HCI->Prof. Dr. Björn Ommer

Prof. Dr. Björn Ommer

Assistant Professor for Computer Vision

Universität Heidelberg
Heidelberg Collaboratory for Image Processing (HCI) &

Interdisciplinary Center for Scientific Computing (IWR) &

Transcultural Studies (TS)

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

Tel.(office):   +49 6221/54-7853
Tel.(secret.): +49 6221/54-8846
Fax:               +49 6221/54-8790
Email:           ommer (at) uni-heidelberg (dot) de

»» Open position for a PhD student in Web-Scale Computer Vision

Brief C.V.

Björn Ommer is an assistant 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 2010 and CVPR 2011.

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.

Publications

» Please also refer to the research section for an overview over selected projects.

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

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

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

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

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

Borislav Antic and Björn Ommer,
Robust Multiple-Instance Learning with Superbags,
in: ACCV'12, Springer, 2012 (accepted, in press).

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

Peter Bell, Joseph Schlecht, and Björn Ommer,
Nonverbal Communication in Medieval Illustrations Revisited by Computer Vision and Art History,
in: Visual Resources Journal (Special Issue on Digital Art History), Taylor and Francis, 2012 (accepted, in press).

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, 2012

Björn Ommer,
The Role of Shape in Visual Recognition,
in: Shape Perception in Human and Computer Vision: An Interdisciplinary Perspective, Springer, (accepted, in press).

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, 2012.

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.

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.