A multiphase dynamic labeling model for variational recognition-driven image segmentation

International Journal of Computer Vision

Volumn 66, Issue 1, 2006, Pages 67-81

  Cremers, Daniel ^a   Sochen, Nir ^b   Schnörr, Christoph ^c  

^a UNIVERSITY OF BONN   (Germany)

^b TEL AVIV UNIVERSITY   (Israel)

^c UNIVERSITY OF MANNHEIM   (Germany)

Author keywords

Dynamic labeling; Image segmentation; Level set methods; Recognition modeling; Shape priors; Variational methods

Indexed keywords

FUNCTIONS; IMAGE ANALYSIS; INTEGRATION; OPTIMIZATION; VECTORS;

DYNAMIC LABELING; LEVEL SET METHODS; RECOGNITION MODELING; SHAPE PRIORS; VARIATIONAL METHODS;

IMAGE SEGMENTATION;

EID: 29344442389     PISSN: 09205691     EISSN: 15731405     Source Type: Journal    
DOI: 10.1007/s11263-005-3676-z     Document Type: Article

References (38)

1. Brox, T. and Weickert, J. 2004. A TV flow based local scale measure for texture discrimination. In T. Pajdla and V. Hlavac (Eds.), European Conf. on Computer Vision, vol. 3022 of LNCS, Prague, Springer, pp. 578-590.
2. Caselles, V., Catté, F., Coll T., and Dibos, F., 1993. A geometric model for active contours in image processing. Numer. Math., 66:1-31.
3. Caselles, V., Kimmel, R., and Sapiro, G. 1995. Geodesic active contours. In Proc. IEEE Intl. Conf. on Comp. Vis., Boston, USA, pp. 694-699.
4. Chan, T.F. and Vese, L.A. 2001. Active contours without edges. IEEE Trans. Image Processing, 10(2):266-277.
5. Chan, T. and Zhu, W. 2003. Level set based shape prior segmentation. Technical Report 03-66, Computational Applied Mathematics, UCLA, Los Angeles.
6. Charpiat, G., Faugeras, O., and Keriven, R. 2005. Approximations of shape metrics and application to shape warping and empirical shape statistics. Journal of Foundations of Computational Mathematics, 5(1):1-58.
7. Chen, Y., Tagare, H., Thiruvenkadam, S., Huang, F., Wilson, D., Gopinath, K.S., Briggs, R.W., and Geiser, E. 2002. Using shape priors in geometric active contours in a variational framework. Int. J. of Computer Vision, 50(3):315-328.
8. Cootes, T., Taylor, C., Cooper, D.H., and Graham, J. 1992. Training models of shape from sets of examples. In Brit. Mach. Vis. Conf., pp. 9-18.
9. Cootes, T.F. and Taylor, C.J. 1999. A mixture model for representing shape variation. Image and Vision Computing, 17(8):567-574.
10. Cremers, D., Kohlberger, T., and Schnörr, C. 2003. Shape statistics in kernel space for variational image segmentation. Pattern Recognition, 36(9):1929-1943.
11. Cremers, D., Osher, S.J., and Soatto, S. 2004. Kernel density estimation and intrinsic alignment for knowledge-driven segmentation: Teaching level sets to walk. In Pattern Recognition, vol. 3175 of LNCS, Springer, pp. 36-44.
12. Cremers, D., Osher, S.J., and Soatto, S. 2004b. (To appear).
13. Cremers, D. and Soatto, S. 2003. A pseudo-distance for shape priors in level set segmentation. In N. Paragios (Ed.), IEEE 2nd Int. Workshop on Variational, Geometric and Level Set Methods, Nice, pp. 169-176.
14. Cremers, D. and Soatto, S. 2005. Motion competition: A variational framework for piecewise parametric motion segmentation. Int. J. of Computer Vision, 62(3):249-265.
15. Cremers, D., Sochen, N., and Schnörr, C. 2003. Towards recognition-based variational segmentation using shape priors and dynamic labeling. In L. Griffith (Ed.), Int. Conf. on Scale Space Theories in Computer Vision, volume 2695 of LNCS, Isle of Skye, Springer, pp. 388-400.
16. Cremers, D., Sochen, N., and Schnörr, C. 2004. Multiphase dynamic labeling for variational recognition-driven image segmentation. In T. Pajdla and V. Hlavac (Eds.), European Conf. on Computer Vision, vol. 3024 of LNCS, Springer, pp. 74-86.
17. Cremers, D., Tischhäuser, F., Weickert, J., and Schnörr, C. 2002. Diffusion snakes: Introducing statistical shape knowledge into the Mumford-Shah functional. Int. J. of Computer Vision, 50(3):295-313.
18. Dervieux, A. and Thomasset, F. 1979. A finite element method tor the simulation of Raleigh-Taylor instability. Springer Lect. Notes in Math., 771:145-158.
19. Dervieux, A. and Thomasset, F. 1981. Multifluid incompressible flows by a finite element method. Lecture Notes in Physics, 11:158-163.
20. Heiler, M. and Schnörr, C. 2005. Natural image statistics for natural image segmentation. Int. J. of Computer Vision, 63(1):5-19.
21. Kichenassamy, S., Kumar, A., Olver, P.J., Tannenbaum, A., and Yezzi, AJ. 1995. Gradient flows and geometric active contour models. In Proc. IEEE Intl. Conf. on Comp. Vis., Boston, USA pp. 810-815.
22. Leventon, M., Crimson, W., and Faugeras, O. 2000. Statistical shape influence in geodesic active contours. In CVPR, Hilton Head Island, SC, vol. 1, pp. 316-323.
23. Malladi, R., Sethian, J.A., and Vemuri, B.C. 1995. Shape modeling with front propagation: A level set approach. IEEE PAMI, 17(2): 158-175.
24. Mumford, D. and Shah, J. 1989. Optimal approximations by piecewise smooth functions and associated variational problems. Comm. Pure Appl. Math., 42:577-685.
25. Osher, S.J. and Sethian, J.A. 1988. Fronts propagation with curvature dependent speed: Algorithms based on Hamilton - Jacobi formulations. J. of Comp. Phys., 79:12-49.
26. Paragios, N. and Deriche, R. 2002. Geodesic active regions and level set methods for supervised texture segmentation. Int. J. of Computer Vision, 46(3): 223-247.
27. Riklin-Raviv, T., Kiryati, N., and Sochen, N. 2004. Unlevel sets: Geometry and prior-based segmentation. In T. Pajdla and V. Hlavac (Eds.), European Conf. on Computer Vision, volume 3024 of LNCS, Prague, Springer, pp. 50-61.
28. Rousson, M. and Cremers, D. 2005. Efficient kernel density estimation of shape and intensity priors for level set segmentation. In MICCAI (To appear).
29. Rousson, M. and Deriche, R.A. 2002. variational framework for active and adaptive segmentation of vector valued images. In Proc. IEEE Workshop on Motion and Video Computing, Orlando, Florida, pp. 56-62.
30. Rousson, M. and Paragios, N. 2002. Shape priors for level set representations. In A. Heyden et al. (Eds.) Proc. of the Europ. Conf. on Comp. Vis., volume 2351 of LNCS, Springer, Copenhagen, Berlin, pp. 78-92.
31. Rousson, M., Paragios, N., and Deriche, R. 2004. Implicit active shape models for 3d segmentation in MRI imaging. In MICCAI, pp. 209-216.
32. Rudin, L.I., Osher, S., and Fatemi, E. 1992. Nonlinear total variation based noise removal algorithms. Physica D, 60:259-268.
33. Sagiv, C., Sochen, N., and Zeevi, Y. 2001. Geodesic active contours applied to texture feature space. In M. Kerckhove (Ed.) Intl. Conf. on Scale Space Theories for Comp. Vision, vol. 2106 of LNCS, Springer, pp. 344-352.
34. Sussman, M., Smereka P., and Osher, S.J. 1994. A level set approach for computing solutions to incompressible twophase flow. J. of Comp. Phys., 94:146-159.
35. Tsai, A., Yezzi, A., Wells, W., Tempany, C., Tucker, D., Fan, A., Crimson, E., and Willsky, A. 2001. Model-based curve evolution technique for image segmentation. In Comp. Vision Patt. Recog., Kauai, Hawaii, pp. 463-468.
36. Tsai, A., Yezzi, A.J., and Willsky, A.S. 2001. Curve evolution implementation of the Mumford-Shah functional for image segmentation, denoising, interpolation, and magnification. IEEE Trans. on Image Processing, 10(8): 1169-1186.
37. Vese, L.A. and Chan, T.F. 2002. A multiphase level set framework for image processing using the Mumford-Shah functional. Int. J. of Computer Vision, 50(3):271-293.
38. Zhu, S.C. and Yuille, A. 1996. Region competition: Unifying snakes, region growing, and Bayes/MDL for multiband image segmentation. IEEE PAMI, 18(9):884-900.