Ruprecht-Karls-Universität Heidelberg
HCI->Prof. Dr. Björn Ommer

 

Prof. Dr. Björn Ommer

Full Professor for Computer Vision


Universität Heidelberg
   Heidelberg Collaboratory for Image Processing (HCI) &  

   Interdisciplinary Center for Scientific Computing (IWR) &  



Speyerer Str. 6
Uni Heidelberg, IWR
D-69115 Heidelberg, Germany

Tel.(office):   +49 6221/54-7853
Tel.(secret.): +49 6221/54-8875
Fax:               +49 6221/54-8790
Email:            ommer (at) uni-heidelberg (dot) de

That's me

                »» Open positions for a Postdoc and PhD student in Web-Scale Computer Vision

Brief C.V.

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

He has studied computer science together with physics as a minor subject at the Rheinische Friedrich-Wilhelms-Universität Bonn, Germany. In 2003 he was awarded a diploma (~M.Sc.) in computer science (summa cum laude)---his diploma 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 Pattern Recognition Letters. Björn is one of the directors of the HCI, a member of the extended board of directors 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 CVPR 2014, ICCV 2013, CVPR 2011, and CVPR 2010. Björn has organized the 2011 DAGM Workshop on Unsolved Problems in Pattern Recognition.


Research Areas

Computer vision, machine learning, cognitive science; esp.: visual object recognition in images and video, action recognition, tracking, shape analysis, graphical models, compositionality, perceptual organization and applications in biomedical image analysis, geoinformation processing, and visual analysis of cultural heritage.


Selected Publications

» Please also refer to the research section for an overview over selected projects and to our ongoing grants and projects.

Pradeep Yarlagadda and Björn Ommer,
Beyond the sum of parts: Voting with groups of dependent entities,
IEEE Transactions on PAMI, IEEE, 2014 (accepted, in press).

Wahl, A.S., Omlor, W., Rubio, J.C., Chen, J.L., Zheng, H., Schröter, A., Gullo, M., Weinmann, O., Kobayashi, K.,
Helmchen, F., Ommer, B., Schwab, M.E.,
Asynchronous therapy restores motor control by rewiring of the rat corticospinal tract after stroke,
Science 344(6189): 1250-1255 (article), 2014.

Borislav Antic and Björn Ommer,
Learning Latent Constituents for Recognition of Group Activities in Video,
in: ECCV'14, Springer, 2014 (accepted, in press). Oral slides: PDF / PPT (zipped w/ videos).

Angela Eigenstetter, Masato Takami, and Björn Ommer,
Randomized Max-Margin Compositions for Visual Recognition,
in: CVPR'14, IEEE, 2014 (accepted, in press).

Antonio Monroy, Peter Bell, and Björn Ommer,
Morphological analysis for investigating artistic images,
Image and Vision Computing 32(6):414-423, Elsevier, 2014.
» Preprint.

Masato Takami, Peter Bell, and Björn Ommer,
An Approach to Large Scale Interactive Retrieval of Cultural Heritage,
in: Proceedings of the EUROGRAPHICS Workshops on Graphics and Cultural Heritage, EUROGRAPHICS Association, 2014 (accepted, in press).

Melih Kandemir, Jose C Rubio, Ute Schmidt, Christian Wojek, Johannes Welbl, Björn Ommer, Fred A Hamprecht,
Event Detection by Feature Unpredictability in Phase-Contrast Videos of Cell Cultures,
in: MICCAI, 2014 (accepted, in press).

Masato Takami, Peter Bell, and Björn Ommer,
Offline Learning of Prototypical Negatives for Efficient Online Exemplar SVM,
in: Proceedings of the IEEE Winter Conference on Applications of Computer Vision, IEEE, 2014 (accepted, in press).

Borislav Antic, Timo Milbich, and Björn Ommer,
Less is More: Video Trimming for Action Recognition,
in: ICCV'13 (HACI), Springer, 2013.

Peter Bell, Joseph Schlecht, and Björn Ommer,
Nonverbal Communication in Medieval Illustrations Revisited by Computer Vision and Art History,
Visual Resources Journal (Special Issue on Digital Art History) 29(1):26-37, Taylor and Francis, 2013.
» Preprint.

Björn Ommer,
The Role of Shape in Visual Recognition,
in: Shape Perception in Human and Computer Vision: An Interdisciplinary Perspective, Springer, 2013.
» Preprint.

Pradeep Yarlagadda, Antonio Monroy, Bernd Carque, and Björn Ommer,
Towards a Computer-based Understanding of Medieval Images,
in: Scientific Computing and Cultural Heritage - Contributions in Computational Humanities, pages 89-97, Springer, ISBN: 978-3-642-28020-7, 2013. » Project page.

Christoph Garbe and Björn Ommer,
Parameter Estimation in Image Processing and Computer Vision,
in: Model Based Parameter Estimation: Theory and Applications, pages 311-334, Springer, ISBN: 978-3-642-30366-1, 2013

Angela Eigenstetter and Björn Ommer,
Visual Recognition using Embedded Feature Selection for Curvature Self-Similarity,
in: NIPS'12, 2012.

Pradeep Yarlagadda and Björn Ommer,
From Meaningful Contours to Discriminative Object Shape,
in: ECCV'12, Springer, 2012.

Antonio Monroy and Björn Ommer,
Beyond Bounding-Boxes: Learning Object Shape by Model-driven Grouping,
in: ECCV'12, Springer, 2012.

Antonio Monroy, Peter Bell, and Björn Ommer,
Shaping Art with Art: Morphological Analysis for Investigating Artistic Reproductions,
in: ECCV'12 (VISART), Springer, 2012.

Angela Eigenstetter*, Pradeep Yarlagadda*, and Björn Ommer,
Max-Margin Regularization for Reducing Accidentalness in Chamfer Matching,
in: ACCV'12, Springer, 2012 (* indicates equal contribution).

Borislav Antic and Björn Ommer,
Robust Multiple-Instance Learning with Superbags,
in: ACCV'12, Springer, 2012. Oral slides.

Pradeep Yarlagadda*, Angela Eigenstetter*, and Björn Ommer,
Learning Discriminative Chamfer Regularization,
in: BMVC'12, 2012 (* indicates equal contribution).

Borislav Antic and Björn Ommer,
Video Parsing for Abnormality Detection,
in: ICCV'11, IEEE, 2011.

Joseph Schlecht and Björn Ommer,
Contour-based Object Detection,
in: BMVC'11, 2011.

Antonio Monroy, Angela Eigenstetter, and Björn Ommer,
Beyond Straight Lines - Object Detection using Curvature,
in: ICIP'11, IEEE, 2011.

Joseph Schlecht, Bernd Carque, and Björn Ommer,
Detecting Gestures in Medieval Images,
in: ICIP'11, IEEE, 2011.

Antonio Monroy, Bernd Carque and Björn Ommer,
Reconstructing the Drawing Process of Reproductions from Medieval Images,
in: ICIP'11, IEEE, 2011.

Antonio Monroy, Till Kroeger, Matthias Arnold, and Björn Ommer,
Parametric Object Detection for Iconographic Analysis,
in: Scientific Computing and Cultural Heritage (SCCH), 2011.

Pradeep Yarlagadda, Antonio Monroy, Bernd Carque and Björn Ommer,
Top-down Analysis of Low-level Object Relatedness Leading to Semantic Understanding of Medieval Image Collections,
in: Computer Vision and Image Analysis of Art II, Proc. of SPIE Vol. 7869, pp. 061-069, 2011.

Pradeep Yarlagadda, Antonio Monroy and Björn Ommer,
Voting by Grouping Dependent Parts,
in: ECCV'10, LNCS 6315, pp. 197-210, Springer, 2010.

Björn Ommer and Joachim M. Buhmann,
Learning the Compositional Nature of Visual Object Categories for Recognition,
IEEE Transactions on PAMI, 32(3): 501-516, IEEE, 2010.

Jenny Wagner and Björn Ommer,
Efficiently Clustering Earth Mover's Distance,
in: ACCV'10, Springer, 2010.

Pradeep Yarlagadda, Antonio Monroy, Bernd Carque and Björn Ommer,
Recognition and Analysis of Objects in Medieval Images,
in: ACCV'10 e-Heritage, Springer, 2010.

Björn Ommer and Jitendra Malik,
Multi-Scale Object Detection by Clustering Lines,
in: ICCV'09, IEEE, 2009.

Björn Ommer, Theodor Mader and Joachim M. Buhmann,
Seeing the Objects Behind the Dots: Recognition in Videos from a Moving Camera,
in: International Journal of Computer Vision (IJCV), 83(1):57-71, Springer, 2009
Supplementary material (videos) & Reprint from SpringerLink.
    » To obtain the video dataset, please send me an email.

Pradeep Yarlagadda, Antonio Monroy, Bernd Carque, and Björn Ommer,
Towards a Computer-based Understanding of Medieval Images,
in: Scientific Computing and Cultural Heritage (SCCH), Springer, 2009.

Björn Ommer,
Seeing the Objects Behind the Parts: Learning Compositional Models for Visual Recognition,
VDM Verlag, ISBN: 978-3-639-02144-8, 2008.

Björn Ommer and Joachim M. Buhmann,
Compositional Object Recognition, Segmentation, and Tracking in Video,
in: International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR'07), LNCS 4679, Springer, 2007.

Christian Sigg, Bernd Fischer, Björn Ommer, Volker Roth, and Joachim M. Buhmann,
Nonnegative CCA for Audiovisual Source Separation,
in: IEEE International Workshop on Machine Learning for Signal Processing'07, IEEE, 2007.

Björn Ommer and Joachim M. Buhmann,
Learning the Compositional Nature of Visual Objects,
in: CVPR'07, IEEE, 2007.

Volker Roth and Björn Ommer
Exploiting Low-level Image Segmentation for Object Recognition,
in: Pattern Recognition (Symposium of the DAGM), LNCS 4174, Springer, 2006.

Björn Ommer, Michael Sauter and Joachim M. Buhmann,
Learning Top-Down Grouping of Compositional Hierarchies for Recognition,
in: CVPR'06 (POCV), IEEE 2006.

Björn Ommer and Joachim M. Buhmann,
Learning Compositional Categorization Models,
in: ECCV'06, LNCS 3953, Part III, pp. 316-329, Springer, 2006.

Björn Ommer and Joachim M. Buhmann,
Object Categorization by Compositional Graphical Models,
in: EMMCVPR'05, LNCS 3757, pp. 235–250, Springer, 2005.


Björn Ommer and Joachim M. Buhmann,
A Compositionality Architecture for Perceptual Feature Grouping,
in: EMMCVPR'03, LNCS 2683, pp. 275-290, Springer, 2003.


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


Links

Computer Vision Group @ Uni Heidelberg

HCI @ Uni Heidelberg

University of Heidelberg

Computer Vision Group @ UC Berkeley

Pattern Analysis and Machine Learning Group @ ETH Zurich




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