Publications

Below is a list of publications I have co-authored. For bibliometrics, see Google Scholar.

2018

Directional Statistics with the Spherical Normal Distribution
Søren Hauberg.
In Proceedings of FUSION 2018.
PDF Supplements

Deep Diffeomorphic Transformer Networks
Nicki Skafte Detlefsen, Oren Freifeld and Søren Hauberg.
In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, USA. July 2018.
PDF Code

On the Geometry of Latent Variable Models
Søren Hauberg.
Oberwolfach abstract from Statistics for Data with Geometric Structure, 2018 (3).
PDF Workshop and complete report

The non-central Nakagami distribution
Søren Hauberg.
Unpublished technical note, 2018.
PDF

Latent Space Oddity: on the Curvature of Deep Generative Models
Georgios Arvanitidis, Lars Kai Hansen and Søren Hauberg.
In International Conference on Learning Representations (ICLR), 2018.
PDF

Probabilistic Riemannian submanifold learning with wrapped Gaussian process latent variable models
Anton Mallasto, Søren Hauberg, and Aasa Feragen.
PDF ArXiv

Geodesic Clustering in Deep Generative Models
Tao Yang, Georgios Arvanitidis, Dongmei Fu, Xiaogang Li, and Søren Hauberg.
arXiv preprint, 2018.
PDF arXiv

Only Bayes should learn a manifold (on the estimation of differential geometric structure from data)
Søren Hauberg.
Unpublished manuscript, 2018.
PDF arXiv

2017

Intrinsic Grassmann Averages for Online Linear and Robust Subspace Learning
Rudrasis Chakraborty, Søren Hauberg, and Baba C. Vemuri.
In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Honolulu, USA. July 2017.
PDF arXiv

Maximum likelihood estimation of Riemannian metrics from Euclidean data
Georgios Arvanitidis, Lars Kai Hansen and Søren Hauberg.
In Geometric Science of Information (GSI), 2017.
PDF

2016

Data-driven forward model inference for EEG brain imaging
Sofie Therese Hansen, Søren Hauberg, and Lars Kai Hansen.
In NeuroImage, 2016.
Publisher's site Preprint

Dreaming More Data: Class-dependent Distributions over Diffeomorphisms for Learned Data Augmentation
Søren Hauberg, Oren Freifeld, Anders Boesen Lindbo Larsen, John W. Fisher III, and Lars Kai Hansen.
In Proceedings of the 19th international Conference on Artificial Intelligence and Statistics (AISTATS), volume 51, 2016.
PDF Publisher's site arXiv Animation Supplements

Transformations Based on Continuous Piecewise-Affine Velocity Fields
Oren Freifeld, Søren Hauberg, Kayhan Batmanghelich and John W. Fisher III.
In IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI).
PDF Supplements Derivative of gradient Code

A Locally Adaptive Normal Distribution
Georgios Arvanitidis, Lars Kai Hansen, and Søren Hauberg.
Neural Information Processing Systems (NIPS), 2016.
PDF arXiv YouTube

Open Problem: Kernel methods on manifolds and metric spaces. What is the probability of a positive definite geodesic exponential kernel?
Aasa Feragen and Søren Hauberg.
In Conference on Learning Theory (COLT), 2016.
PDF Publisher's site

2015

Scalable Robust Principal Component Analysis using Grassmann Averages
Søren Hauberg, Aasa Feragen, Raffi Enficiaud and Michael J. Black.
In IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI).
PDF Publishers site Supplements Paper site w. code

Highly-Expressive Spaces of Well-Behaved Transformations: Keeping It Simple
Oren Freifeld, Søren Hauberg, Kayhan Batmanghelich and John W. Fisher III.
In International Conference on Computer Vision (ICCV), Santiago, Chile. December 2015.
PDF Code

Geodesic Exponential Kernels: When Curvature and Linearity Conflict
Aasa Feragen,Françous Lauze, and Søren Hauberg.
In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Boston, USA. June 2015.
PDF Extended abstract (PDF) SIMBAD extended abstract (PDF) arXiv

Principal Curves on Riemannian Manifolds
Søren Hauberg.
In IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI).
PDF Publishers site Supplements Animation

A Random Riemannian Metric for Probabilistic Shortest-Path Tractography
Søren Hauberg, Michael Schober, Matthew Liptrot, Philipp Hennig, and Aasa Feragen.
In Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2015, Munich, Germany, September 2015.
PDF Youtube

2014

Probabilistic shortest path tractography in DTI using Gaussian Process ODE solvers
Michael Schober, Niklas Kasenburg, Aasa Feragen, Philipp Hennig, and Søren Hauberg.
In Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2014, Boston, USA, September 2014.
PDF Supplementary material Paper site w. code Youtube 1 Youtube 2

Probabilistic Solutions to Differential Equations and their Application to Riemannian Statistics
Philipp Hennig and Søren Hauberg.
In Proceedings of the 17th international Conference on Artificial Intelligence and Statistics (AISTATS), volume 33, 2014.
PDF Paper site w. code Youtube Supplements

Model Transport: Towards Scalable Transfer Learning on Manifolds
Oren Freifeld, Søren Hauberg, and Michael J. Black.
In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Columbus, Ohio, USA. June 2014.
PDF Supplementary material

Grassmann Averages for Scalable Robust PCA
Søren Hauberg, Aasa Feragen, and Michael J. Black.
In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Columbus, Ohio, USA. June 2014.
PDF Paper site w. code Tutorial video Results video Supplementary materials CVPR talk (video)

Metrics for Probabilistic Geometries
Alessandra Tosi, Søren Hauberg, Alfredo Vellido, and Neil D. Lawrence.
In The Conference on Uncertainty in Artificial Intelligence (UAI), Quebec, Canada. July 2014.
PDF arXiv

2013

Unscented Kalman Filtering on Riemannian Manifolds
Søren Hauberg, Françous Lauze, and Kim S. Pedersen.
Journal of Mathematical Imaging and Vision, 46(1):103-120, May 2013.
PDF Publishers site Errata

2012

A Geometric Take on Metric Learning
Søren Hauberg, Oren Freifeld, and Michael J. Black.
In Advances in Neural Information Processing Systems (NIPS) 25, MIT Press, pages 2033-2041, 2012.
PDF Supplementary material Code Poster

Natural Metrics and Least-Committed Priors for Articulated Tracking
Søren Hauberg, Stefan Sommer, and Kim S. Pedersen.
Image and Vision Computing, 30(6-7):453-461, 2012.
PDF Code Publishers site

HUMIM Software for Articulated Tracking
Søren Hauberg and Kim S. Pedersen.
Technical Report 01/2012, Department of Computer Science, University of Copenhagen, January 2012.
PDF

Spatial Measures between Human Poses for Classification and Understanding
Søren Hauberg and Kim S. Pedersen.
In In Articulated Motion and Deformable Objects, Springer Berlin Heidelberg, volume 7378, LNCS, pages 26-36, 2012.
Publishers site

A geometric framework for statistics on trees
Aasa Feragen, Mads Nielsen, Søren Hauberg, Pechin Lo, Marleen de Bruijne, and Françous Lauze.
Technical Report 11/02, Department of Computer Science, University of Copenhagen, January 2012.
PDF

2011

Unscented Kalman Filtering for Articulated Human Tracking
Anders B.L. Larsen, Søren Hauberg, and Kim S. Pedersen.
In Image Analysis, Springer Berlin Heidelberg, volume 6688, Lecture Notes in Computer Science, pages 228-237, 2011.
PDF Publishers site

Spatial Models of Human Motion
Søren Hauberg.
PhD thesis. University of Copenhagen, 2011.

Means in spaces of tree-like shapes
Aasa Feragen, Søren Hauberg, Mads Nielsen, and Françous Lauze.
In IEEE International Conference on Computer Vision (ICCV), pages 736 -746, 2011.
PDF Supplementary material

A Physically Natural Metric for Human Motion and the Associated Brownian Motion Model
Søren Hauberg and Kim S. Pedersen.
In 1st IEEE Workshop on Kernels and Distances for Computer Vision (ICCV workshop), 2011.
Workshop site

Data-Driven Importance Distributions for Articulated Tracking
Søren Hauberg and Kim S. Pedersen.
In Energy Minimization Methods in Computer Vision and Pattern Recognition, Springer Berlin Heidelberg, volume 6819, Lecture Notes in Computer Science, pages 287-299, 2011.
PDF Errata Code

Predicting Articulated Human Motion from Spatial Processes
Søren Hauberg and Kim S. Pedersen.
International Journal of Computer Vision (IJCV), 2011.
PDF Paper site Code Publishers site

An Empirical Study on the Performance of Spectral Manifold Learning Techniques
Peter Mysling, Søren Hauberg and Kim S. Pedersen.
In Artificial Neural Networks and Machine Learning - ICANN 2011, Springer Berlin Heidelberg, volume 6791, Lecture Notes in Computer Science, pages 347-354, 2011.
PDF

2010

Manifold Valued Statistics, Exact Principal Geodesic Analysis and the Effect of Linear Approximations
Stefan Sommer, Françous Lauze, Søren Hauberg, and Mads Nielsen.
In Computer Vision - ECCV 2010, Springer Berlin Heidelberg, volume 6316, pages 43-56, 2010.
PDF Publishers site

Dense Marker-less Three Dimensional Motion Capture
Søren Hauberg, Bente R. Jensen, Morten Engell-Nørregård, Kenny Erleben, and Kim S. Pedersen.
In Virtual Vistas; Eleventh International Symposium on the 3D Analysis of Human Movement, 2010.

GPU Accelerated Likelihoods for Stereo-Based Articulated Tracking
Rune M. Friborg, Søren Hauberg, and Kenny Erleben.
In The CVGPU workshop at European Conference on Computer Vision (ECCV) 2010.
PDF

Stick It! Articulated Tracking using Spatial Rigid Object Priors
Søren Hauberg and Kim S. Pedersen.
In Computer Vision - ACCV 2010, Springer Berlin Heidelberg, volume 6494, Lecture Notes in Computer Science, pages 758-769, 2010
PDF Errata Paper site Code Publishers site

Gaussian-like Spatial Priors for Articulated Tracking
Søren Hauberg, Stefan Sommer, and Kim S. Pedersen.
In Computer Vision - ECCV 2010, Springer Berlin Heidelberg, volume 6311, Lecture Notes in Computer Science, pages 425-437, 2010.
PDF Errata Paper site Code Publishers site

2009

Interactive Inverse Kinematics for Monocular Motion Estimation
Morten Engell-Nørregård, Søren Hauberg, Jerome Lapuyade,Kenny Erleben, and Kim S. Pedersen.
In The 6th Workshop on Virtual Reality Interaction and Physical Simulation (VRIPHYS), 2009.
Conference site Paper site

Three Dimensional Monocular Human Motion Analysis in End-Effector Space
Søren Hauberg, Jerome Lapuyade, Morten Engell-Nørregård, Kenny Erleben, and Kim S. Pedersen.
In Energy Minimization Methods in Computer Vision and Pattern Recognition, Springer Berlin Heidelberg, volume 5681, Lecture Notes in Computer Science, pages 235-248, 2009.
PDF Paper site Publishers site

2008

An Efficient Algorithm for Modelling Duration in Hidden Markov Models, with a Dramatic Application
Søren Hauberg and Jakob Sloth.
Journal of Mathematical Imaging and Vision, 2008.
PDF Paper site Publishers site

Brownian Warps for Non-Rigid Registration
Mads Nielsen, Peter Johansen, Andrew Jackson, Benny Lautrup, and Søren Hauberg.
Journal of Mathematical Imaging and Vision, 2008.
PDF Publishers site

GNU Octave Manual Version 3
John W. Eaton, David Bateman, and Søren Hauberg.
Network Theory Ltd., 2008.
Publishers site GNU Octave