Efficient segmentation of piecewise smooth images

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4485 LNCS, Issue , 2007, Pages 709-720

  Piovano, Jérome ^a   Rousson, Mikaël ^b   Papadopoulo, Théodore ^a  

^a UNIVERSITÉ PARIS EST   (France)

^b SIEMENS CORPORATE RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL COMPLEXITY; CONTOUR MEASUREMENT; GLOBAL OPTIMIZATION; MATHEMATICAL MODELS; POISSON EQUATION; ROBUST CONTROL; STATISTICS;

COMPUTATIONAL BURDEN; CONTOUR EVOLUTION; ENERGY FORMULATION; GLOBAL STATISTICS;

IMAGE SEGMENTATION;

EID: 37249018663     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-72823-8_61     Document Type: Conference Paper

References (16)

1. G. Aubert, M. Barlaud, O. Faugeras, and S. Jehan-Besson. Image segmentation using active contours: Calculus of variations or shape gradients? SIAM Journal of Applied Mathematics, 63(6):2128-2154, 2003.
2. V. Caselles, R. Kimmel, and G. Sapiro. Geodesic active contours. International Journal of Computer Vision, Vol 22:61-79, 1997.
3. D. Cremers, M. Rousson, and R. Deriche. A review of statistical approaches to level set segmentation: integrating color, texture, motion and shape. International Journal of Computer Vision, 2006.
4. R. Deriche. Using Canny's criteria to derive a recursively implemented optimal edge detector. The International Journal of Computer Vision, 1(2): 167-187, May 1987.
5. A. Dervieux and F. Thomasset. A finite element method for the simulation of Raleigh-Taylor instability. Springer Led. Notes in Math., 771:145-158, 1979.
6. O. Juan, R. Keriven, and G. Postelnicu. Stochastic motion and the level set method in computer vision: Stochastic active contours. The International Journal of Computer Vision, 69(1):7-25, 2006.
7. S. Kichenassamy, A. Kumar, P. Olver, A. Tannenbaum, and A. Yezzi. Gradient flows and geometric active contour models. In Proceedings of the 5International Conference on Computer Vision, pages 810-815, Boston, MA, June 1995. IEEE Computer Society Press.
8. J. Kim, J. Fisher, A. Yezzi, M. Cetin, and A. Willsky. Nonparametric methods for image segmentation using information theory and curve evolution. In IEEE International Conference on Image Processing, pages 797-800, September 2002.
9. D. Mumford and J. Shah. Optimal approximations by piecewise smooth functions and associated variational problems. Comm. Pure Appl. Math., 42:577-685, 1989.
10. S. Osher and J. Sethian. Fronts propagation with curvature dependent speed: Algorithms based on Hamilton-Jacobi formulations. J. of Comp. Phys., 79:12-49, 1988.
11. N. Paragios and R. Deriche. Geodesic active regions and level set methods for supervised texture segmentation. The International Journal of Computer Vision, 46(3):223-247, 2002.
12. M. Rousson and R. Deriche. A variational framework for active and adaptative segmentation of vector valued images. In Proc. IEEE Workshop on Motion and Video Computing, pages 56-62, Orlando, Florida, December 2002.
13. J. Sethian. Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Sciences. Cambridge Monograph on Applied and Computational Mathematics. Cambridge University Press, 1999.
14. A. Tsai, A. Jr. Yezzi, and A.S. Willsky. Curve evolution implementation of the mumford-shah functional for image segmentation, denoising, interpolation, and magnification. IEEE Transactions on Image Processing, 10(8) :1169-1186, August 2001.
15. L. Vese. Multiphase object detection and image segmentation. In S. Osher and N. Paragios, editors, Geometric Level Set Methods in Imaging, Vision and Graphics, pages 175-194. Springer Verlag, 2003.
16. L. Vese and T. Chan. A multiphase level set framework for image segmentation using the mumford and shah model. International Journal of Computer Vision, 50:271-293, 2002.