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- W2110348055 abstract "The covariance matrix adaptation evolution strategy (CMA-ES) has been successfully used to minimize functionals on vector spaces. We generalize the concept of the CMA-ES to Riemannian manifolds and evaluate its performance in two experiments. First, we minimize synthetic functionals on the 2-D sphere. Second, we consider the reconstruction of shapes in 3-D voxel data. A novel formulation of this problem leads to the minimization of edge and region-based segmentation functionals on the Riemannian manifold of parametric 3-D medial axis representation. We compare the results to gradient-based methods on manifolds and particle swarm optimization on tangent spaces and differential evolution." @default.
- W2110348055 created "2016-06-24" @default.
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- W2110348055 date "2010-04-01" @default.
- W2110348055 modified "2023-10-17" @default.
- W2110348055 title "The CMA-ES on Riemannian Manifolds to Reconstruct Shapes in 3-D Voxel Images" @default.
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- W2110348055 doi "https://doi.org/10.1109/tevc.2009.2029567" @default.
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