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Andreas Morel-Forster
Andreas Morel-Forster
Postdoctoral Researcher, Department for Mathematics and Computer Science, University of Basel
Verified email at unibas.ch
Title
Cited by
Cited by
Year
Shape Modeling Using Gaussian Process Morphable Models
M Lüthi, A Forster, T Gerig, T Vetter
Statistical Shape and Deformation Analysis: Methods, Implementation and …, 2017
193*2017
Morphable face models-an open framework
T Gerig, A Morel-Forster, C Blumer, B Egger, M Luthi, S Schönborn, ...
2018 13th IEEE International Conference on Automatic Face & Gesture …, 2018
1872018
Analyzing and reducing the damage of dataset bias to face recognition with synthetic data
A Kortylewski, B Egger, A Schneider, T Gerig, A Morel-Forster, T Vetter
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019
752019
Markov chain monte carlo for automated face image analysis
S Schönborn, B Egger, A Morel-Forster, T Vetter
International Journal of Computer Vision 123 (2), 160-183, 2017
692017
Occlusion-aware 3d morphable models and an illumination prior for face image analysis
B Egger, S Schönborn, A Schneider, A Kortylewski, A Morel-Forster, ...
International Journal of Computer Vision 126 (12), 1269-1287, 2018
622018
Empirically analyzing the effect of dataset biases on deep face recognition systems
A Kortylewski, B Egger, A Schneider, T Gerig, A Morel-Forster, T Vetter
Proceedings of the IEEE conference on computer vision and pattern …, 2018
542018
Training deep face recognition systems with synthetic data
A Kortylewski, A Schneider, T Gerig, B Egger, A Morel-Forster, T Vetter
arXiv preprint arXiv:1802.05891, 2018
482018
A monte carlo strategy to integrate detection and model-based face analysis
S Schönborn, A Forster, B Egger, T Vetter
German Conference on Pattern Recognition, 101-110, 2013
282013
Background modeling for generative image models
S Schönborn, B Egger, A Forster, T Vetter
Computer Vision and Image Understanding 136, 117-127, 2015
202015
Can synthetic faces undo the damage of dataset bias to face recognition and facial landmark detection?
A Kortylewski, B Egger, A Morel-Forster, A Schneider, T Gerig, C Blumer, ...
arXiv preprint arXiv:1811.08565, 2018
14*2018
Occlusion-aware 3D Morphable Face Models.
B Egger, A Schneider, C Blumer, A Forster, S Schönborn, T Vetter
BMVC 2, 4, 2016
142016
Probabilistic fitting of active shape models
A Morel-Forster, T Gerig, M Lüthi, T Vetter
International Workshop on Shape in Medical Imaging, 137-146, 2018
132018
Pose normalization for eye gaze estimation and facial attribute description from still images
B Egger, S Schönborn, A Forster, T Vetter
German conference on pattern recognition, 317-327, 2014
122014
Human face shape analysis under spherical harmonics illumination considering self occlusion
J Zivanov, A Forster, S Schönborn, T Vetter
2013 International Conference on Biometrics (ICB), 1-8, 2013
112013
A closest point proposal for MCMC-based probabilistic surface registration
D Madsen, A Morel-Forster, P Kahr, D Rahbani, T Vetter, M Lüthi
European Conference on Computer Vision, 281-296, 2020
102020
Greedy structure learning of hierarchical compositional models
A Kortylewski, A Wieczorek, M Wieser, C Blumer, S Parbhoo, ...
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019
102019
Informed MCMC with Bayesian neural networks for facial image analysis
A Kortylewski, M Wieser, A Morel-Forster, A Wieczorek, S Parbhoo, ...
arXiv preprint arXiv:1811.07969, 2018
92018
Generative shape and image analysis by combining Gaussian processes and MCMC sampling
A Morel-Forster
University_of_Basel, 2016
82016
To fit or not to fit: Model-based face reconstruction and occlusion segmentation from weak supervision
C Li, A Morel-Forster, T Vetter, B Egger, A Kortylewski
arXiv preprint arXiv:2106.09614, 2021
42021
Robust registration of statistical shape models for unsupervised pathology annotation
D Rahbani, A Morel-Forster, D Madsen, M Lüthi, T Vetter
Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and …, 2019
42019
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