Hanno Ackermann, Ph.D.
Leibniz Universität Hannover
Institut für Informationsverarbeitung
Appelstr. 9A
30167 Hannover
Germany
phone: +49 511 762-5328
fax: +49 511 762-5333
office location: room 1327A

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.

Show recent publications only
  • Conference Contributions
    • 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
    • Florian Kluger, Hanno Ackermann, Michael Ying Yang, Bodo Rosenhahn
      Temporally Consistent Horizon Lines
      International Conference on Robotics and Automation (ICRA), Paris, France, June 2020
    • 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
    • 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
    • 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
    • Florian Kluger, Christoph Reinders, Kevin Raetz, Philipp Schelske, Bastian Wandt, Hanno Ackermann, Bodo Rosenhahn
      Region-based Cycle-Consistent Data Augmentation for Object Detection
      IEEE International Conference on Big Data Workshops, December 2018
    • Bastian Wandt, Hanno Ackermann, Bodo Rosenhahn
      A Kinematic Chain Space for Monocular Motion Capture
      ECCV Workshops, September 2018
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • Hanno Ackermann, Bodo Rosenhahn
      Non-Rigid Self-Calibration Of A Projective Camera
      Proceedings of the 11th Asian Conference on Computer Vision (ACCV), November 2012
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
  • Journals
    • 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
    • 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
    • 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
    • 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
    • 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
  • Book Chapters
    • 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
    • Hanno Ackermann
      Factorization
      Computer Vision: A Reference Guide, Springer, pp. 288-291, 2014, edited by Katsushi Ikeuchi
  • Patents
    • 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
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.