Publications

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Conference Paper
Zhang, C, Huber, F, Knop, M and Hamprecht, F A (2014). Yeast Cell Detection and Segmentation in Bright Field Microscopy. ISBI. Proceedings. IEEE Computer Society. 1267-1270PDF icon Technical Report (4.13 MB)
Straehle, C N, Köthe, U and Hamprecht, F A (2013). Weakly supervised learning of image partitioning using decision trees with structured split criteria. ICCV 2013. Proceedings. 1849-1856PDF icon Technical Report (5.97 MB)
Fiaschi, L, Diego, F, Grosser, K - H, Schiegg, M, Köthe, U, Zlatic, M and Hamprecht, F A (2014). Tracking indistinguishable translucent objects over time using weakly supervised structured learning. CVPR. Proceedings. 2736 - 2743PDF icon Technical Report (1.47 MB)
Rapp, H, Frank, M, Hamprecht, F A and Jähne, B (2007). A theoretical and experimental investigation of the systematic errors and statistical uncertainties of time-of-flight cameras. Proc.\ Dyn3D Workshop, Heidelberg, Sept. 11, 2007. ZESS, Univ.\ Siegen
Lou, X and Hamprecht, F A (2011). Structured Learning for Cell Tracking. NIPS 2011. Proceedings. 1296-1304PDF icon Technical Report (1.41 MB)
Diego, F and Hamprecht, F A (2014). Sparse Space-Time Deconvolution for Calcium Image Analysis. NIPS. Proceedings. 64-72. http://papers.nips.cc/paper/5342-sparse-space-time-deconvolution-for-calcium-image-analysisPDF icon Technical Report (5.27 MB)
Görlitz, L, Menze, B H, Weber, M - A and Kelm, B Michael (2007). Semi-Supervised Tumor Detection in MRSI With Discriminative Random Fields. Pattern Recognition. Springer. 4713 224-233PDF icon Technical Report (872.46 KB)
Görlitz, L, Menze, B H, Weber, M - A and Kelm, B Michael (2007). Semi-Supervised Tumor Detection in MRSI With Discriminative Random Fields. Pattern Recognition. Springer. 4713 224-233PDF icon Technical Report (872.46 KB)
Drory, A, Haubold, C, Avidan, S and Hamprecht, F A (2014). Semi-Global Matching: A Principled Derivation in Terms of Message Passing. GCPR. Proceedings. 43-53PDF icon Technical Report (2.6 MB)
Andres, B, Hamprecht, F A and Garbe, C S (2007). Selection of Local Optical Flow Models by Means of Residual Analysis. Pattern Recognition. Springer. 4713 72-81PDF icon Technical Report (229.64 KB)
Andres, B, Garbe, C S, Schnörr, C and Jähne, B (2007). Selection of local optical flow models by means of residual analysis. Proceedings of the 29th DAGM Symposium on Pattern Recognition. Springer. 72--81
Andres, B, Hamprecht, F A and Garbe, C S (2007). Selection of Local Optical Flow Models by Means of Residual Analysis. Pattern Recognition. Springer. 4713 72-81PDF icon Technical Report (229.64 KB)
Andres, B, Garbe, C S, Schnörr, C and Jähne, B (2007). Selection of local optical flow models by means of residual analysis. Proceedings of the 29th DAGM Symposium on Pattern Recognition. Springer. 72--81
Andres, B, Köthe, U, Helmstaedter, M, Denk, W and Hamprecht, F A (2008). Segmentation of SBFSEM Volume Data of Neural Tissue by Hierarchical Classification. Pattern Recognition. 30th DAGM Symposium Munich, Germany, June 10-13, 2008. Proceedings. Springer. 5096 142-152PDF icon Technical Report (1.21 MB)
Straehle, C N, Köthe, U, Briggman, K, Denk, W and Hamprecht, F A (2012). Seeded watershed cut uncertainty estimators for guided interactive segmentation. CVPR 2012. Proceedings. 765 - 772PDF icon Technical Report (2.84 MB)
Zhang, H, Hamprecht, F A and Amann, A (2005). Report about VOCs Dataset's Analysis based on Random Forests. Proceedings of the HPC-Asia05. IEEE Computer Society Press. 603-607PDF icon Technical Report (232.13 KB)
Andres, B, Köthe, U, Bonea, A, Nadler, B and Hamprecht, F A (2009). Quantitative Assessment of Image Segmentation Quality by Random Walk Relaxation Times. Pattern Recognition. 31st DAGM Symposium, Jena, Germany, September 9-11, 2009. Proceedings. Springer. 5748 502-511PDF icon Technical Report (3.08 MB)
Schiegg, M, Heuer, B, Haubold, C, Wolf, S, Köthe, U and Hamprecht, F A (2015). Proof-reading Guidance in Cell Tracking by Sampling from Tracking-by-assignment Models. ISBI. Proceedings. 394-398PDF icon Technical Report (648.55 KB)
Andres, B, Kappes, J H, Beier, T, Köthe, U and Hamprecht, F A (2011). Probabilistic Image Segmentation with Closedness Constraints. ICCV, Proceedings. 2611 - 2618PDF icon Technical Report (8.18 MB)
Andres, B, Kappes, J H, Beier, T, Köthe, U and Hamprecht, F A (2011). Probabilistic Image Segmentation with Closedness Constraints. Proceedings of ICCVPDF icon Technical Report (2.95 MB)
Yarkony, J, Beier, T, Baldi, P and Hamprecht, F A (2014). Parallel Multicut Segmentation via Dual Decomposition. New Frontiers in Mining Complex Patterns - Third International Workshop, {NFMCP} 2014, Held in Conjunction with {ECML-PKDD} 2014, Nancy, France, September 19, 2014, Revised Selected Papers. http://dx.doi.org/10.1007/978-3-319-17876-9_4
Menze, B H, Kelm, B Michael, Splitthoff, N, Köthe, U and Hamprecht, F A (2011). On oblique random forests. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2011. Proceedings. Springer. 453-469PDF icon Technical Report (665.33 KB)
Kaster, F O, Kassemeyer, S, Merkel, B, Nix, O and Hamprecht, F A (2010). An object-oriented library for systematic training and comparison of classifiers for computer-assisted tumor diagnosis from MRSI measurements. Bildverarbeitung für die Medizin 2010 -- Algorithmen, Systeme, Anwendungen. Springer. 97-101PDF icon Technical Report (1.12 MB)
Hanselmann, M, Köthe, U, Renard, B Y, Kirchner, M, Heeren, R M A and Hamprecht, F A (2009). Multivariate Watershed Segmentation of Compositional Data. Proceedings of the 15th International Conference on Discrete Geometry for Computer Imagery (DGCI), in press. Springer. 5810 180-192PDF icon Technical Report (1.25 MB)
Straehle, C N, Kandemir, M, Köthe, U and Hamprecht, F A (2014). Multiple instance learning with response-optimized random forests. ICPR. Proceedings. 3768 - 3773PDF icon Technical Report (296.66 KB)
Urban, G, Bendszus, M, Hamprecht, F A and Kleesiek, J (2014). Multi-modal Brain Tumor Segmentation using Deep Convolutional NeuralNetworks. MICCAI BraTS (Brain Tumor Segmentation) Challenge. Proceedings, winningcontribution. 31-35
Menze, B H, Kelm, B Michael, Heck, D, Lichy, M P and Hamprecht, F A (2006). Machine-based rejection of low quality spectra and estimation of brain tumor probabilities from magnetic resonance spectroscopic images. Bildverarbeitung für die Medizin. 31-36PDF icon Technical Report (672.84 KB)
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 268-275PDF icon Technical Report (2.92 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 419-427PDF icon Technical Report (2.87 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 AbstractsPDF icon Technical Report (1.05 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-representationsPDF icon Technical Report (2.79 MB)
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. ECCV 2012PDF icon Technical Report (532.64 KB)
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_12PDF icon Technical Report (446.28 KB)
Straehle, C N, Peter, S, Köthe, U and Hamprecht, F A (2013). K-smallest Spanning Tree Segmentations. German Conference on Pattern Recognition (DAGM/GCPR). Proceedings. Springer. 375-384PDF icon Technical Report (1.18 MB)

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