Prof. Dr. Björn Ommer

Full Professor for Computer Vision

Heidelberg Collaboratory for Image Processing (HCI) &
Interdisciplinary Center for Scientific Computing (IWR),
Universität Heidelberg

Mathematikon (INF 205), Room 4.321
HCI / IWR, Uni Heidelberg
D-69120 Heidelberg, Germany
Tel.(office): +49 6221/54-14806
Tel.(secret.): +49 6221/54-14807
Fax: +49 6221/54-14814

Email: ommer (at) uni-heidelberg (dot) de
Bjorn

»» Open Position in Deep Web-Scale Computer Vision



Brief C.V.


Björn Ommer is a full professor for Scientific Computing and leads the Computer Vision Group at Heidelberg University.

He has studied computer science together with physics as a minor subject at the University of Bonn, Germany. His diploma (~M.Sc.) thesis focused on visual grouping based on perceptual organization and compositionality.

After that he pursued his doctoral studies at ETH Zurich Switzerland in the Pattern Analysis and Machine Learning Group headed by Joachim M. Buhmann. He received his Ph.D. degree from ETH Zurich in 2007 for his dissertation "Learning the Compositional Nature of Objects for Visual Recognition" which was awarded the ETH Medal.

Thereafter, Björn held a post-doc position in the Computer Vision Group of Jitendra Malik at UC Berkeley.

He serves as an associate editor for the journal IEEE T-PAMI and previously for Pattern Recognition Letters. Björn is one of the directors of the HCI and of the IWR, principle investigator in the research training group 1653 ("Spatio/Temporal Graphical Models and Applications in Image Analysis"), and a member of the executive board and scientific committee of the Heidelberg Graduate School HGS MathComp. He has received the Outstanding Reviewer Award at ICCV'15, CVPR'14, ICCV'13, CVPR'11, and CVPR'10 and is serving as Area Chair for ECCV'18. Björn has organized the 2011 DAGM Workshop on Unsolved Problems in Pattern Recognition.



Research Areas


Computer vision, machine learning, cognitive science, biomedical image analysis, and the digital humanities; esp.: deep learning & weak supervision, visual object recognition in images and video, action recognition, shape analysis, compositionality, and their applications.



Publications


Main publications' list »» Publications of the Ommer lab

2013

Bell, P, Schlecht, J and Ommer, B (2013). Nonverbal Communication in Medieval Illustrations Revisited by Computer Vision and Art History. Visual Resources Journal, Special Issue on Digital Art History. Taylor & Francis. 29 26--37. http://www.tandfonline.com/doi/abs/10.1080/01973762.2013.761111

2012

Eigenstetter, A, Yarlagadda, P and Ommer, B (2012). Max-Margin Regularization for Reducing Accidentalness in Chamfer Matching. Proceedins of the Aian Conference on Computer Vision. Springer. 152--163PDF iconTechnical Report (7.31 MB)
Monroy, A, Bell, P and Ommer, B (2012). Shaping Art with Art: Morphological Analysis for Investigating Artistic Reproductions. Proceedings of the European Conference on Computer Vision, Workshop on VISART. Springer. 7583 571--580PDF iconTechnical Report (7 MB)
Yarlagadda, P and Ommer, B (2012). From Meaningful Contours to Discriminative Object Shape. Proceedings of the European Conference on Computer Vision. Springer. 7572 766--779PDF iconTechnical Report (4.58 MB)
Eigenstetter, A and Ommer, B (2012). Visual Recognition using Embedded Feature Selection for Curvature Self-Similarity. Proceedings of the Conference on Advances in Neural Information Processing Systems. MIT Press. 377--385PDF iconTechnical Report (3.27 MB)
Yarlagadda, P, Eigenstetter, A and Ommer, B (2012). Learning Discriminative Chamfer Regularization. BMVC. Springer. 1--11. http://www.bmva.org/bmvc/2012/BMVC/paper020/paper020.pdf
Antic, B and Ommer, B (2012). Robust Multiple-Instance Learning with Superbags. Proceedins of the Aian Conference on Computer Vision. Springer. 242--255PDF iconTechnical Report (319.58 KB)
Monroy, A and Ommer, B (2012). Beyond Bounding-Boxes: Learning Object Shape by Model-driven Grouping. IEEE Transactions on Pattern Analysis and Machine Intelligence. Springer. 7574 582--595PDF iconTechnical Report (1.58 MB)

2011

Monroy, A, Carque, B and Ommer, B (2011). Reconstructing the Drawing Process of Reproductions from Medieval Images. Proceedings of the International Conference on Image Processing. IEEE. 2974--2977. https://hciweb.iwr.uni-heidelberg.de/compvis/research/manesse/PDF iconTechnical Report (2.43 MB)
Monroy, A, Eigenstetter, A and Ommer, B (2011). Beyond Straight Lines - Object Detection using Curvature. International Conference on Image Processing (ICIP). IEEEPDF iconTechnical Report (2.65 MB)
Schlecht, J, Carque, B and Ommer, B (2011). Detecting Gestures in Medieval Images. Proceedings of the International Conference on Image Processing. IEEE. 1309--1312PDF iconTechnical Report (1.61 MB)
Schlecht, J and Ommer, B (2011). Contour-based Object Detection. BMVC. 1--9PDF iconTechnical Report (2.62 MB)
Antic, B and Ommer, B (2011). Video Parsing for Abnormality Detection. Proceedings of the IEEE International Conference on Computer Vision. IEEE. 2415--2422PDF iconTechnical Report (990.21 KB)
Yarlagadda, P, Monroy, A, Carque, B and Ommer, B (2011). Top-down Analysis of Low-level Object Relatedness Leading to Semantic Understanding of Medieval Image Collections. Conference on Computer Vision and Image Analysis of Art II. 7869 61--69PDF iconTechnical Report (11.06 MB)
Monroy, A, Kröger, T, Arnold, M and Ommer, B (2011). Parametric Object Detection for Iconographic Analysis. Scientific Computing & Cultural Heritage. http://www.academia.edu/9439693/Parametric_Object_Detection_for_Iconographic_Analysis

2010

Wagner, J and Ommer, B (2010). Efficiently Clustering Earth Mover's Distance. Proceedins of the Aian Conference on Computer Vision. Springer. 477--488PDF iconTechnical Report (841.98 KB)
Yarlagadda, P, Monroy, A and Ommer, B (2010). Voting by Grouping Dependent Parts. Proceedings of the European Conference on Computer Vision. Springer. 6315 197--210PDF iconTechnical Report (2.99 MB)
Yarlagadda, P, Monroy, A, B., C and Ommer, B (2010). Recognition and Analysis of Objects in Medieval Images. Proceedins of the Aian Conference on Computer Vision, Workshop on e-Heritage. Springer. 296--305PDF iconTechnical Report (2.76 MB)
Ommer, B and Buhmann, J M (2010). Learning the Compositional Nature of Visual Object Categories for Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence. IEEE. 32 501--516PDF iconTechnical Report (2.78 MB)

2009

Keränen, S V E, DePace, A, Hendriks, C L Luengo, Fowlkes, C, Arbelaez, P, Ommer, B, Brox, T, Henriquez, C, Wunderlich, Z, Eckenrode, K, Fischer, B, Hammonds, A and Celniker, S E (2009). Computational Analysis of Quantitative Changes in Gene Expression and Embryo Morphology between Species. Evolution-The Molecular Landscape
Yarlagadda, P, Monroy, A, B., C and Ommer, B (2009). Towards a Computer-based Understanding of Medieval Images. Scientific Computing & Cultural Heritage. Springer. 89--97. http://link.springer.com/chapter/10.1007%2F978-3-642-28021-4_10#page-1
Ommer, B and Malik, J (2009). Multi-scale Object Detection by Clustering Lines. Proceedings of the IEEE International Conference on Computer Vision. IEEE. 484--491PDF iconTechnical Report (3.18 MB)
Ommer, B, Mader, T and Buhmann, J M (2009). Seeing the Objects Behind the Dots: Recognition in Videos from a Moving Camera. International Journal of Computer Vision. Springer. 83 57--71PDF iconTechnical Report (9.61 MB)

2008

Ommer, B (2008). Seeing The Objects Behind The Parts: Learning Compositional Models For Visual Recognition. VDM Verlag. http://www.amazon.com/Seeing-Objects-Behind-Parts-Compositional/dp/3639021444/ref=sr_1_1?ie=UTF8&s=books&qid=1232659136&sr=1-1

2007

Ommer, B and Buhmann, J M (2007). Learning the Compositional Nature of Visual Objects. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE. 1--8PDF iconTechnical Report (2.78 MB)
Sigg, C, Fischer, B, Ommer, B, Roth, V and Buhmann, J M (2007). Nonnegative CCA for Audiovisual Source Separation. International Workshop on Machine Learning for Signal Processing. IEEE. 253--258PDF iconTechnical Report (1.27 MB)
Ommer, B and Buhmann, J M (2007). Compositional Object Recognition, Segmentation, and Tracking in Video. Proceedings of the International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition. Springer. 4679 318--333PDF iconTechnical Report (2.78 MB)

2006

Ommer, B, Sauter, M and M., B J (2006). Learning Top-Down Grouping of Compositional Hierarchies for Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Workshop on Perceptual Organization in Computer Vision. IEEE. 194--194PDF iconTechnical Report (358.98 KB)
Roth, V and Ommer, B (2006). Exploiting Low-level Image Segmentation for Object Recognition. Pattern Recognition, Symposium of the DAGM. Springer. 4174 11--20PDF iconTechnical Report (473.84 KB)
Ommer, B and Buhmann, J M (2006). Learning Compositional Categorization Models. Proceedings of the European Conference on Computer Vision. Springer. 3953 316--329PDF iconTechnical Report (1.35 MB)

2005

Ommer, B and Buhmann, J M (2005). Object Categorization by Compositional Graphical Models. Proceedings of the International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition. Springer. 3757 235--250PDF iconTechnical Report (2.07 MB)

2003

Ommer, B and Buhmann, J M (2003). A Compositionality Architecture for Perceptual Feature Grouping. Proceedings of the International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition. Springer. 2683 275--290PDF iconTechnical Report (2.89 MB)

Pages



Reports and Publications in Popular Science


TV documentary on our interdisciplinary work featured by RNF Television.

Björn Ommer, Bilder im Chaos, in: Universitas 68(810): 46-55, 2013.

Björn Ommer, From Chaos to Image - The Grammar of Patterns, in: Ruperto Carola Magazine, 03/2013.

Björn Ommer, Vom Pixel zum Bild - Wie Computer das Sehen lernen und die Forschungsarbeiten von Geistes- und Naturwissenschaftlern unterstützen können, in: Ruperto Carola Magazine, 02/2011.

Image Recognition: Teaching Computers to See, in: Young Talents -Innovative Ideas - Viable Alliances, 2011.

Automatische Bildanalyse - Blinde Computer sollen sehen lernen, in: Spiegel Online news report, 22.07.2011.

Dem Computer das Sehen beibringen, in: Rhein-Neckar-Zeitung newspaper article, 19.04.2010.



Teaching


Computer Vision Group: Teaching Website


Links


Computer Vision Group @ Uni Heidelberg

HCI @ Uni Heidelberg

University of Heidelberg

Computer Vision Group @ UC Berkeley

Institute for Machine Learning @ ETH Zurich