| Abstract: |
| The Chan-Vese functionals have proven to by a first-class method for segmentation and classification.
Previously they have been implemented with level-set methods based on a pixel-wise representation of
the level-sets. Later parametrized level-set approximations, such as splines, have been studied. In
this talk we consider neural networks as parametrized approximations of level-set functions. We
show in particular, that parametrized two-layer networks are most efficient to approximate polyhedral
segments and classes. We also prove the efficiency for segmentation and classification. |
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