• Corpus ID: 1618

Riemannian level-set methods for tensor-valued data

@article{Zra2007RiemannianLM,
  title={Riemannian level-set methods for tensor-valued data},
  author={Mourad Z{\'e}ra{\"i} and Maher Moakher},
  journal={ArXiv},
  year={2007},
  volume={abs/0705.0214}
}
We present a novel approach for the derivation of PDEs modeling curvaturedriven flows for matrix-valued data. This approach is based on the Riemannian geometry of the manifold of Symmetric Positive Definite MatricesP(n). The differential geometric attributes ofP(n) such as the bi-invariant metric, the covariant derivative and the Christoffel symbols allow us to extend scalar-valued mean curvature and snakes methods to the tensor data setting. Since the data live onP(n), these methods have the… 

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