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Gaussian Process Morphable Models

Lüthi, Marcel and Gerig, Thomas and Jud, Christoph and Vetter, Thomas. (2017) Gaussian Process Morphable Models. IEEE Transactions on Pattern Analysis and Machine Intelligence, PP (99).

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Official URL: https://edoc.unibas.ch/59212/

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Abstract

Models of shape variations have become a central component for the automated analysis of images. An important class of shape models are point distribution models (PDMs). These models represent a class of shapes as a normal distribution of point variations, whose parameters are estimated from example shapes. Principal component analysis (PCA) is applied to obtain a low-dimensional representation of the shape variation in terms of the leading principal components. In this paper, we propose a generalization of PDMs, which we refer to as Gaussian Process Morphable Models (GPMMs). We model the shape variations with a Gaussian process, which we represent using the leading components of its Karhunen-Loève expansion. To compute the expansion, we make use of an approximation scheme based on the Nyström method. The resulting model can be seen as a continuous analog of a standard PDM. However, while for PDMs the shape variation is restricted to the linear span of the example data, with GPMMs we can define the shape variation using any Gaussian process. For example, we can build shape models that correspond to classical spline models and thus do not require any example data. Furthermore, Gaussian processes make it possible to combine different models. For example, a PDM can be extended with a spline model, to obtain a model that incorporates learned shape characteristics but is flexible enough to explain shapes that cannot be represented by the PDM.
Faculties and Departments:05 Faculty of Science > Departement Mathematik und Informatik > Informatik > Computergraphik Bilderkennung (Vetter)
UniBasel Contributors:Vetter, Thomas and Lüthi, Marcel
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:Institute of Electrical and Electronics Engineers
ISSN:0162-8828
Note:Publication type according to Uni Basel Research Database: Journal article
Language:English
Identification Number:
Last Modified:09 Feb 2018 13:06
Deposited On:09 Feb 2018 13:05

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