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 language | English |
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Title of host publication | Computer Vision - ECCV 2010 : 11th European Conference on Computer Vision, Heraklion, Crete, Greece, September 5-11, 2010, Proceedings, Part VI |
Editors | Kostas Daniilidis, Petros Maragos, Nikos Paragios |
Number of pages | 14 |
Volume | Part VI |
Publisher | Springer |
Publication date | 2010 |
Pages | 43-56 |
ISBN (Print) | 978-3-642-15566-6 |
ISBN (Electronic) | 978-3-642-15567-3 |
DOIs | |
Publication status | Published - 2010 |
Event | 11th European Conference on Computer Vision - Heraklion, Greece Duration: 5 Sept 2010 → 11 Sept 2010 Conference number: 11 |
Conference
Conference | 11th European Conference on Computer Vision |
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Number | 11 |
Country/Territory | Greece |
City | Heraklion |
Period | 05/09/2010 → 11/09/2010 |
Series | Lecture notes in computer science |
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Number | 6316 |
ISSN | 0302-9743 |