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

51 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationComputer Vision - ECCV 2010 : 11th European Conference on Computer Vision, Heraklion, Crete, Greece, September 5-11, 2010, Proceedings, Part VI
EditorsKostas Daniilidis, Petros Maragos, Nikos Paragios
Number of pages14
VolumePart VI
PublisherSpringer
Publication date2010
Pages43-56
ISBN (Print)978-3-642-15566-6
ISBN (Electronic)978-3-642-15567-3
DOIs
Publication statusPublished - 2010
Event11th European Conference on Computer Vision - Heraklion, Greece
Duration: 5 Sept 201011 Sept 2010
Conference number: 11

Conference

Conference11th European Conference on Computer Vision
Number11
Country/TerritoryGreece
CityHeraklion
Period05/09/201011/09/2010
SeriesLecture notes in computer science
Number6316
ISSN0302-9743

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