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Hanno Ackermann, Ph.D.
Hanno Ackermann left the Institut für Informationsverabeitung.
Publications and research activities from the time after the departure are not listed here.

Hanno Ackermann studied Computer Engineering at the University of Mannheim. He received his masters degree (Dipl.-Inf.) in 2003. From 10/2004 until 3/2008 he did his Phd at the University of Okayama, Japan. From 5/2008 until 9/2008 he worked as PostDoc at the Max-Planck-Institute for Computer Science in Saarbruecken, Germany. Since 10/2008 he is a member of the group of Prof. Rosenhahn at Leibniz University Hannover. In January and February 2014, he did a short stay with Prof. David Suter. He is currently funded by a DFG-scholarship.

He is interested in supervised and unsupervised learning including Reinforcement Learning, segmentation and clustering of data as well as model fitting.

Projects
Structure and Motion Estimation
Structure and Motion Estimation
Semantic Scene Analysis
Semantic Scene Analysis
Statistical Face Models
Statistical Face Models
HDR Imaging
HDR Imaging
Publications
Show selected publications only
  • Florian Kluger, Hanno Ackermann, Eric Brachmann, Michael Ying Yang, Bodo Rosenhahn
    Cuboids Revisited: Learning Robust 3D Shape Fitting to Single RGB Images
    CVPR, June 2021
    (Arxiv.org) BibTeX
  • Yuren Cong, Hanno Ackermann, Wentong Liao, Michael Ying Yang, Bodo Rosenhahn
    NODIS: Neural Ordinary Differential Scene Understanding
    European Conference on Computer Vision (ECCV), August 2020
    (Arxiv.org) BibTeX
  • Hanno Ackermann, Holger Meuel, Bodo Rosenhahn, Jörn Ostermann
    Verfahren und Vorrichtung zum Aufnehmen eines Digitalbildes
    Deutsches Patent und Markenamt (DPMA), pp. 1-12, patent number DPMA, Amtl. Az. 10 2017 129 77, patent application 2017-12-13, August 2020
    (Link) BibTeX
  • Florian Kluger, Hanno Ackermann, Michael Ying Yang, Bodo Rosenhahn
    Temporally Consistent Horizon Lines
    International Conference on Robotics and Automation (ICRA), Paris, France, June 2020
    (Arxiv.org) BibTeX
  • Florian Kluger, Eric Brachmann, Hanno Ackermann, Carsten Rother, Michael Ying Yang, Bodo Rosenhahn
    CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus
    Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, June 2020
    (Arxiv.org) BibTeX
  • Stella Graßhof, Hanno Ackermann, Sami Brandt, Jörn Ostermann
    Multilinear Modelling of Faces and Expressions
    Transactions on Pattern Analysis and Machine Intelligence (TPAMI), IEEE, April 2020
    (IEEEexplore) BibTeX
  • Sami Brandt, Hanno Ackermann, Stella Graßhof
    Uncalibrated Non-Rigid Factorisation by Independent Subspace Analysis
    Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops, Seoul, Korea, October 2019
    (IEEEexplore) BibTeX
  • Maren Awiszus, Hanno Ackermann, Bodo Rosenhahn
    Learning Disentangled Representations via Independent Subspaces
    Proceedings of the IEEE International Conference on Computer Vision Workshops (ICCVW), October 2019
    (pdfPDF) BibTeX
  • Christoph Reinders, Hanno Ackermann, Michael Ying Yang, Bodo Rosenhahn
    Learning Convolutional Neural Networks for Object Detection with very little Training Data
    Multimodal Scene Understanding, Academic Press, 2019, edited by Michael Ying Yang, Bodo Rosenhahn and Vittorio Murino
    BibTeX
  • Florian Kluger, Christoph Reinders, Kevin Raetz, Philipp Schelske, Bastian Wandt, Hanno Ackermann, Bodo Rosenhahn
    Region-based Cycle-Consistent Data Augmentation for Object Detection
    2018 IEEE International Conference on Big Data, December 2018
    (pdf) BibTeX
  • Bastian Wandt, Hanno Ackermann, Bodo Rosenhahn
    A Kinematic Chain Space for Monocular Motion Capture
    ECCV Workshops, September 2018
    (pdf) BibTeX
  • Christoph Reinders, Hanno Ackermann, Michael Ying Yang, Bodo Rosenhahn
    Object Recognition from very few Training Examples for Enhancing Bicycle Maps
    2018 IEEE Intelligent Vehicles Symposium (IV), June 2018
    (pdfArxiv.org) BibTeX
  • Holger Meuel, Hanno Ackermann, Bodo Rosenhahn, Jörn Ostermann
    Physical High Dynamic Range (HDR) Imaging with Conventional Sensors
    Proceedings of the 33rd Picture Coding Symposium (PCS), pp. 209-213, San Francisco, California, USA, June 2018
    (pdf, pdf) BibTeX
  • Florian Kluger, Hanno Ackermann, Michael Ying Yang, Bodo Rosenhahn
    Deep Learning for Vanishing Point Detection Using an Inverse Gnomonic Projection
    39th German Conference on Pattern Recognition, Springer Lecture Notes in Computer Science (LNCS), Basel, Switzerland, September 2017
    (pdfDOI) BibTeX
  • Michael Ying Yang, Wentong Liao, Hanno Ackermann, Bodo Rosenhahn
    On support relations and semantic scene graphs
    ISPRS Journal of Photogrammetry and Remote Sensing, Elsevier, Vol. 131, pp. 15-25, July 2017
    (Link) BibTeX
  • Stella Graßhof, Hanno Ackermann, Felix Kuhnke, Jörn Ostermann, Sami Brandt
    Projective Structure from Facial Motion
    15th IAPR International Conference on Machine Vision Applications (MVA) (accepted), Nagoya (Japan), May 2017
    (pdf, pdf) BibTeX
  • Stella Graßhof, Hanno Ackermann, Sami Brandt, Jörn Ostermann
    Apathy is the Root of all Expressions
    12th IEEE Conference on Automatic Face and Gesture Recognition (FG2017), Washington D.C., USA, 2017
    (pdf, pdf) BibTeX
  • Michael Ying Yang, Hanno Ackermann, Weiyao Lin, Sitong Feng, Bodo Rosenhahn
    Motion Segmentation via Global and Local Sparse Subspace Optimization
    Photogrammetric Engineering & Remote Sensing, 2017
    BibTeX
  • Holger Meuel, Stephan Ferenz, Marco Munderloh, Hanno Ackermann, Jörn Ostermann
    In-loop Radial Distortion Compensation for Long-term Mosaicking of Aerial Videos
    Proc. of the 23rd IEEE International Conference on Image Processing (ICIP), pp. 2961-2965, Phoenix, Arizona, USA, September 2016
    (pdf, pdfIEEEexplore) BibTeX
  • Bastian Wandt, Hanno Ackermann, Bodo Rosenhahn
    3D Reconstruction of Human Motion from Monocular Image Sequences
    Transactions on Pattern Analysis and Machine Intelligence, IEEE, Vol. 38, No. 8, pp. 1505-1516, 2016
    (pdfDOI) BibTeX
  • Stefan Siersdörfer, Philipp Kemkes, Hanno Ackermann, Sergej Zerr
    Who with Whom and How? - Guided Pattern Mining for Extracting Large Social Networks using Search Engines
    24th ACM International Conference on Information and Knowledge Management (CIKM), Melbourne, Australia, October 2015
    BibTeX
  • Bastian Wandt, Hanno Ackermann, Bodo Rosenhahn
    3D Human Motion Capture from Monocular Image Sequences
    IEEE Conference on Computer Vision and Pattern Recognition Workshops, IEEE, June 2015
    (pdfDOI) BibTeX
  • Kai Cordes, Mark Hockner, Hanno Ackermann, Bodo Rosenhahn, Jörn Ostermann
    WM-SBA: Weighted Multibody Sparse Bundle Adjustment
    The 14th IAPR International Conference on Machine Vision Applications (MVA), pp. 162--165, Tokyo, Japan, May 2015
    (pdfDOI) BibTeX
  • Stella Graßhof, Hanno Ackermann, Jörn Ostermann
    Estimation of Face Parameters using Correlation Analysis and a Topology Preserving Prior
    14th IAPR International Conference on Machine Vision Applications (MVA), Tokyo, May 2015
    (pdf, pdf) BibTeX
  • Hanno Ackermann, Björn Scheuermann, Tat-Jun Chin, Bodo Rosenhahn
    Randomly Walking Can Get You Lost: Graph Segmentation with Unknown Edge Weights
    10th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), Springer Lecture Notes in Computer Sciences (LNCS), Hong Kong, China, 2015
    (pdfDOI) BibTeX
  • Pulak Purkait, Tat-Jun Chin, Hanno Ackermann, David Suter
    Clustering with hypergraphs: the case for large hyperedges
    European Conference on Computer Vision (ECCV), Springer, pp. 672-687, September 2014
    (pdf) BibTeX
  • Hanno Ackermann
    Factorization
    Computer Vision: A Reference Guide, Springer, pp. 288-291, 2014, edited by Katsushi Ikeuchi
    (Springer) BibTeX
  • Christian Cordes, Hanno Ackermann, Bodo Rosenhahn
    A Low-Rank Constraint for Parallel Stereo Cameras
    Proceedings of the German Conference on Pattern Recognition (GCPR), Springer Lecture Notes on Computer Sciences (LNCS), pp. 31-40, September 2013, edited by Joachim Weickert, Matthias Hein, Bernt Schiele
    (pdfDOI) BibTeX
  • Hanno Ackermann, Bodo Rosenhahn
    Non-Rigid Self-Calibration Of A Projective Camera
    Proceedings of the 11th Asian Conference on Computer Vision (ACCV), November 2012
    (pdfDOI) BibTeX
  • H. Ackermann, B. Rosenhahn
    Projective Reconstruction from Incomplete Trajectories by Global and Local Constraints
    The 8th European Conference on Visual Media Production (CVMP), November 2011
    (pdf) BibTeX
  • F. R. Schmidt, H. Ackermann, B. Rosenhahn
    Multilinear Model Estimation with L2-Regularization
    33rd Annual Symposium of the German Association for Pattern Recognition (DAGM) , September 2011
    (pdf) BibTeX
  • Hanno Ackermann, Bodo Rosenhahn
    A Linear Solution to 1-Dimensional Subspace Fitting under Incomplete Data
    Asian Conference on Computer Vision, Queenstown, New Zealand, November 2010
    (pdf) BibTeX
  • Nils Hasler, Hanno Ackermann, Bodo Rosenhahn, Thorsten Thormählen, Hans-Peter Seidel
    Multilinear Pose and Body Shape Estimation of Dressed Subjects from Image Sets
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE Computer Society, San Francisco, USA, June 2010
    (pdf) BibTeX
  • Hanno Ackermann, Bodo Rosenhahn
    Trajectory Reconstruction for Affine Structure-from-Motion by Global and Local Constraints
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2009, IEEE Computer Society, pp. 2890-2897, Miami, USA, June 2009
    (pdf) BibTeX
  • Hanno Ackermann, Kenichi Kanatani
    Fast projective reconstruction: Toward ultimate efficiency
    IPSJ Transactions on Computer Vision and Image Media, Vol. 49, pp. 68-78, March 2008
    (pdf) BibTeX
  • Hanno Ackermann, Kenichi Kanatani
    Iterative Low Complexity Factorization for Projective Reconstruction
    2nd Workshop on Robot Vision, Springer, pp. 153-164, Auckland, New Zealand, February 2008, edited by Sommer, Gerald; Klette, Reinhard
    (pdf) BibTeX
  • Hanno Ackermann, Kenichi Kanatani
    Robust and efficient 3-D reconstruction by self-calibration
    IAPR Conference on Machine Vision Applications (MVA 2007), pp. 178-181, Tokyo, Japan, May 2007
    (pdf) BibTeX
  • Kenichi Kanatani, Yasuyuki Sugaya, Hanno Ackermann
    Uncalibrated factorization using a variable symmetric affine camera
    9th European Conference on Computer Vision (ECCV 2006), pp. 147-158, Graz, Austria, May 2006
    (pdf) BibTeX
  • Tomomi Takashina, Hanno Ackermann
    R-Based Environment for Image Processing Algorithm Design
    Distributed Statistical Computing (DSC), Technische Universität Wien in Vienna, Austria, 2003, edited by Kurt Hornik and Friedrich Leisch
    BibTeX
Other activities

 

Themen für Bachelor- und Masterarbeiten:

 

  • Machine Learning (Bachelor/Master)

 

Please contact me for further information.

 

 

 

Source code:

The sources to the CVPR09 paper can be found here. This code is provided "as is" without any implied warranty for non-commercial use only. Permission to modify and distribute this code is granted. If you use this software or any modifications, please cite the corresponding CVPR paper.

Japanese

Some short info about myself in Japanese.
 

Last changed: 2024-11-21