Principal geodesic analysis

In geometric data analysis and statistical shape analysis, principal geodesic analysis is a generalization of principal component analysis to a non… (More)
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Topic mentions per year

2003-2016
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2014
2014
The importance of manifolds and Riemannian geometry is spreading to applied fields in which the need to model non-linear… (More)
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2014
2014
Computing a concise representation of the anatomical variability found in large sets of images is an important first step in many… (More)
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2013
2013
Principal geodesic analysis (PGA) is a generalization of principal component analysis (PCA) for dimensionality reduction of data… (More)
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2010
2010
Manifolds are widely used to model non-linearity arising in a range of computer vision applications. This paper treats statistics… (More)
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2008
2008
We present a novel method for automatic shape model building from a collection of training shapes. The result is a shape model… (More)
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2007
2007
PGA, or Principal Geodesic Analysis, is an extension of the classical PCA (Principal Component Analysis) to the case of data… (More)
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2007
2007
This paper describes how face recognition can be effected using 3D shape information extracted from single 2D image views. We… (More)
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Highly Cited
2004
Highly Cited
2004
Diffusion tensor magnetic resonance imaging (DT-MRI) is emerging as an important tool in medical image analysis of the brain… (More)
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Highly Cited
2004
Highly Cited
2004
A primary goal of statistical shape analysis is to describe the variability of a population of geometric objects. A standard… (More)
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Highly Cited
2003
Highly Cited
2003
Principal component analysis has proven to be useful for understanding geometric variability in populations of parameterized… (More)
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