, B., Torr, P.H.S., Cipolla, R.: Shape Context and Chamfer Matching in Cluttered Scenes. IEEE CVPR 1, 127–133 (2003)Google Scholar
  9. 9.
    Gorelick, L., Galun, M., Sharon, E., Basri, R., Brandt, A.: Shape Representation and Classification Using the Poisson Equation. IEEE Transaction on Pattern Recognition and Machine Intelligence 28(12), 1991–2005 (2005)CrossRefGoogle Scholar
  10. 10.
    Navarro, G.: A guided tour to approximate string matching. ACM Computing Surveys (CSUR) 33(1), 31–88 (2000)CrossRefGoogle Scholar
  11. 11.
    Kimia Image Database (May 2007), Available at http://www.lems.brown.edu/~dmc/main.html
  12. 12.

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Hamidreza Zaboli
    • 1
  • Mohammad Rahmati
    • 1
  1. 1.Department of Computer Engineering, Amirkabir University of Technology, Tehran, Email: zaboli@graduate.orgIran

Personalised recommendations