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 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 ICCV 2015, 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, biomedical image analysis, and the digital humanities; esp.: visual object recognition in images and video, action recognition, shape analysis, graphical models, compositionality, perceptual organization and their applications.



Publications


Main publications' list »» Publications of the Ommer lab

2015

Rubio JC, Ommer B. Regularizing Max-Margin Exemplars by Reconstruction and Generative Models. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE; 2015. p. 4213--4221. PDF iconrubio_ommer_cvpr15.pdf (2.8 MB)
Antic B, Büchler U, Wahl AS, Schwab ME, Ommer B. Spatiotemporal Parsing of Motor Kinematics for Assessing Stroke Recovery. In Medical Image Computing and Computer-Assisted Intervention. Springer; 2015. PDF iconantic_ommer_miccai15.pdf (2.24 MB)
Antic B, Ommer B. Per-Sample Kernel Adaptation for Visual Recognition and Grouping. In Proceedings of the IEEE International Conference on Computer Vision. IEEE; 2015. PDF iconAntic_Per-Sample_Kernel_Adaptation_ICCV_2015_paper.pdf (1.58 MB)
Yarlagadda P, Ommer B. Beyond the Sum of Parts: Voting with Groups of Dependent Entities. IEEE Transactions on Pattern Analysis and Machine Intelligence [Internet]. IEEE; 2015;37:1134--1147. http://www.computer.org/csdl/trans/tp/preprint/06926849.pdf
Rubio JC, Eigenstetter A, Ommer B. Generative Regularization with Latent Topics for Discriminative Object Recognition. Pattern Recognition. Elsevier; 2015;48:3871--3880. PDF iconrubio_ommer_PatRec2015.pdf (5.49 MB)
Antic B, Ommer B. Spatio-temporal Video Parsing for Abnormality Detection. arXiv [Internet]. 2015;abs/1502.06235. http://arxiv.org/abs/1502.06235PDF icon1502.06235.pdf (4.61 MB)
Bell P, Ommer B. Training Argus. Kunstchronik. Monatsschrift für Kunstwissenschaft, Museumswesen und Denkmalpflege. Zentralinstitut für Kunstgeschichte; 2015;68:414--420.

2014

Takami M, Bell P, Ommer B. Offline Learning of Prototypical Negatives for Efficient Online Exemplar SVM. In Winter Conference on Applications of Computer Vision [Internet]. IEEE; 2014. p. 377--384. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6836075
Takami M, Bell P, Ommer B. An Approach to Large Scale Interactive Retrieval of Cultural Heritage. In Eurographics Workshop on Graphics and Cultural Heritage. The Eurographics Association; 2014. PDF icontakami_bell_ommer.Eurographicsgch14.pdf (7.94 MB)
Eigenstetter A, Takami M, Ommer B. Randomized Max-Margin Compositions for Visual Recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE; 2014. p. 3590--3597. PDF iconrm2c_cvpr14.pdf (8.01 MB)
Kandemir M, Rubio JC, Schmidt U, Welbl J, Ommer B, Hamprecht FA. Event Detection by Feature Unpredictability in Phase-Contrast Videos of Cell Cultures. In MICCAI. Proceedings. Springer; 2014. pp. 154-161.
Antic B, Ommer B. Learning Latent Constituents for Recognition of Group Activities in Video. In Proceedings of the European Conference on Computer Vision. Springer; 2014. p. 33--47. PDF iconantic_ommer_eccv14.pdf (4.54 MB)
Kandemir M, Rubio JC, Schmidt U, Wojek C, Welbl J, Ommer B, et al.. Event Detection by Feature Unpredictability in Phase-Contrast Videos of Cell Cultures. In Medical Image Computing and Computer-Assisted Intervention. Springer; 2014. p. 154--161. PDF iconkandemir_14_event.pdf (2 MB)
Monroy A, Bell P, Ommer B. Morphological Analysis for Investigating Artistic Images. Image and Vision Computing. Elsevier; 2014;32:414--423. PDF iconmonroy_ommer.ivc14.pdf (2.86 MB)
Wahl AS, Omlor W, Rubio JC, Chen JL, Zheng H, Schröter A, et al.. Asynchronous Therapy Restores Motor Control by Rewiring of the Rat Corticospinal Tract after Stroke. Science [Internet]. American Association for The Advancement of Science; 2014;344:1250--1255. http://www.sciencemag.org/content/344/6189/1250

2013

Garbe CS, Ommer B. Parameter Estimation in Image Processing and Computer Vision. In Model Based Parameter Estimation: Theory and Applications. Springer; 2013. p. 311--334. PDF icongarbe_ommer_mbpe.pdf (928 KB)
Ommer B. The Role of Shape in Visual Recognition. In Shape Perception in Human Computer Vision: An Interdisciplinary Perspective. Springer; 2013. p. 373--385. PDF iconommer_shape.pdf (8.18 MB)
Yarlagadda P, Monroy A, Carque B, Ommer B. Towards a Computer-based Understanding of Medieval Images. In Scientific Computing & Cultural Heritage [Internet]. Springer; 2013. p. 89--97. http://link.springer.com/chapter/10.1007/978-3-642-28021-4_10
Antic B, Milbich T, Ommer B. Less is More: Video Trimming for Action Recognition. In Proceedings of the IEEE International Conference on Computer Vision, Workshop on Understanding Human Activities: Context and Interaction. IEEE; 2013. p. 515--521. PDF iconantic_ommer.iccv13.pdf (984.89 KB)
Monroy A, Bell P, Ommer B. A Morphometric Approach to Reception Analysis of Premodern Art. In Scientific Computing & Cultural Heritage. 2013. PDF iconscch2013_monroy.pdf (17.75 MB)
Arnold M, Bell P, Ommer B. Automated Learning of Self-Similarity and Informative Structures in Architecture. In Scientific Computing & Cultural Heritage. 2013.
Bell P, Schlecht J, Ommer B. Nonverbal Communication in Medieval Illustrations Revisited by Computer Vision and Art History. Visual Resources Journal, Special Issue on Digital Art History [Internet]. Taylor & Francis; 2013;29:26--37. http://www.tandfonline.com/doi/abs/10.1080/01973762.2013.761111

2012

Antic B, Ommer B. Robust Multiple-Instance Learning with Superbags. In Proceedins of the Aian Conference on Computer Vision. Springer; 2012. p. 242--255. PDF iconantic_ommer_accv12.pdf (319.58 KB)
Eigenstetter A, Yarlagadda P, Ommer B. Max-Margin Regularization for Reducing Accidentalness in Chamfer Matching. In Proceedins of the Aian Conference on Computer Vision. Springer; 2012. p. 152--163. PDF iconeigenstetter_yarlagadda_ommer_ACCV12.pdf (7.31 MB)
Monroy A, Bell P, Ommer B. Shaping Art with Art: Morphological Analysis for Investigating Artistic Reproductions. In Proceedings of the European Conference on Computer Vision, Workshop on VISART. Springer; 2012. p. 571--580. PDF iconmonroy_ommer_art_eccv12.pdf (7 MB)
Yarlagadda P, Ommer B. From Meaningful Contours to Discriminative Object Shape. In Proceedings of the European Conference on Computer Vision. Springer; 2012. p. 766--779. PDF iconyarlagadda_ommer_eccv12.pdf (4.58 MB)
Eigenstetter A, Ommer B. Visual Recognition using Embedded Feature Selection for Curvature Self-Similarity. In Proceedings of the Conference on Advances in Neural Information Processing Systems. MIT Press; 2012. p. 377--385. PDF iconeigenstetter_ommer_nips12.pdf (3.27 MB)
Yarlagadda P, Eigenstetter A, Ommer B. Learning Discriminative Chamfer Regularization. In BMVC [Internet]. Springer; 2012. p. 1--11. http://www.bmva.org/bmvc/2012/BMVC/paper020/paper020.pdf

2011

Monroy A, Carque B, Ommer B. Reconstructing the Drawing Process of Reproductions from Medieval Images. In Proceedings of the International Conference on Image Processing. IEEE; 2011. p. 2974--2977. PDF icon1563325580.pdf (2.43 MB)
Schlecht J, Carque B, Ommer B. Detecting Gestures in Medieval Images. In Proceedings of the International Conference on Image Processing. IEEE; 2011. p. 1309--1312. PDF iconICIP11.pdf (1.61 MB)
Schlecht J, Ommer B. Contour-based Object Detection. In BMVC. 2011. p. 1--9. PDF iconBMVC11.pdf (2.62 MB)
Antic B, Ommer B. Video Parsing for Abnormality Detection. In Proceedings of the IEEE International Conference on Computer Vision. IEEE; 2011. p. 2415--2422. PDF iconparsing_iccv11.pdf (990.21 KB)
Yarlagadda P, Monroy A, Carque B, Ommer B. Top-down Analysis of Low-level Object Relatedness Leading to Semantic Understanding of Medieval Image Collections. In Conference on Computer Vision and Image Analysis of Art II. 2011. p. 61--69. PDF iconspie2010.pdf (11.06 MB)
Monroy A, Kröger T, Arnold M, Ommer B. Parametric Object Detection for Iconographic Analysis. In Scientific Computing & Cultural Heritage [Internet]. 2011. http://www.academia.edu/9439693/Parametric_Object_Detection_for_Iconographic_Analysis

2010

Wagner J, Ommer B. Efficiently Clustering Earth Mover's Distance. In Proceedins of the Aian Conference on Computer Vision. Springer; 2010. p. 477--488. PDF iconwagner_ommer_accv10.pdf (841.98 KB)
Yarlagadda P, Monroy A, Ommer B. Voting by Grouping Dependent Parts. In Proceedings of the European Conference on Computer Vision. Springer; 2010. p. 197--210. PDF iconyarlagadda_ommer_eccv10.pdf (2.99 MB)
Yarlagadda P, Monroy A, B. C, Ommer B. Recognition and Analysis of Objects in Medieval Images. In Proceedins of the Aian Conference on Computer Vision, Workshop on e-Heritage. Springer; 2010. p. 296--305. PDF iconaccv2010.pdf (2.76 MB)
Ommer B, Buhmann JM. Learning the Compositional Nature of Visual Object Categories for Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence. IEEE; 2010;32:501--516. PDF iconommer_pami10.pdf (2.78 MB)

2009

Keränen SVE, DePace A, Hendriks CLLuengo, Fowlkes C, Arbelaez P, Ommer B, et al.. Computational Analysis of Quantitative Changes in Gene Expression and Embryo Morphology between Species. In Evolution-The Molecular Landscape. 2009.
Yarlagadda P, Monroy A, B. C, Ommer B. Towards a Computer-based Understanding of Medieval Images. In Scientific Computing & Cultural Heritage [Internet]. Springer; 2009. p. 89--97. http://link.springer.com/chapter/10.1007%2F978-3-642-28021-4_10#page-1

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