Robust Boundary Delineation Using Random-Phase-Shift Active Contours

  • Astrit Rexhepi
  • Farzin Mokhtarian
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4522)


When an active contour is applied to a noisy image, the contour is sometimes attracted to a local energy minimum, since the noise gives rise to high rates of change of the image gray levels. In this paper we will describe a novel method of overcoming this problem by using a sparse set of points to represent the active contour C and randomly varying the positions of these points.


Active Contour Object Boundary External Energy Active Contour Model Image Gray Level 
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.


  1. 1.
    Kass, M., Witkin, A.P., Terzopoulos, D.: Snakes: Active Contour Models. International Journal of Computer Vision 1, 321–331 (1988)CrossRefGoogle Scholar
  2. 2.
    Blake, A., Isard, M.: Active Contours. Springer, Heidelberg (1998)Google Scholar
  3. 3.
    Delanges, P., Benois, J., Barba, D.: Active Contours Approach to Object Tracking in Image Sequences with Complex Background. Pattern Recognition Letters 16, 171–178 (1995)CrossRefGoogle Scholar
  4. 4.
    Wang, M., Evans, J., Hassebrook, L., Knapp, C.: A Multistage, Optimal Active Contour Model. IEEE Trans. on Image Processing 5, 1586–1591 (1996)CrossRefGoogle Scholar
  5. 5.
    Wong, Y.Y., Yuen, P.C., Tong, C.S.: Segmented Snake for Contour Detection. Pattern Recognition 31, 1669–1679 (1998)CrossRefGoogle Scholar
  6. 6.
    Chesnaud, C., Refregier, P., Boulet, V.: Statistical Region Snake-Based Segmentation Adapted to Different Physical Noise Models. IEEE Trans. on Pattern Analysis and Machine Intelligence 21, 1145–1157 (1999)CrossRefGoogle Scholar
  7. 7.
    Ray, N., Chanda, B., Das, J.: A Fast and Flexible Multiresolution Snake with a Definite Termination Criterion. Pattern Recognition 34, 1483–1490 (2001)zbMATHCrossRefGoogle Scholar
  8. 8.
    Velasco, F.A., Marroquin, J.L.: Robust Parametric Active Contours: The Sandwich Snakes. Machine Vision and Applications 12, 238–242 (2001)CrossRefGoogle Scholar
  9. 9.
    Cohen, L.D.: On Active Contour Models and Balloons. Computer Vision, Graphics, and Image Processing 53, 211–218 (1991)zbMATHGoogle Scholar
  10. 10.
    Davatzikos, C., Prince, J.L.: Adaptive Active Contour Algorithms for Extracting and Mapping Thick Curves. In: Computer Vision and Pattern Recognition, pp. 524–529 (1993)Google Scholar
  11. 11.
    Xu, G., Segawa, E., Tsuji, S.: Robust Active Contours with Insensitive Parameters. Pattern Recognition 27, 879–884 (1994)CrossRefGoogle Scholar
  12. 12.
    Metaxas, D., Kakadiaris, I.A.: Elastically Adaptive Deformable Models. In: Buxton, B.F., Cipolla, R. (eds.) ECCV 1996. LNCS, vol. 1065, pp. 550–559. Springer, Heidelberg (1996)Google Scholar
  13. 13.
    Xu, C., Prince, J.L.: Gradient Vector Flow: A New External Force for Snakes. In: Conference on Computer Vision and Pattern Recognition, pp. 66–71 (1997)Google Scholar
  14. 14.
    Davatzikos, C., Prince, J.L.: Convexity Analysis of Active Contour Problems. Image and Vision Computing 17, 27–36 (1999)CrossRefGoogle Scholar
  15. 15.
    Peterfreund, N.: The Velocity Snake: Deformable Contour for Tracking in Spatio-Velocity Space. Computer Vision and Image Understanding 73, 346–356 (1999)zbMATHCrossRefGoogle Scholar
  16. 16.
    Neuenschwander, W., Fua, P., Szekely, G., Kubler, O.: Making Snakes Converge from Minimal Initialization. In: International Conference on Pattern Recognition, pp. 613–615 (1994)Google Scholar
  17. 17.
    Amini, A., Tehrani, S., Weymouth, T.E.: Using Dynamic Programming for Minimizing the Energy of Active Contours in the Presence of Hard Constraints. In: International Conference on Computer Vision, pp. 95–99 (1988)Google Scholar
  18. 18.
    Williams, D.J., Shah, M.: A Fast Algorithm for Active Contours. In: International Conference on Computer Vision, pp. 592–598 (1990)Google Scholar
  19. 19.
    Caselles, V., Kimmel, R., Sapiro, G.: Geodesic Active Contours. International Journal on Computer Vision 22, 61–79 (1997)zbMATHCrossRefGoogle Scholar
  20. 20.
    Mokhtarian, F., Mohanna, F.: Fast Active Contour Convergence through Curvature Scale Space Filtering. In: Image and Vision Computing, pp. 157–162 (2001)Google Scholar
  21. 21.
    Rexhepi, A., Rosenfeld, A., Mokhtarian, F.: Extracting Boundaries from Images by Comparing Cooccurrence Matrices. In: Digital Image Computing Techniques and Applications, DICTA (2003)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Astrit Rexhepi
    • 1
  • Farzin Mokhtarian
    • 1
  1. 1.Centre for Vision, Speech, and Signal Processing, School of Electronics and Physical Sciences, University of Surrey, Guildford GU2 7XHUnited Kingdom

Personalised recommendations