# Principal geodesic analysis for the study of nonlinear statistics of shape

@article{Fletcher2004PrincipalGA, title={Principal geodesic analysis for the study of nonlinear statistics of shape}, author={P. Thomas Fletcher and Conglin Lu and Stephen M. Pizer and Sarang C. Joshi}, journal={IEEE Transactions on Medical Imaging}, year={2004}, volume={23}, pages={995-1005} }

A primary goal of statistical shape analysis is to describe the variability of a population of geometric objects. A standard technique for computing such descriptions is principal component analysis. However, principal component analysis is limited in that it only works for data lying in a Euclidean vector space. While this is certainly sufficient for geometric models that are parameterized by a set of landmarks or a dense collection of boundary points, it does not handle more complex…

## 753 Citations

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This paper discusses the decoupling of pose and shape in multi-object sets using different normalization settings, and introduces methods of describing the statistics of object pose and object shape, both separately and simultaneously using a novel extension of PGA.

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