Abstract
Level set based segmentation has been used with and without shape priors, to approach difficult segmentation problems in several application areas. This paper addresses two limitations of the classical level set based segmentation approaches: They usually deliver just two regions – one foreground and one background region, and if some prior information is available, they are able to take into account just one prior but not more. In these cases, one object of interest is reconstructed but other possible objects of interest and unfamiliar image structures are suppressed.
The approach we propose in this paper can simultaneously handle an arbitrary number of regions and competing shape priors. Adding to that, it allows the integration of numerous pose invariant shape priors, while segmenting both known and unknown objects. Unfamiliar image structures are considered as separate regions. We use a region splitting to obtain the number of regions and the initialization of the required level set functions. In a second step, the energy of these level set functions is robustly minimized and similar regions are merged in a last step. All these steps are considering given shape priors. Experimental results demonstrate the method for arbitrary numbers of regions and competing shape priors.
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Fussenegger, M., Deriche, R., Pinz, A. (2006). A Multiphase Level Set Based Segmentation Framework with Pose Invariant Shape Priors. In: Narayanan, P.J., Nayar, S.K., Shum, HY. (eds) Computer Vision – ACCV 2006. ACCV 2006. Lecture Notes in Computer Science, vol 3852. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11612704_40
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DOI: https://doi.org/10.1007/11612704_40
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