Publications

Search

Show only items where

Author

Type

Term

Year

Keyword

Export 114 results:

Filters: Author is Björn Ommer [Clear All Filters]

A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

A

M. Takami, Bell, P., and Ommer, B., “An Approach to Large Scale Interactive Retrieval of Cultural Heritage”, in Eurographics Workshop on Graphics and Cultural Heritage, 2014.

Technical Report (7.94 MB)

A. - S. Wahl, Omlor, W., Rubio, J. C., Chen, J. L., Zheng, H., Schröter, A., Gullo, M., Weinmann, O., Kobayashi, K., Helmchen, F., Ommer, B., and Schwab, M. E., “Asynchronous Therapy Restores Motor Control by Rewiring of the Rat Corticospinal Tract after Stroke”, Science, vol. 344, p. 1250--1255, 2014.

S. Lang and Ommer, B., “Attesting Similarity: Supporting the Organization and Study of Art Image Collections with Computer Vision”, Digital Scholarship in the Humanities, Oxford University Press, vol. 33, no. 4, pp. 845-856, 2018.

M. Arnold, Bell, P., and Ommer, B., “Automated Learning of Self-Similarity and Informative Structures in Architecture”, in Scientific Computing & Cultural Heritage, 2013.

B

A. Blattmann, Milbich, T., Dorkenwald, M., and Ommer, B., “Behavior-Driven Synthesis of Human Dynamics”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2021.

A. Monroy and Ommer, B., “Beyond Bounding-Boxes: Learning Object Shape by Model-driven Grouping”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 7574. Springer, p. 582--595, 2012.

Technical Report (1.58 MB)

A. Monroy, Eigenstetter, A., and Ommer, B., “Beyond Straight Lines - Object Detection using Curvature”, in International Conference on Image Processing (ICIP), 2011.

Technical Report (2.65 MB)

P. Yarlagadda and Ommer, B., “Beyond the Sum of Parts: Voting with Groups of Dependent Entities”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, p. 1134--1147, 2015.

C

T. Milbich, Roth, K., Sinha, S., Schmidt, L., Ghassemi, M., and Ommer, B., “Characterizing Generalization under Out-Of-Distribution Shifts in Deep Metric Learning”. 2021.

M. Bautista, Sanakoyeu, A., Sutter, E., and Ommer, B., “CliqueCNN: Deep Unsupervised Exemplar Learning”, in Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS), Barcelona, 2016.

Article (5.79 MB)

B. Ommer and Buhmann, J. M., “Compositional Object Recognition, Segmentation, and Tracking in Video”, in Proceedings of the International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, 2007, vol. 4679, p. 318--333.

Technical Report (2.78 MB)

B. Ommer and Buhmann, J. M., “A Compositionality Architecture for Perceptual Feature Grouping”, in Proceedings of the International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, 2003, vol. 2683, p. 275--290.

Technical Report (2.89 MB)

S. V. E. Keränen, 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., “Computational Analysis of Quantitative Changes in Gene Expression and Embryo Morphology between Species”, in Evolution-The Molecular Landscape, 2009.

P. Bell and Ommer, B., “Computer Vision und Kunstgeschichte — Dialog zweier Bildwissenschaften”, in Computing Art Reader: Einführung in die digitale Kunstgeschichte, P. Kuroczyński et al. (ed.), 2018.

413-17-83318-2-10-20181210.pdf (2.98 MB)

D. Kotovenko, Sanakoyeu, A., Lang, S., and Ommer, B., “Content and Style Disentanglement for Artistic Style Transfer”, in Proceedings of the Intl. Conf. on Computer Vision (ICCV), 2019.

J. Schlecht and Ommer, B., “Contour-based Object Detection”, in BMVC, 2011, p. 1--9.

Technical Report (2.62 MB)

N. Sayed, Brattoli, B., and Ommer, B., “Cross and Learn: Cross-Modal Self-Supervision”, in German Conference on Pattern Recognition (GCPR) (Oral), Stuttgart, Germany, 2018.

Article (891.47 KB)

Oral slides (9.17 MB)

D

S. Lang and Ommer, B., “Das Objekt jenseits der Digitalisierung”, Das digitale Objekt, vol. 7. 2020.

lang_ommer_digitalhumanities_2020_.pdf (599.56 KB)

T. Dencker, Klinkisch, P., Maul, S. M., and Ommer, B., “Deep learning of cuneiform sign detection with weak supervision using transliteration alignment”, PLoS ONE, vol. 15, no. 12, 2020.

N. Ufer and Ommer, B., “Deep Semantic Feature Matching”, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.

article (8.88 MB)

A. Sanakoyeu, Bautista, M., and Ommer, B., “Deep Unsupervised Learning of Visual Similarities”, Pattern Recognition, vol. 78, 2018.

PDF (8.35 MB)

M. Bautista, Sanakoyeu, A., and Ommer, B., “Deep Unsupervised Similarity Learning using Partially Ordered Sets”, in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.

deep_unsupervised_similarity_learning_cvpr_2017_paper.pdf (905.82 KB)

J. Schlecht, Carque, B., and Ommer, B., “Detecting Gestures in Medieval Images”, in Proceedings of the International Conference on Image Processing, 2011, p. 1309--1312.

Technical Report (1.61 MB)

P. Bell and Ommer, B., “Digital Connoisseur? How Computer Vision Supports Art History”, in Connoisseurship nel XXI secolo. Approcci, Limiti, Prospettive, A. Aggujaro & S. Albl (ed.), Rome: Artemide, 2016.

P. Esser, Rombach, R., and Ommer, B., “A Disentangling Invertible Interpretation Network for Explaining Latent Representations”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2020.

Article (13.07 MB)

T. Milbich, Roth, K., Bharadhwaj, H., Sinha, S., Bengio, Y., Ommer, B., and Cohen, J. Paul, “DiVA: Diverse Visual Feature Aggregation for Deep Metric Learning”, IEEE European Conference on Computer Vision (ECCV). 2020.

A. Sanakoyeu, Tschernezki, V., Büchler, U., and Ommer, B., “Divide and Conquer the Embedding Space for Metric Learning”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2019.

E

A. - S. Wahl, Erlebach, E., Brattoli, B., Büchler, U., Kaiser, J., Ineichen, V. B., Mosberger, A. C., Schneeberger, S., Imobersteg, S., Wieckhorst, M., Stirn, M., Schroeter, A., Ommer, B., and Schwab, M. E., “Early reduced behavioral activity induced by large strokes affects the efficiency of enriched environment in rats”, Sage Journals, vol. Journal of Cerebral Blood Flow & Metabolism, 2018.

0271678x18777661.pdf (770.87 KB)

J. Wagner and Ommer, B., “Efficiently Clustering Earth Mover's Distance”, in Proceedins of the Aian Conference on Computer Vision, 2010, p. 477--488.

Technical Report (841.98 KB)

M. Kandemir, Rubio, J. C., Schmidt, U., Welbl, J., Ommer, B., and Hamprecht, F. A., “Event Detection by Feature Unpredictability in Phase-Contrast Videos of Cell Cultures”, in MICCAI. Proceedings, 2014, pp. 154-161.

Paper (2 MB)

M. Kandemir, Rubio, J. C., Schmidt, U., Wojek, C., Welbl, J., Ommer, B., and Hamprecht, F. A., “Event Detection by Feature Unpredictability in Phase-Contrast Videos of Cell Cultures”, in Medical Image Computing and Computer-Assisted Intervention, 2014, p. 154--161.

Technical Report (2 MB)

V. Roth and Ommer, B., “Exploiting Low-level Image Segmentation for Object Recognition”, in Pattern Recognition, Symposium of the DAGM, 2006, vol. 4174, p. 11--20.

Technical Report (473.84 KB)

F

P. Yarlagadda and Ommer, B., “From Meaningful Contours to Discriminative Object Shape”, in Proceedings of the European Conference on Computer Vision, 2012, vol. 7572, p. 766--779.

Technical Report (4.58 MB)

G

J. C. Rubio, Eigenstetter, A., and Ommer, B., “Generative Regularization with Latent Topics for Discriminative Object Recognition”, Pattern Recognition, vol. 48, p. 3871--3880, 2015.

Technical Report (5.49 MB)

R. Rombach, Esser, P., and Ommer, B., “Geometry-Free View Synthesis: Transformers and no 3D Priors”, in Proceedings of the Intl. Conf. on Computer Vision (ICCV), 2021.

Pages