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F

Börner, K, Hermle, J, Sommer, C, Brown, N P, Knapp, B, Glass, B, Torralba, G, Reymann, J, Beil, N, Beneke, J, Pepperkok, R, Schneider, R and Ludwig, T (2010). From experimental setup to bioinformatics: An RNAi screening platform to identify host factors involved in HIV-1 replication. Biotechnology Journal. 5 39-49

Technical Report (556.09 KB)

Yarlagadda, P and Ommer, B (2012). From Meaningful Contours to Discriminative Object Shape. Proceedings of the European Conference on Computer Vision. Springer. 7572 766--779

Technical Report (4.58 MB)

Beier, T, Hamprecht, F A and Kappes, J H (2015). Fusion Moves for Correlation Clustering. CVPR. Proceedings. 3507-3516

Technical Report (1.19 MB)

G

Rubio, J C, Eigenstetter, A and Ommer, B (2015). Generative Regularization with Latent Topics for Discriminative Object Recognition. Pattern Recognition. Elsevier. 48 3871--3880

Technical Report (5.49 MB)

Köthe, U, Andres, B, Kröger, T and Hamprecht, F A (2010). Geometric Analysis of 3D Electron Microscopy Data. Proceedings of Workshop on Discrete Geometry and Mathematical Morphology (WADGMM). 22-26

Technical Report (1.43 MB)

Andres, B, Kröger, T, Briggmann, K L, Denk, W, Norogod, N, Knott, G W, Köthe, U and Hamprecht, F A (2012). Globally Optimal Closed-Surface Segmentation for Connectomics. ECCV 2012. Proceedings, Part 3. 778-791

Technical Report (2.72 MB)

Kappes, J H, Speth, M, Andres, B, Reinelt, G and Schnörr, C (2011). Globally Optimal Image Partitioning by Multicuts. EMMCVPR. Springer. 31-44

Technical Report (7.3 MB)

Beier, T (2014). Graph Based Image Analysis. University of Heidelberg

H

Andres, B, Köthe, U, Kröger, T and Hamprecht, F A (2010). How to Extract the Geometry and Topology from Very Large 3D Segmentations. ArXiv e-prints. http://arxiv.org/abs/1009.6215

Technical Report (1.44 MB)

I

Kleesiek, J, Biller, A, Urban, G, Köthe, U, Bendszus, M and Hamprecht, F A (2014). ilastik for Multi-modal Brain Tumor Segmentation. MICCAI BraTS (Brain Tumor Segmentation) Challenge. Proceedings, 3rdplace. 12-17

Technical Report (405.91 KB)

Kaster, F O (2011). Image Analysis for the Life Sciences - Computer-assisted Tumor Diagnostics and Digital Embryomics. University of Heidelberg

Frank, M and Hamprecht, F A (2011). Image-Based Supervision of a Periodically Working Machine. Pattern Analysis and Applications. 1-10

Technical Report (466.61 KB)

Krasowski, N, Beier, T, Knott, G W, Köthe, U, Hamprecht, F A and Kreshuk, A (2015). Improving 3D EM Data Segmentation by Joint Optimization over Boundary Evidence and Biological Priors. 12th {IEEE} International Symposium on Biomedical Imaging, {ISBI} 2015, Brooklyn, NY, USA, April 16-19, 2015. 536-539

Technical Report (2.25 MB)

Blumenthal, F (2014). Information-Geometric Optimization For Image Segmentation. University of Heidelberg

Kandemir, M and Hamprecht, F A (2014). Instance Label Prediction by Dirichlet Process Multiple Instance Learning. UAI. Proceedings

Technical Report (4.26 MB)

Frank, M (2007). Investigation Of A 3D Camera. University of Heidelberg

J

Diego, F, Serrat, J and López, A M (2012). Joint SpatioTemporal Alignment of Sequences. IEEE Transactions on Multimedia (TMM). PP (99) 1

Technical Report (5.15 MB)

K

Fiaschi, L, Grosser, K - H, Afonso, B, Zlatic, M and Hamprecht, F A (2013). Keeping Count: Leveraging Temporal Context to Count Heavily Overlapping Objects. ISBI 2013.Proceedings. 656-659

Technical Report (711.68 KB)

L

Andres, B, Kappes, J H, Beier, T, Köthe, U and Hamprecht, F A (2012). The Lazy Flipper: Efficient Depth-Limited Exhaustive Search in Discrete Graphical Models. Computer Vision - {ECCV} 2012 - 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part {VII}. http://dx.doi.org/10.1007/978-3-642-33786-4_12

Technical Report (446.28 KB)

Andres, B, Kappes, J H, Köthe, U and Hamprecht, F A (2010). The Lazy Flipper: MAP Inference in Higher-Order Graphical Models by Depth-limited Exhaustive Search. ArXiv e-prints. http://arxiv.org/abs/1009.4102

Technical Report (625.06 KB)

Ommer, B and Buhmann, J M (2006). Learning Compositional Categorization Models. Proceedings of the European Conference on Computer Vision. Springer. 3953 316--329

Technical Report (1.35 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 (2014). Learning Latent Constituents for Recognition of Group Activities in Video. Proceedings of the European Conference on Computer Vision (ECCV) (Oral). Springer. 33--47

Technical Report (4.54 MB)

Diego, F and Hamprecht, F A (2013). Learning Multi-Level Sparse Representation. NIPS. Proceedings. http://papers.nips.cc/paper/5076-learning-multi-level-sparse-representations

Technical Report (2.79 MB)

Diego, F and Hamprecht, F A (2013). Learning Multi-Level Sparse Representation for Identifying Neuronal Activity. Signal Processing with Adaptive Sparse Structured Representations Workshop (SPARS). Book of Abstracts

Technical Report (1.05 MB)

Jehle, M, Sommer, C and Jähne, B (2010). Learning of Optimal Illumination for Material Classification. Proceedings of the 32nd DAGM Symposium on Pattern Recognition, Darmstadt, Germany. Springer. 563-572

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--516

Technical Report (2.78 MB)

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--8

Technical Report (2.78 MB)

Fiaschi, L, Nair, R, Köthe, U and Hamprecht, F A (2012). Learning to Count with Regression Forest and Structured Labels. ICPR 2012. Proceedings. 2685-2688

Technical Report (3.66 MB)

Kröger, T, Mikula, S, Denk, W, Köthe, U and Hamprecht, F A (2013). Learning to Segment Neurons with Non-local Quality Measures. MICCAI 2013. Proceedings, part II. Springer. 8150 419-427

Technical Report (2.87 MB)

Funke, J, Hamprecht, F A and Zhang, C (2015). Learning to Segment: Training Hierarchical Segmentation under a Topological Loss. MICCAI. Proceedings, Part III. Springer. 9351 268-275

Technical Report (2.92 MB)

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--194

Technical Report (358.98 KB)

Kröger, (2014). Learning-based Segmentation for Connectomics. University of Heidelberg

Antic, B, Milbich, T and Ommer, B (2013). Less is More: Video Trimming for Action Recognition. Proceedings of the IEEE International Conference on Computer Vision, Workshop on Understanding Human Activities: Context and Interaction. IEEE. 515--521

Technical Report (984.89 KB)

M

Lindner, M (2011). A Machine Learning Approach To Improve Digital Embryo Analysis. University of Heidelberg

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