EXTRACTION OF REGION BOUNDARY PATTERNS WITH ACTIVE CONTOURS

Mohamed Ben Salah, Amar Mitiche

Abstract

In this study we address the problem of recovering region boundary patterns consistent with a given pattern. A level set method formulated in the variational framework evolves an active contour towards regions of interest boundaries while omitting the others. The curve evolution results from the minimization of a functional which measures the similarity between the distribution of an image-based geometric feature on the curve and a model distribution. The corresponding curve evolution equation can be viewed as a geodesic active contour flow having a variable stopping function. This affords a global representation of the objects boundaries which can effectively drive active curve segmentation in a variety of otherwise adverse conditions. We ran several experiments supported by quantitative performance evaluations using various examples of segmentation and tracking.

References

  1. Ben Ayed, I., Li, S., and Ross, I. (2009). A statistical overlap prior for variational image segmentation. International Journal of Computer Vision, 85(1):115-132.
  2. Ben Ayed, I., Mitiche, A., Salah, M. B., and Li, S. (2010). Finding image distributions on active curves. In CVPR, pages 3225-3232.
  3. Berg, A., Berg, T., and Malik, J. (2005). Shape matching and object recognition using low distortion correspondance. In CVPR.
  4. Boykov, Y. and Funka Lea, G. (2006). Graph cuts and efficient N-D image segmentation. International Journal of Computer Vision, 70(2):109-131.
  5. Caselles, V., Kimmel, R., and Sapiro, G. (1997). Geodesic active contours. International Journal of Computer Vision, 22(1):61-79.
  6. Chan, T. and Vese, L. (2001). Active contours without edges. IEEE Transactions on Image Processing, 10(2):266-277.
  7. Chan, T. and Zhu, W. (2005). Level set based shape prior segmentation. In Computer Vision and Pattern Recognition, volume 2, pages 1164-1170.
  8. Cremers, D., Osher, S., and Soatto, S. (2006). Kernel density estimation and intrinsic alignment for shape priors in level set segmentation. International Journal of Computer Vision, 69(3):335-351.
  9. Cremers, D., Rousson, M., and Deriche, R. (2007). A review of statistical approaches to level set segmentation: Integrating color, texture, motion and shape. International Journal of Computer Vision, 72(2):195- 215.
  10. Cremers, D., Sochen, N., and Schnorr, C. (2003). Towards recognition-based variational segmentation using shape priors and dynamic labeling. In International Conference on Scale Space Theories in Computer Vision, volume 2695, pages 388-400.
  11. Do Carmo, M. P. (1976). Differential Geometry of Curves and Surfaces. Prentice Hall.
  12. Ferrari, V., Jurie, F., and Schmid, C. (2009). From images to shape models for object detection. International Journal of Computer Vision.
  13. Ferrari, V., Tuytelaars, T., and Gool, L. V. (2006). Object detection by contour segment networks. In European Conference on Computer Vision (ECCV),.
  14. Foulonneau, A., Charbonnier, P., and Heitz, F. (2006). Affine-invariant geometric shape priors for regionbased active contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(8):1352-1357.
  15. Foulonneau, A., Charbonnier, P., and Heitz, F. (2009). Multi-reference shape priors for active contours. International Journal of Computer Vision, 81(1):68-81.
  16. Freedman, D. and Zhang, T. (2004). Active contours for tracking distributions. IEEE Transactions on Image Processing, 13(4):518-526.
  17. Holtzman-Gazit, M., Kimmel, R., Peled, N., and Goldsher, D. (2006). Segmentation of thin structures in volumetric medical images. IEEE Transacions on Image Processing, 15(2):354-363.
  18. Kass, M., Witkin, A. P., and Terzopoulos, D. (1988). Snakes: Active contour models. International Journal of Computer Vision, 1(4):321-331.
  19. Kichenassamy, S., Kumar, A., Olver, P. J., Tannenbaum, A., and Yezzi, A. J. (1995). Gradient flows and geometric active contour models. In ICCV, pages 810-815.
  20. Lecellier, F., Jehan-Besson, S., Fadili, J., Aubert, G., and Revenu, M. (2009). Optimization of divergences within the exponential family for image segmentation. In SSVM, pages 137-149.
  21. Leventon, M. E., Grimson, W. E., and Faugeras, O. (2000). Statistical shape influence in geodesic active contours. In Conference on Computer Vision and Pattern Recognition, volume 1, pages 316-323.
  22. Li, C., Xu, C., Gui, C., and Fox, M. D. (2005). Level set evolution without re-initialization: A new variational formulation. In Computer Vision and Pattern Recognition.
  23. Mansouri, A. and Mitiche, A. (2002). Region tracking via local statistics and level set pdes. In IEEE International Conference on Image Processing, volume III, pages 605-608, Rochester, NY, USA.
  24. Mansouri, A., Mitiche, A., and Vazquez, C. (2006). Multiregion competition: A level set extension of region competition to multiple region partioning. Computer Vision and Image Understanding, 101(3):137-150.
  25. Martin, D., Fowlkes, C., and Malik, J. (2004). Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(5):530-549.
  26. Michailovich, O. V., Rathi, Y., and Tannenbaum, A. (2007). Image segmentation using active contours driven by the Bhattacharyya gradient flow. IEEE Transactions on Image Processing, 16(11):2787-2801.
  27. Mitiche, A. and Ayed, I. B. (2010). Variational and Level Set Methods in Image Segmentation. Springer, 1st edition.
  28. Mortensen, F. N. (2008). Progress in Autonomous Robot Research. Nova Science Publishers.
  29. Myronenko, A. and Song, X. B. (2009). Global active contour-based image segmentation via probability alignment. In CVPR, pages 2798-2804.
  30. Paragios, N. and Deriche, R. (2002). Geodesic active regions and level set methods for supervised texture segmentation. International Journal of Computer Vision, 46(3):223-247.
  31. Paragios, N., Mellina-Gottardo, O., and Ramesh, V. (2004). Gradient vector flow fast geometric active contours. IEEE Transactions on Pattern Analalysis and Machine Intelligence, 26(3):402-407.
  32. Paragios, N., Rousson, M., and Ramesh, V. (2002). Matching distance functions: A shape to area variational approach for global to local registration. In European Conference in Computer Vision (ECCV), pages 775- 790.
  33. Rousson, M. and Cremers, D. (2005). Efficient kernel density estimation of shape and intensity priors for level set segmentation. In MICCAI, pages 757-764.
  34. Salah, M. B., Mitiche, A., and Ayed, I. B. (2010). Effective level set image segmentation with a kernel induced data term. IEEE Transactions on Image Processing, 19(1):220-232.
  35. Samson, C., Blanc-Feraud, L., Aubert, G., and Zerubia, J. (2000). A level set model for image classification. International Journal of Computer Vision, 40(3):187- 197.
  36. Sethian, J. A. (1999). Level Set Methods and Fast Marching Methods. Cambridge University Press.
  37. Vazquez, C., Mitiche, A., and Laganiere, R. (2006). Joint segmentation and parametric estimation of image motion by curve evolution and level sets. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(5):782-793.
  38. Zhu, S. C. (2003). Statistical modeling and conceptualization of visual patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(6):691-712.
  39. Zhu, S. C. and Yuille, A. (1996). Region competition: Unifying snakes, region growing, and Bayes/MDL for multiband image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 118(9):884-900.
Download


Paper Citation


in Harvard Style

Ben Salah M. and Mitiche A. (2012). EXTRACTION OF REGION BOUNDARY PATTERNS WITH ACTIVE CONTOURS . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012) ISBN 978-989-8565-03-7, pages 240-248. DOI: 10.5220/0003826102400248


in Bibtex Style

@conference{visapp12,
author={Mohamed Ben Salah and Amar Mitiche},
title={EXTRACTION OF REGION BOUNDARY PATTERNS WITH ACTIVE CONTOURS},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012)},
year={2012},
pages={240-248},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003826102400248},
isbn={978-989-8565-03-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012)
TI - EXTRACTION OF REGION BOUNDARY PATTERNS WITH ACTIVE CONTOURS
SN - 978-989-8565-03-7
AU - Ben Salah M.
AU - Mitiche A.
PY - 2012
SP - 240
EP - 248
DO - 10.5220/0003826102400248