Advertisement

Edge Strength Functions as Shape Priors in Image Segmentation

  • Erkut Erdem
  • Aykut Erdem
  • Sibel Tari
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3757)

Abstract

Many applications of computer vision requires segmenting out of an object of interest from a given image. Motivated by unlevel-sets formulation of Raviv, Kiryati and Sochen [8] and statistical formulation of Leventon, Grimson and Faugeras [6], we present a new image segmentation method which accounts for prior shape information. Our method depends on Ambrosio-Tortorelli approximation of Mumford-Shah functional. The prior shape is represented by a by-product of this functional, a smooth edge indicator function, known as the “edge strength function”, which provides a distance-like surface for the shape boundary. Our method can handle arbitrary deformations due to shape variability as well as plane Euclidean transformations. The method is also robust with respect to noise and missing parts. Furthermore, this formulation does not require simple closed curves as in a typical level set formulation.

Keywords

Image Segmentation Segmentation Result Active Contour Shape Boundary Simple Closed Curf 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ambrosio, L., Tortorelli, V.: On the approximation of functionals depending on jumps by elliptic functionals via Γ-convergence. Commun. Pure Appl. Math. 43(8), 999–1036 (1990)zbMATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Aslan, C.: Disconnected skeletons for shape recognition. Master’s thesis, Department of Computer Engineering, Middle East Technical University (May 2005)Google Scholar
  3. 3.
    Chan, T., Vese, L.: Active contours without edges. IEEE Trans. Image Processing 10(2), 266–277 (2001)zbMATHCrossRefGoogle Scholar
  4. 4.
    Chen, Y., Tagare, H., Thiruvenkadam, S., Huang, F., Wilson, D., Gopinath, K.S., Briggs, R.W., Geiser, E.A.: Using prior shapes in geometric active contours in a variational framework. Int. J. Comput. Vision 50(3), 315–328 (2002)zbMATHCrossRefGoogle Scholar
  5. 5.
    Cremers, D., Tischhäuser, F., Weickert, J., Schnörr, C.: Diffusion snakes: Introducing statistical shape knowledge into the mumford-shah functional. Int. J. Comput. Vision 50(3), 295–313 (2002)zbMATHCrossRefGoogle Scholar
  6. 6.
    Leventon, M.E., Eric, W., Grimson, L., Faugeras, O.D.: Statistical shape influence in geodesic active contours. In: CVPR, pp. 1316–1323 (2000)Google Scholar
  7. 7.
    Mumford, D., Shah, J.: Optimal approximations by piecewise smooth functions and associated variational problems. Commun. Pure Appl. Math. 42(5), 577–685 (1989)zbMATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Riklin-Raviv, T., Kiryati, N., Sochen, N.A.: Unlevel-sets: Geometry and prior-based segmentation. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3024, pp. 50–61. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  9. 9.
    Rousson, M., Paragios, N.: Shape priors for level set representations. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2351, pp. 78–92. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  10. 10.
    Tari, S., Shah, J., Pien, H.: Extraction of shape skeletons from grayscale images. CVIU 66(2), 133–146 (1997)Google Scholar
  11. 11.
    Tsai, A., Yezzi, A.J., Wells III, W.M., Tempany, C., Tucker, D., Fan, A., Grimson, W.E.L., Willsky, A.S.: A shape-based approach to the segmentation of medical imagery using level sets. IEEE Trans. Med. Imaging 22(2), 137–154 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Erkut Erdem
    • 1
  • Aykut Erdem
    • 1
  • Sibel Tari
    • 1
  1. 1.Department of Computer EngineeringMiddle East Technical UniversityAnkaraTurkey

Personalised recommendations