Abstract
Variational frameworks based on level set methods are popular for the general problem of image segmentation. They combine different feature channels in an energy minimization approach. In contrast to other popular segmentation frameworks, e.g. the graph cut framework, current level set formulations do not allow much user interaction. Except for selecting the initial boundary, the user is barely able to guide or correct the boundary propagation. Based on Dempster-Shafer theory of evidence we propose a segmentation framework which integrates user interaction in a novel way. Given the input image, the proposed algorithm determines the best segmentation allowing the user to take global influence on the boundary propagation.
This work is partially funded by the German Research Foundation (RO 2497/6-1).
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References
Boykov, Y., Jolly, M.: Interactive graph cuts for optimal boundary and region segmentation of objects in nd images. In: International Conference on Computer Vision, vol. 1, pp. 105–112 (2001)
Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. International Journal of Computer Vision 22(1), 61–79 (1997)
Chan, T., Vese, L.: Active contours without edges. IEEE Transactions on Image Processing 10(2), 266–277 (2001)
Cremers, D., Fluck, O., Rousson, M., Aharon, S.: A probabilistic level set formulation for interactive organ segmentation. In: Proc. of the SPIE Medical Imaging, San Diego, USA (2007)
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(2), 195–215 (2007)
Cremers, D., Schnörr, C., Weickert, J.: Diffusion Snakes: Combining statistical shape knowledge and image information in a variational framework. In: Paragios, N. (ed.) IEEE First Int. Workshop on Variational and Level Set Methods, Vancouver, pp. 137–144 (2001)
Dempster, A.P.: A generalization of bayesian inference. Journal of the Royal Statistical Society. Series B (Methodological) 30(2), 205–247 (1968)
Dempster, A., Laird, N., Rubin, D., et al.: Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society. Series B (Methodological) 39(1), 1–38 (1977)
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active Contour models. International Journal of Computer Vision 1(4), 321–331 (1988)
Kim, J., Fisher III, J., Yezzi Jr., A., Cetin, M., Willsky, A.: Nonparametric methods for image segmentation using information theory and curve evolution. In: IEEE International Conference on Image Processing (ICIP), pp. 797–800 (2002)
Malladi, R., Sethian, J.A., Vemuri, B.C.: Shape modeling with front propagation: A level set approach. IEEE Transactions on Pattern Analysis and Machine Intelligence 17, 158–175 (1995)
Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proc. 8th Int’l Conf. Computer Vision, vol. 2, pp. 416–423 (2001)
Mumford, D., Shah, J.: Boundary detection by minimizing functionals. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 22–26. IEEE Computer Society Press, Springer, San Francisco, CA (1985)
Osher, S., Paragios, N.: Geometric level set method in imaging, vision, and graphics. Springer, Heidelberg (2003)
Osher, S., Sethian, J.: Fronts propagating with curvature dependent speed: Algorithm based on hamilton-jacobi formulation. Journal of Computational Physics 79, 12–49 (1988)
Rother, C., Kolmogorov, V., Blake, A.: Grabcut: Interactive foreground extraction using iterated graph cuts. In: ACM SIGGRAPH 2004 Papers, p. 314. ACM, New York (2004)
Rousson, M., Brox, T., Deriche, R.: Active unsupervised texture segmentation on a diffusion based feature space. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Madison, WI, pp. 699–704 (2003)
Rousson, M., Paragios, N.: Shape priors for level set representations. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2351, pp. 78–92. Springer, Heidelberg (2002)
Scheuermann, B., Rosenhahn, B.: Feature quarrels: The dempster-shafer evidence theory for image segmentation using a variational framework. In: Kimmel, R., Klette, R., Sugimoto, A. (eds.) ACCV 2010, Part II. LNCS, vol. 6493, pp. 426–439. Springer, Heidelberg (2011)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 888–905 (2000)
Tsai, A., Yezzi Jr., A., Wells III, W., Tempany, C., Tucker, D., Fan, A., Grimson, W., Willsky, A.: Model-based curve evolution technique for image segmentation. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 463–468 (2001)
Zhu, S.C., Yuille, A.: Region competition: unifying snakes, region growing, and bayes/mdl for multiband image segmentation. IEEE Transaction on Pattern Analysis and Machine Intelligence 18(9), 884–900 (1996)
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Scheuermann, B., Rosenhahn, B. (2011). Interactive Image Segmentation Using Level Sets and Dempster-Shafer Theory of Evidence. In: Heyden, A., Kahl, F. (eds) Image Analysis. SCIA 2011. Lecture Notes in Computer Science, vol 6688. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21227-7_61
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DOI: https://doi.org/10.1007/978-3-642-21227-7_61
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