Advertisement

Water Flow Based Complex Feature Extraction

  • Xin U Liu
  • Mark S Nixon
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4179)

Abstract

A new general framework for shape extraction is presented, based on the paradigm of water flow. The mechanism embodies the fluidity of water and hence can detect complex shapes. A new snake-like force functional combining edge-based and region-based forces produces capability for both range and accuracy. Properties analogous to surface tension and adhesion are also applied so that the smoothness of the evolving contour and the ability to flow into narrow branches can be controlled. The method has been assessed on synthetic and natural images, and shows encouraging detection performance and ability to handle noise, consistent with properties included in its formulation.

Keywords

Active Contour Topological Change Image Force Impulsive Noise Gradient Vector Flow 
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.
    Cohen, L.D.: On active contour models and balloons. CVGIP, Image Understanding 53(2), 211–218 (1991)MATHCrossRefGoogle Scholar
  2. 2.
    Cohen, L.D., Cohen, I.: Finite element methods for active models and balloons for 2-D and 3-D images. IEEE Trans. PAMI 15, 1131–1147 (1993)Google Scholar
  3. 3.
    Xu, C., Prince, J.L.: Snakes, shapes, and gradient vector flow. IEEE Trans. Image Processing 7(3), 359–369 (1998)MATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Figueiredo, M., Leitao, J.: Bayesian estimation of ventricular contours in angiographic images. IEEE Trans. Medical Imaging 11, 416–429 (1992)CrossRefGoogle Scholar
  5. 5.
    Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. Image Processing 10, 266–276 (2001)MATHCrossRefGoogle Scholar
  6. 6.
    McInerney, T., Terzopoulos, D.: Topologically adaptive snakes. In: Int’l. Conf. Computer Vision 1995, pp. 840–845 (1995)Google Scholar
  7. 7.
    Casselles, V., Kimmel, R., Spiro, G.: Geodesic active contours. International Journal of Computer Vision 22(1), 61–79 (1997)CrossRefGoogle Scholar
  8. 8.
    Malladi, R., et al.: Shape modeling with front propagation: A level set approach. IEEE Trans. PAMI 17, 158–174Google Scholar
  9. 9.
    Adams, R., Bischof, L.: Seeded region growing. IEEE Trans. PAMI 16(6), 641–647Google Scholar
  10. 10.
    Zhu, S.C., Yuille, A.: Region competition: unifying snakes, region growing, and Bayes/MDL for multi-band image segmentation. IEEE Trans. PAMI 18(9), 884–900Google Scholar
  11. 11.
    Kiran, V., Bora, P.K.: Watersnake: integrating the watershed and the active contour algorithms. In: TENCON 2003. Conference on Convergent Technologies for Asia-Pacific Region, October 2003, vol. 2, pp. 868–871 (2003)Google Scholar
  12. 12.
    Bleau, Leon, L.J.: Watershed-base segmentation and region merging. Computer Vision and Image Understanding 77, 317–370 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xin U Liu
    • 1
  • Mark S Nixon
    • 1
  1. 1.ISIS group, School of ECSUniversity of SouthamptonSouthamptonU.K.

Personalised recommendations