English

Riemannian level-set methods for tensor-valued data

Computer Vision and Pattern Recognition 2007-05-23 v1
Authors: Mourad Zerai , Maher Moakher
Comments: 11 pages, 03 figures, to be published in the proceedings of SSVM 2007, LNCS Springer
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Abstract

We present a novel approach for the derivation of PDE modeling curvature-driven flows for matrix-valued data. This approach is based on the Riemannian geometry of the manifold of Symmetric Positive Definite Matrices Pos(n).

Keywords

manifold learning

Cite

@article{arxiv.0705.0214,
  title  = {Riemannian level-set methods for tensor-valued data},
  author = {Mourad Zerai and Maher Moakher},
  journal= {arXiv preprint arXiv:0705.0214},
  year   = {2007}
}

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