A convex formulation for global histogram based binary segmentation

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

Volumn 8081 LNCS, Issue , 2013, Pages 335-349

  Yildizoǧlu, Romain ^a   Aujol, Jean François ^a   Papadakis, Nicolas ^a  

^a UNIVERSITÉ DE BORDEAUX   (France)

Author keywords

convex; global histogram; non smooth optimization; segmentation; structure tensor

Indexed keywords

BINARY SEGMENTATION; CONVEX; CUMULATIVE HISTOGRAM; GLOBAL HISTOGRAM; NONSMOOTH OPTIMIZATION; NUMERICAL ALGORITHMS; STRUCTURE TENSORS; WASSERSTEIN DISTANCE;

ALGORITHMS; IMAGE SEGMENTATION; OPTIMIZATION; PATTERN RECOGNITION; STATISTICAL METHODS; TENSORS;

GRAPHIC METHODS;

EID: 84884970688     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-40395-8_25     Document Type: Conference Paper

References (31)

1. Aubert, G., Barlaud, M., Faugeras, O., Jehan-Besson, S.: Image segmentation using active contours: Calculus of variations or shape gradients? SIAM Applied Mathematics 63, 2003 (2002)
2. Aubert, G., Kornprobst, P.: Mathematical Problems in Image Processing. Applied Mathematical Sciences, vol. 147. Springer (2002)
3. Aujol, J.-F., Aubert, G., Blanc-Féraud, L.: Wavelet-based level set evolution for classification of textured images. IEEE Transactions on Image Processing 12, 1634-1641 (2003)
4. Ayed, I., Chen, H., Punithakumar, K., Ross, I., Li, S.: Graph cut segmentation with a global constraint: Recovering region distribution via a bound of the bhattacharyya measure. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3288-3295. IEEE (2010)
5. Brown, E., Chan, T.F., Bresson, X.: Completely Convex Formulation of the Chan-Vese Image Segmentation Model. In: IJCV, pp. 1-19 (2011)
6. Brox, T., Rousson, M., Deriche, R., Weickert, J.: Unsupervised segmentation incorporating colour, texture, and motion. In: Petkov, N., Westenberg, M.A. (eds.) CAIP 2003. LNCS, vol. 2756, pp. 353-360. Springer, Heidelberg (2003)
7. Brox, T., Weickert, J.: Level set segmentation with multiple regions. IEEE Transactions on Image Processing 15(10), 3213-3218 (2006)
8. Chambolle, A., Pock, T.: A first-order primal-dual algorithm for convex problems with applications to imaging. JMIV 40, 120-145 (2011)
9. Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Transactions on Image Processing 10(2), 266-277 (2001)
10. Collins, M.D., Xu, J., Grady, L., Singh, V.: Random walks based multi-image segmentation: Quasiconvexity results and gpu-based solutions. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2012), pp. 1656-1663. IEEE (2012)
11. Cremers, D., Rousson, M., Deriche, R.: A review of statistical approaches to level set segmentation: Integrating color, texture, motion and shape. International Journal of Computer Vision 72, 215 (2007)
12. Delfour, M.C., Zolésio, J.-P.: Shapes and geometries: analysis, differential calculus, and optimization. Society for Industrial and Applied Mathematics, Philadelphia (2001)
13. Gorelick, L., Schmidt, F.R., Boykov, Y., Delong, A., Ward, A.: Segmentation with non-linear regional constraints via line-search cuts. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part I. LNCS, vol. ECCV 2012, pp. 583-597. Springer, Heidelberg (2012)
14. Herbulot, A., Jehan-Besson, S., Duffner, S., Barlaud, M., Aubert, G.: Segmentation of vectorial image features using shape gradients and information measures. Journal of Mathematical Imaging and Vision 25(3), 365-386 (2006)
15. Kim, J., Fisher, J.W., Yezzi, A., Cetin, M., Willsky, A.S.: A nonparametric statistical method for image segmentation using information theory and curve evolution. IEEE Transactions on Image Processing 14, 1486-1502 (2005)
16. Mumford, D., Shah, J.: Optimal approximation by piecewise smooth functions and associated variational problems. Comm. Pure Appl. Math. 42, 577-685 (1989)
17. Ni, K., Bresson, X., Chan, T.F., Esedoglu, S.: Local histogram based segmentation using the wasserstein distance. International Journal of Computer Vision 84(1), 97-111 (2009)
18. Nikolova, M., Esedoglu, S., Chan, T.F.: Algorithms for finding global minimizers of image segmentation and denoising models. SIAM Journal on Applied Mathematics 66(5), 1632-1648 (2006)
19. Osher, S., Sethian, J.A.: Fronts propagating with curvature-dependent speed: Algorithms based on hamilton-jacobi formulations. JCP 79(1), 12-49 (1988)
20. Paragios, N., Deriche, R.: Geodesic active regions for supervised texture segmentation. In: ICCV (1999)
21. Peyré, G., Fadili, J., Rabin, J.: Wasserstein active contours. In: IEEE International Conference on Image Processing (ICIP 2012) (2012)
22. Pock, T., Chambolle, A.: Diagonal preconditioning for first order primal-dual algorithms in convex optimization. In: IEEE International Conference on Computer Vision (ICCV 2011), pp. 1762-1769 (2011)
23. Pock, T., Cremers, D., Bischof, H., Chambolle, A.: Global solutions of variational models with convex regularization. SIAM JIS 3, 1122-1145 (2010)
24. Punithakumar, K., Yuan, J., Ben Ayed, I., Li, S., Boykov, Y.: A convex max-flow approach to distribution-based figure-ground separation. SIAM Journal on Imaging Sciences 5(4), 1333-1354 (2012)
25. Rother, C., Minka, T., Blake, A., Kolmogorov, V.: Cosegmentation of image pairs by histogram matching-incorporating a global constraint into mrfs. In: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2006), vol. 1, pp. 993-1000. IEEE (2006)
26. Rousson, M., Brox, T., Deriche, R.: Active unsupervised texture segmentation on a diffusion based feature space. In: CVPR (2003)
27. Vese, L.A., Chan, T.F.: A multiphase level set framework for image segmentation using the mumford and shah model. International Journal of Computer Vision 50, 271-293 (2002)
28. Vicente, S., Kolmogorov, V., Rother, C.: Joint optimization of segmentation and appearance models. In: IEEE International Conference on Computer Vision (ICCV 2009), pp. 755-762. IEEE (2009)
29. Yildizoglu, R., Aujol, J.-F., Papadakis, N.: Active contours without level sets. In: IEEE International Conference on Image Processing (ICIP 2012) (2012)
30. Yuan, Y., Ukwatta, E., Tai, X.C., Fenster, A., Schnörr, C.: A fast global optimization-based approach to evolving contours with generic shape prior. In: submission in IEEE TPAMI, also UCLA Tech. Report CAM 12-38 (2012)
31. Zhu, S.C., Lee, T.S., Yuille, A.L.: Region competition: unifying snakes, region growing, energy/bayes/mdl for multi-band image segmentation. In: IEEE International Conference on Computer Vision (ICCV 1995), pp. 416-423 (June 1995)