Manifold valued statistics, exact principal, geodesic analysis and the effect of linear, approximations

Authors:
Stefan Sommer;François Lauze;Søren Hauberg;Mads Nielsen
Affiliations:
Dept. of Computer Science, Univ. of Copenhagen, Denmark;Dept. of Computer Science, Univ. of Copenhagen, Denmark;Dept. of Computer Science, Univ. of Copenhagen, Denmark;Dept. of Computer Science, Univ. of Copenhagen, Denmark and Nordic Bioscience Imaging, Herlev, Denmark
Venue:
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Year:
2010

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Abstract

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 when linearizing the manifold prior to performing statistical operations. Using recent advances in manifold computations, we present a comparison between the non-linear analog of Principal Component Analysis, Principal Geodesic Analysis, in its linearized form and its exact counterpart that uses true intrinsic distances. We give examples of datasets for which the linearized version provides good approximations and for which it does not. Indicators for the differences between the two versions are then developed and applied to two examples of manifold valued data: outlines of vertebrae from a study of vertebral fractures and spacial coordinates of human skeleton end-effectors acquired using a stereo camera and tracking software.