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ISBI
2008
IEEE
14 years 5 months ago
Deformation-based nonlinear dimension reduction: Applications to nuclear morphometry
We describe a new approach for elucidating the nonlinear degrees of freedom in a distribution of shapes depicted in digital images. By combining a deformation-based method for mea...
Gustavo K. Rohde, Wei Wang, Tao Peng, Robert F. Mu...
COMPGEOM
2011
ACM
12 years 8 months ago
Persistence-based clustering in riemannian manifolds
We present a clustering scheme that combines a mode-seeking phase with a cluster merging phase in the corresponding density map. While mode detection is done by a standard graph-b...
Frédéric Chazal, Leonidas J. Guibas,...
IDEAL
2005
Springer
13 years 10 months ago
Bearing Similarity Measures for Self-organizing Feature Maps
The neural representation of space in rats has inspired many navigation systems for robots. In particular, Self-Organizing (Feature) Maps (SOM) are often used to give a sense of lo...
Narongdech Keeratipranon, Frédéric M...
ECCV
2010
Springer
13 years 9 months ago
Manifold Valued Statistics, Exact Principal Geodesic Analysis and the Effect of Linear Approximations
Manifolds are widely used to model non-linearity arising in a range of computer vision applications. This paper treats statistics on manifolds and the loss of accuracy occurring wh...
JMIV
2006
185views more  JMIV 2006»
13 years 4 months ago
Intrinsic Statistics on Riemannian Manifolds: Basic Tools for Geometric Measurements
In medical image analysis and high level computer vision, there is an intensive use of geometric features like orientations, lines, and geometric transformations ranging from simp...
Xavier Pennec