Error-controlled model approximation for Gaussian process morphable models
DOI10.1007/s10851-018-0854-5zbMath1443.65030OpenAlexW2898565664WikidataQ129080420 ScholiaQ129080420MaRDI QIDQ2417934
Marcel Lüthi, Thomas Vetter, Thomas Gerig, Jürgen Dölz, Helmut Harbrecht
Publication date: 31 May 2019
Published in: Journal of Mathematical Imaging and Vision (Search for Journal in Brave)
Full work available at URL: https://edoc.unibas.ch/69076/3/main.pdf
Gaussian processimage registrationnon-rigid registrationshape registrationGaussian process morphable modelsGPMMpivoted Choleskystatistical shape modeling
Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) Direct numerical methods for linear systems and matrix inversion (65F05) Machine vision and scene understanding (68T45)
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- Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions
- Approximation of eigenfunctions in kernel-based spaces
- On the low-rank approximation by the pivoted Cholesky decomposition
- Bases for kernel-based spaces
- Integral equations. Theory and numerical treatment
- Error-controlled model approximation for Gaussian process morphable models
- Multiscale kernels
- Efficient approximation of random fields for numerical applications
- Computational anatomy: an emerging discipline
- Approximation of bi-variate functions: singular value decomposition versus sparse grids
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