Generalized Fast Marching Method: Applications to Image Segmentation

Abstract : In this paper, we propose a segmentation method based on the generalized fast marching method (GFMM) developed by Carlini et al. (submitted). The classical fast marching method (FMM) is a very efficient method for front evolution problems with normal velocity (see also Epstein and Gage, The curve shortening flow. In: Chorin, A., Majda, A. (eds.) Wave Motion: Theory, Modelling and Computation, 1997) of constant sign. The GFMM is an extension of the FMM and removes this sign constraint by authorizing time-dependent velocity with no restriction on the sign. In our modelling, the velocity is borrowed from the Chan-Vese model for segmentation (Chan and Vese, IEEE Trans Image Process 10(2):266-277, 2001). The algorithm is presented and analyzed and some numerical experiments are given, showing in particular that the constraints in the initialization stage can be weakened and that the GFMM offers a powerful and computationally efficient algorithm.
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Nicolas Forcadel, Carole Le Guyader, Christian Gout. Generalized Fast Marching Method: Applications to Image Segmentation. Numerical Algorithms, Springer Verlag, 2008, 48 (1-3), pp.189-211. ⟨10.1007/s11075-008-9183-x⟩. ⟨hal-00372740⟩



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