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ORCIDFred A. Hamprecht
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- affiliation: University of Heidelberg, Interdisciplinary Center for Scientific Computing, Germany
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2020 – today
- 2024
[c101]Moritz Sturm, Lorenzo Cerrone, Fred A. Hamprecht:
SynCellFactory: Generative Data Augmentation for Cell Tracking. MICCAI (12) 2024: 304-313- [i37]Enrique Fita Sanmartín, Christoph Schnörr, Fred A. Hamprecht:
The Central Spanning Tree Problem. CoRR abs/2404.06447 (2024) - [i36]Moritz Sturm, Lorenzo Cerrone, Fred A. Hamprecht:
SynCellFactory: Generative Data Augmentation for Cell Tracking. CoRR abs/2404.16421 (2024) - [i35]Peter Lippmann, Gerrit Gerhartz, Roman Remme, Fred A. Hamprecht:
Tensor Frames - How To Make Any Message Passing Network Equivariant. CoRR abs/2405.15389 (2024) - [i34]Lennart Bürger, Fred A. Hamprecht, Boaz Nadler:
Truth is Universal: Robust Detection of Lies in LLMs. CoRR abs/2407.12831 (2024) - [i33]Enrique Fita Sanmartín, Christoph Schnörr, Fred A. Hamprecht:
Expectation and Variance of the Degree of a Node in Random Spanning Trees. CoRR abs/2409.13472 (2024) - [i32]Dmitry Kobak, Fred A. Hamprecht, Smita Krishnaswamy, Gal Mishne, Sebastian Damrich:
Low-Dimensional Embeddings of High-Dimensional Data: Algorithms and Applications (Dagstuhl Seminar 24122). Dagstuhl Reports 14(3): 92-115 (2024) - 2023
- [c100]Sebastian Damrich, Jan Niklas Böhm, Fred A. Hamprecht, Dmitry Kobak:
From $t$-SNE to UMAP with contrastive learning. ICLR 2023 - [c99]Philipp Nazari, Sebastian Damrich, Fred A. Hamprecht:
Geometric Autoencoders - What You See is What You Decode. ICML 2023: 25834-25857 - [i31]Roman Remme, Tobias Kaczun, Maximilian Scheurer, Andreas Dreuw, Fred A. Hamprecht:
KineticNet: Deep learning a transferable kinetic energy functional for orbital-free density functional theory. CoRR abs/2305.13316 (2023) - [i30]Philipp Nazari, Sebastian Damrich, Fred A. Hamprecht:
Geometric Autoencoders - What You See is What You Decode. CoRR abs/2306.17638 (2023) - 2022
- [j26]Quentin Garrido, Sebastian Damrich, Alexander Jäger, Dario Cerletti, Manfred Claassen, Laurent Najman, Fred A. Hamprecht:
Visualizing hierarchies in scRNA-seq data using a density tree-biased autoencoder. Bioinform. 38(Supplement_1): i316-i324 (2022) - [c98]Alberto Bailoni, Constantin Pape, Nathan Hütsch, Steffen Wolf, Thorsten Beier, Anna Kreshuk, Fred A. Hamprecht:
GASP, a generalized framework for agglomerative clustering of signed graphs and its application to Instance Segmentation. CVPR 2022: 11635-11645 - [c97]Lorenzo Cerrone, Athul Vijayan, Tejasvinee Mody
, Kay Schneitz, Fred A. Hamprecht:
CellTypeGraph: A New Geometric Computer Vision Benchmark. CVPR 2022: 20865-20875 - [c96]Enrique Fita Sanmartín, Sebastian Damrich, Fred A. Hamprecht:
The Algebraic Path Problem for Graph Metrics. ICML 2022: 19178-19204 - [c95]Peter Lippmann, Enrique Fita Sanmartín, Fred A. Hamprecht:
Theory and Approximate Solvers for Branched Optimal Transport with Multiple Sources. NeurIPS 2022 - [i29]Lorenzo Cerrone, Athul Vijayan, Tejasvinee Mody, Kay Schneitz, Fred A. Hamprecht:
CellTypeGraph: A New Geometric Computer Vision Benchmark. CoRR abs/2205.08166 (2022) - [i28]Sebastian Damrich, Jan Niklas Böhm, Fred A. Hamprecht, Dmitry Kobak:
Contrastive learning unifies t-SNE and UMAP. CoRR abs/2206.01816 (2022) - [i27]Peter Lippmann, Enrique Fita Sanmartín, Fred A. Hamprecht:
Theory and Approximate Solvers for Branched Optimal Transport with Multiple Sources. CoRR abs/2210.07702 (2022) - 2021
- [j25]Steffen Wolf, Alberto Bailoni, Constantin Pape, Nasim Rahaman, Anna Kreshuk, Ullrich Köthe, Fred A. Hamprecht:
The Mutex Watershed and its Objective: Efficient, Parameter-Free Graph Partitioning. IEEE Trans. Pattern Anal. Mach. Intell. 43(10): 3724-3738 (2021) - [c94]Erik Jenner, Enrique Fita Sanmartín, Fred A. Hamprecht:
Extensions of Karger's Algorithm: Why They Fail in Theory and How They Are Useful in Practice. ICCV 2021: 4582-4591 - [c93]Florin C. Walter, Sebastian Damrich, Fred A. Hamprecht:
Multistar: Instance Segmentation Of Overlapping Objects With Star-Convex Polygons. ISBI 2021: 295-298 - [c92]Sebastian Damrich, Fred A. Hamprecht:
On UMAP's True Loss Function. NeurIPS 2021: 5798-5809 - [c91]Enrique Fita Sanmartin, Sebastian Damrich, Fred A. Hamprecht:
Directed Probabilistic Watershed. NeurIPS 2021: 20076-20088 - [i26]Quentin Garrido, Sebastian Damrich, Alexander Jäger, Dario Cerletti, Manfred Claassen, Laurent Najman, Fred A. Hamprecht:
Visualizing hierarchies in scRNA-seq data using a density tree-biased autoencoder. CoRR abs/2102.05892 (2021) - [i25]Sebastian Damrich, Fred A. Hamprecht:
UMAP does not reproduce high-dimensional similarities due to negative sampling. CoRR abs/2103.14608 (2021) - [i24]Erik Jenner, Enrique Fita Sanmartín, Fred A. Hamprecht:
Extensions of Karger's Algorithm: Why They Fail in Theory and How They Are Useful in Practice. CoRR abs/2110.02750 (2021) - 2020
- [j24]Thomas M. Hehn, Julian F. P. Kooij, Fred A. Hamprecht:
End-to-End Learning of Decision Trees and Forests. Int. J. Comput. Vis. 128(4): 997-1011 (2020) - [c90]Steffen Wolf, Fred A. Hamprecht, Jan Funke:
Inpainting Networks Learn to Separate Cells in Microscopy Images. BMVC 2020 - [c89]Alberto Bailoni, Constantin Pape, Steffen Wolf, Anna Kreshuk, Fred A. Hamprecht:
Proposal-Free Volumetric Instance Segmentation from Latent Single-Instance Masks. GCPR 2020: 331-344 - [c88]Steffen Wolf, Yuyan Li, Constantin Pape, Alberto Bailoni, Anna Kreshuk, Fred A. Hamprecht:
The Semantic Mutex Watershed for Efficient Bottom-Up Semantic Instance Segmentation. ECCV (6) 2020: 208-224 - [c87]Elke Kirschbaum, Alberto Bailoni, Fred A. Hamprecht:
DISCo: Deep Learning, Instance Segmentation, and Correlations for Cell Segmentation in Calcium Imaging. MICCAI (5) 2020: 151-162 - [i23]Steffen Wolf, Fred A. Hamprecht, Jan Funke:
Instance Separation Emerges from Inpainting. CoRR abs/2003.00891 (2020) - [i22]Alberto Bailoni, Constantin Pape, Steffen Wolf, Anna Kreshuk, Fred A. Hamprecht:
Proposal-Free Volumetric Instance Segmentation from Latent Single-Instance Masks. CoRR abs/2009.04998 (2020) - [i21]Florin C. Walter, Sebastian Damrich, Fred A. Hamprecht:
MultiStar: Instance Segmentation of Overlapping Objects with Star-Convex Polygons. CoRR abs/2011.13228 (2020)
2010 – 2019
- 2019
- [c86]Lorenzo Cerrone, Alexander Zeilmann, Fred A. Hamprecht:
End-To-End Learned Random Walker for Seeded Image Segmentation. CVPR 2019: 12559-12568 - [c85]Elke Kirschbaum, Manuel Haußmann, Steffen Wolf, Hannah Sonntag, Justus Schneider, Shehabeldin Elzoheiry, Oliver Kann, Daniel Durstewitz, Fred A. Hamprecht:
LeMoNADe: Learned Motif and Neuronal Assembly Detection in calcium imaging videos. ICLR (Poster) 2019 - [c84]Nasim Rahaman, Aristide Baratin, Devansh Arpit, Felix Draxler, Min Lin, Fred A. Hamprecht, Yoshua Bengio, Aaron C. Courville:
On the Spectral Bias of Neural Networks. ICML 2019: 5301-5310 - [c83]Manuel Haußmann, Fred A. Hamprecht, Melih Kandemir:
Deep Active Learning with Adaptive Acquisition. IJCAI 2019: 2470-2476 - [c82]Enrique Fita Sanmartin, Sebastian Damrich, Fred A. Hamprecht:
Probabilistic Watershed: Sampling all spanning forests for seeded segmentation and semi-supervised learning. NeurIPS 2019: 2776-2787 - [c81]Manuel Haußmann, Fred A. Hamprecht, Melih Kandemir:
Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation. UAI 2019: 563-573 - [i20]Steffen Wolf, Alberto Bailoni, Constantin Pape, Nasim Rahaman, Anna Kreshuk, Ullrich Köthe, Fred A. Hamprecht:
The Mutex Watershed and its Objective: Efficient, Parameter-Free Image Partitioning. CoRR abs/1904.12654 (2019) - [i19]Lorenzo Cerrone, Alexander Zeilmann, Fred A. Hamprecht:
End-to-End Learned Random Walker for Seeded Image Segmentation. CoRR abs/1905.09045 (2019) - [i18]Manuel Haußmann, Fred A. Hamprecht, Melih Kandemir:
Deep Active Learning with Adaptive Acquisition. CoRR abs/1906.11471 (2019) - [i17]Alberto Bailoni, Constantin Pape, Steffen Wolf, Thorsten Beier, Anna Kreshuk, Fred A. Hamprecht:
A Generalized Framework for Agglomerative Clustering of Signed Graphs applied to Instance Segmentation. CoRR abs/1906.11713 (2019) - [i16]Elke Kirschbaum, Alberto Bailoni, Fred A. Hamprecht:
DISCo for the CIA: Deep learning, Instance Segmentation, and Correlations for Calcium Imaging Analysis. CoRR abs/1908.07957 (2019) - [i15]Enrique Fita Sanmartin, Sebastian Damrich, Fred A. Hamprecht:
Probabilistic Watershed: Sampling all spanning forests for seeded segmentation and semi-supervised learning. CoRR abs/1911.02921 (2019) - [i14]Steffen Wolf, Yuyan Li, Constantin Pape, Alberto Bailoni, Anna Kreshuk, Fred A. Hamprecht:
The Semantic Mutex Watershed for Efficient Bottom-Up Semantic Instance Segmentation. CoRR abs/1912.12717 (2019) - 2018
- [j23]Virginie Uhlmann, Carsten Haubold, Fred A. Hamprecht, Michael Unser:
DiversePathsJ: diverse shortest paths for bioimage analysis. Bioinform. 34(3): 538-540 (2018) - [j22]Nikola Krasowski, Thorsten Beier, Graham Knott, Ullrich Köthe, Fred A. Hamprecht, Anna Kreshuk:
Neuron Segmentation With High-Level Biological Priors. IEEE Trans. Medical Imaging 37(4): 829-839 (2018) - [c80]Maurice Weiler, Fred A. Hamprecht, Martin Storath:
Learning Steerable Filters for Rotation Equivariant CNNs. CVPR 2018: 849-858 - [c79]Thomas M. Hehn, Fred A. Hamprecht:
End-to-End Learning of Deterministic Decision Trees. GCPR 2018: 612-627 - [c78]Steffen Wolf, Constantin Pape, Alberto Bailoni, Nasim Rahaman, Anna Kreshuk, Ullrich Köthe, Fred A. Hamprecht:
The Mutex Watershed: Efficient, Parameter-Free Image Partitioning. ECCV (4) 2018: 571-587 - [c77]Felix Draxler, Kambis Veschgini, Manfred Salmhofer, Fred A. Hamprecht:
Essentially No Barriers in Neural Network Energy Landscape. ICML 2018: 1308-1317 - [i13]Felix Draxler, Kambis Veschgini, Manfred Salmhofer, Fred A. Hamprecht:
Essentially No Barriers in Neural Network Energy Landscape. CoRR abs/1803.00885 (2018) - [i12]Carsten Haubold, Virginie Uhlmann, Michael Unser, Fred A. Hamprecht:
Diverse M-Best Solutions by Dynamic Programming. CoRR abs/1803.05748 (2018) - [i11]Melih Kandemir, Manuel Haußmann, Fred A. Hamprecht:
Sampling-Free Variational Inference of Bayesian Neural Nets. CoRR abs/1805.07654 (2018) - [i10]Nasim Rahaman, Devansh Arpit, Aristide Baratin, Felix Draxler, Min Lin, Fred A. Hamprecht, Yoshua Bengio, Aaron C. Courville:
On the Spectral Bias of Deep Neural Networks. CoRR abs/1806.08734 (2018) - 2017
- [c76]Manuel Haußmann, Fred A. Hamprecht, Melih Kandemir:
Variational Bayesian Multiple Instance Learning with Gaussian Processes. CVPR 2017: 810-819 - [c75]Carsten Haubold, Virginie Uhlmann, Michael Unser, Fred A. Hamprecht:
Diverse M-Best Solutions by Dynamic Programming. GCPR 2017: 255-267 - [c74]Steffen Wolf, Lukas Schott, Ullrich Köthe, Fred A. Hamprecht:
Learned Watershed: End-to-End Learning of Seeded Segmentation. ICCV 2017: 2030-2038 - [c73]Sven Peter, Ferran Diego, Fred A. Hamprecht, Boaz Nadler:
Cost efficient gradient boosting. NIPS 2017: 1551-1561 - [c72]Sven Peter, Elke Kirschbaum, Martin Both, Lee Campbell, Brandon Harvey, Conor Heins, Daniel Durstewitz, Ferran Diego, Fred A. Hamprecht:
Sparse convolutional coding for neuronal assembly detection. NIPS 2017: 3675-3685 - [i9]Steffen Wolf, Lukas Schott, Ullrich Köthe, Fred A. Hamprecht:
Learned Watershed: End-to-End Learning of Seeded Segmentation. CoRR abs/1704.02249 (2017) - [i8]Maurice Weiler, Fred A. Hamprecht, Martin Storath:
Learning Steerable Filters for Rotation Equivariant CNNs. CoRR abs/1711.07289 (2017) - [i7]Thomas Hehn, Fred A. Hamprecht:
End-to-end Learning of Deterministic Decision Trees. CoRR abs/1712.02743 (2017) - 2016
- [c71]Melih Kandemir, Manuel Haußmann, Ferran Diego, Kumar T. Rajamani, Jeroen van der Laak, Fred A. Hamprecht:
Variational Weakly Supervised Gaussian Processes. BMVC 2016 - [c70]Ferran Diego, Fred A. Hamprecht:
Structured Regression Gradient Boosting. CVPR 2016: 1459-1467 - [c69]Matthias von Borstel, Melih Kandemir, Philip Schmidt, Madhavi K. Rao, Kumar T. Rajamani, Fred A. Hamprecht:
Gaussian Process Density Counting from Weak Supervision. ECCV (1) 2016: 365-380 - [c68]Carsten Haubold, Janez Ales, Steffen Wolf, Fred A. Hamprecht:
A Generalized Successive Shortest Paths Solver for Tracking Dividing Targets. ECCV (7) 2016: 566-582 - [c67]Martin Schiegg, Ferran Diego, Fred A. Hamprecht:
Learning Diverse Models: The Coulomb Structured Support Vector Machine. ECCV (3) 2016: 585-599 - [c66]Thorsten Beier, Björn Andres, Ullrich Köthe, Fred A. Hamprecht:
An Efficient Fusion Move Algorithm for the Minimum Cost Lifted Multicut Problem. ECCV (2) 2016: 715-730 - 2015
- [j21]Martin Schiegg, Philipp Hanslovsky, Carsten Haubold, Ullrich Köthe, Lars Hufnagel, Fred A. Hamprecht:
Graphical model for joint segmentation and tracking of multiple dividing cells. Bioinform. 31(6): 948-956 (2015) - [j20]Melih Kandemir, Fred A. Hamprecht:
Computer-aided diagnosis from weak supervision: A benchmarking study. Comput. Medical Imaging Graph. 42: 44-50 (2015) - [j19]Jörg H. Kappes, Björn Andres, Fred A. Hamprecht, Christoph Schnörr, Sebastian Nowozin, Dhruv Batra, Sungwoong Kim, Bernhard X. Kausler, Thorben Kröger, Jan Lellmann, Nikos Komodakis, Bogdan Savchynskyy, Carsten Rother:
A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems. Int. J. Comput. Vis. 115(2): 155-184 (2015) - [c65]Thorsten Beier, Fred A. Hamprecht, Jörg H. Kappes:
Fusion moves for correlation clustering. CVPR 2015: 3507-3516 - [c64]Martin Schiegg, Ben Heuer, Carsten Haubold, Steffen Wolf, Ullrich Köthe, Fred A. Hamprecht:
Proof-reading guidance in cell tracking by sampling from tracking-by-assignment models. ISBI 2015: 394-398 - [c63]Nikola Krasowski, Thorsten Beier, Graham W. Knott, Ullrich Köthe, Fred A. Hamprecht, Anna Kreshuk:
Improving 3D EM data segmentation by joint optimization over boundary evidence and biological priors. ISBI 2015: 536-539 - [c62]Jan Funke, Fred A. Hamprecht, Chong Zhang:
Learning to Segment: Training Hierarchical Segmentation under a Topological Loss. MICCAI (3) 2015: 268-275 - [c61]Melih Kandemir, Christian Wojek, Fred A. Hamprecht:
Cell Event Detection in Phase-Contrast Microscopy Sequences from Few Annotations. MICCAI (3) 2015: 316-323 - [c60]Anna Kreshuk, Jan Funke, Albert Cardona, Fred A. Hamprecht:
Who Is Talking to Whom: Synaptic Partner Detection in Anisotropic Volumes of Insect Brain. MICCAI (1) 2015: 661-668 - [c59]Melih Kandemir, Fred A. Hamprecht:
The Deep Feed-Forward Gaussian Process: An Effective Generalization to Covariance Priors. FE@NIPS 2015: 145-159 - 2014
- [j18]Xinghua Lou, Martin Schiegg, Fred A. Hamprecht:
Active Structured Learning for Cell Tracking: Algorithm, Framework, and Usability. IEEE Trans. Medical Imaging 33(4): 849-860 (2014) - [c58]Thorsten Beier, Thorben Kröger, Jörg H. Kappes, Ullrich Köthe, Fred A. Hamprecht:
Cut, Glue, & Cut: A Fast, Approximate Solver for Multicut Partitioning. CVPR 2014: 73-80 - [c57]Luca Fiaschi, Ferran Diego Andilla, Konstantin Gregor, Martin Schiegg, Ullrich Köthe, Marta Zlatic, Fred A. Hamprecht:
Tracking Indistinguishable Translucent Objects over Time Using Weakly Supervised Structured Learning. CVPR 2014: 2736-2743 - [c56]Amnon Drory, Carsten Haubold, Shai Avidan, Fred A. Hamprecht:
Semi-Global Matching: A Principled Derivation in Terms of Message Passing. GCPR 2014: 43-53 - [c55]Thorben Kröger, Jörg H. Kappes, Thorsten Beier, Ullrich Köthe, Fred A. Hamprecht:
Asymmetric Cuts: Joint Image Labeling and Partitioning. GCPR 2014: 199-211 - [c54]Christoph N. Straehle, Melih Kandemir, Ullrich Köthe, Fred A. Hamprecht:
Multiple Instance Learning with Response-Optimized Random Forests. ICPR 2014: 3768-3773 - [c53]Faik Boray Tek, Thorben Kröger, Shawn Mikula, Fred A. Hamprecht:
Automated cell nucleus detection for large-volume electron microscopy of neural tissue. ISBI 2014: 69-72 - [c52]Chong Zhang, Florian Huber, Michael Knop, Fred A. Hamprecht:
Yeast cell detection and segmentation in bright field microscopy. ISBI 2014: 1267-1270 - [c51]Melih Kandemir, Annette Feuchtinger, Axel Walch, Fred A. Hamprecht:
Digital pathology: Multiple instance learning can detect Barrett's cancer. ISBI 2014: 1348-1351 - [c50]Chong Zhang, Julian Yarkony, Fred A. Hamprecht:
Cell Detection and Segmentation Using Correlation Clustering. MICCAI (1) 2014: 9-16 - [c49]Melih Kandemir, José C. Rubio, Ute Schmidt, Christian Wojek, Johannes Welbl, Björn Ommer, Fred A. Hamprecht:
Event Detection by Feature Unpredictability in Phase-Contrast Videos of Cell Cultures. MICCAI (2) 2014: 154-161 - [c48]Melih Kandemir, Chong Zhang, Fred A. Hamprecht:
Empowering Multiple Instance Histopathology Cancer Diagnosis by Cell Graphs. MICCAI (2) 2014: 228-235 - [c47]Ferran Diego Andilla, Fred A. Hamprecht:
Sparse Space-Time Deconvolution for Calcium Image Analysis. NIPS 2014: 64-72 - [c46]Julian Yarkony, Thorsten Beier, Pierre Baldi, Fred A. Hamprecht:
Parallel Multicut Segmentation via Dual Decomposition. NFMCP 2014: 56-68 - [c45]Melih Kandemir, Fred A. Hamprecht:
Instance Label Prediction by Dirichlet Process Multiple Instance Learning. UAI 2014: 380-389 - [i6]Jörg H. Kappes, Björn Andres, Fred A. Hamprecht, Christoph Schnörr, Sebastian Nowozin, Dhruv Batra, Sungwoong Kim, Bernhard X. Kausler, Thorben Kröger, Jan Lellmann, Nikos Komodakis, Bogdan Savchynskyy, Carsten Rother:
A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems. CoRR abs/1404.0533 (2014) - 2013
- [j17]Mario Frank, Fred A. Hamprecht:
Image-based supervision of a periodically working machine. Pattern Anal. Appl. 16(3): 407-416 (2013) - [c44]Jörg H. Kappes, Björn Andres, Fred A. Hamprecht, Christoph Schnörr, Sebastian Nowozin, Dhruv Batra, Sungwoong Kim, Bernhard X. Kausler, Jan Lellmann, Nikos Komodakis, Carsten Rother:
A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problems. CVPR 2013: 1328-1335 - [c43]Christoph N. Straehle, Sven Peter, Ullrich Köthe, Fred A. Hamprecht:
K-Smallest Spanning Tree Segmentations. GCPR 2013: 375-384 - [c42]Christoph N. Straehle, Ullrich Köthe, Fred A. Hamprecht:
Weakly Supervised Learning of Image Partitioning Using Decision Trees with Structured Split Criteria. ICCV 2013: 1849-1856 - [c41]Martin Schiegg, Philipp Hanslovsky, Bernhard X. Kausler, Lars Hufnagel, Fred A. Hamprecht:
Conservation Tracking. ICCV 2013: 2928-2935 - [c40]Luca Fiaschi, Gregor Konstantin, Bruno Afonso, Marta Zlatic, Fred A. Hamprecht:
Keeping count: Leveraging temporal context to count heavily overlapping objects. ISBI 2013: 656-659 - [c39]Ferran Diego Andilla, Susanne Reichinnek, Martin Both, Fred A. Hamprecht:
Automated identification of neuronal activity from calcium imaging by sparse dictionary learning. ISBI 2013: 1058-1061 - [c38]Thorben Kröger, Shawn Mikula, Winfried Denk, Ullrich Köthe, Fred A. Hamprecht:
Learning to Segment Neurons with Non-local Quality Measures. MICCAI (2) 2013: 419-427 - [c37]Ferran Diego Andilla, Fred A. Hamprecht:
Learning Multi-level Sparse Representations. NIPS 2013: 818-826 - 2012
- [j16]Björn Andres, Ullrich Köthe, Thorben Kröger, Moritz Helmstaedter, Kevin L. Briggman, Winfried Denk, Fred A. Hamprecht:
3D segmentation of SBFSEM images of neuropil by a graphical model over supervoxel boundaries. Medical Image Anal. 16(4): 796-805 (2012) - [c36]Christoph N. Straehle, Ullrich Köthe, Graham Knott, Kevin L. Briggman, Winfried Denk, Fred A. Hamprecht:
Seeded watershed cut uncertainty estimators for guided interactive segmentation. CVPR 2012: 765-772 - [c35]Jan Funke, Björn Andres, Fred A. Hamprecht, Albert Cardona, Matthew Cook:
Efficient automatic 3D-reconstruction of branching neurons from EM data. CVPR 2012: 1004-1011 - [c34]Xinghua Lou, Ullrich Köthe, Jochen Wittbrodt, Fred A. Hamprecht:
Learning to segment dense cell nuclei with shape prior. CVPR 2012: 1012-1018 - [c33]Bernhard X. Kausler, Martin Schiegg, Björn Andres, Martin S. Lindner, Ullrich Köthe, Heike Leitte, Jochen Wittbrodt, Lars Hufnagel, Fred A. Hamprecht:
A Discrete Chain Graph Model for 3d+t Cell Tracking with High Misdetection Robustness. ECCV (3) 2012: 144-157 - [c32]Björn Andres, Jörg H. Kappes, Thorsten Beier, Ullrich Köthe, Fred A. Hamprecht:
The Lazy Flipper: Efficient Depth-Limited Exhaustive Search in Discrete Graphical Models. ECCV (7) 2012: 154-166 - [c31]Björn Andres, Thorben Kröger, Kevin L. Briggman, Winfried Denk, Natalya Korogod, Graham Knott, Ullrich Köthe, Fred A. Hamprecht:
Globally Optimal Closed-Surface Segmentation for Connectomics. ECCV (3) 2012: 778-791 - [c30]Xinghua Lou, Fred A. Hamprecht:
Structured Learning from Partial Annotations. ICML 2012 - [c29]Christoph Sommer, Luca Fiaschi, Fred A. Hamprecht, Daniel Gerlich:
Learning-based mitotic cell detection in histopathological images. ICPR 2012: 2306-2309 - [c28]Luca Fiaschi, Ullrich Köthe, Rahul Nair, Fred A. Hamprecht:
Learning to count with regression forest and structured labels. ICPR 2012: 2685-2688 - [c27]Xinghua Lou, Luca Fiaschi, Ullrich Köthe, Fred A. Hamprecht:
Quality Classification of Microscopic Imagery with Weakly Supervised Learning. MLMI 2012: 176-183 - [c26]Jens Röder, Boaz Nadler, Kevin Kunzmann, Fred A. Hamprecht:
Active Learning with Distributional Estimates. UAI 2012: 715-725 - [i5]Jens Röder, Boaz Nadler, Kevin Kunzmann, Fred A. Hamprecht:
Active Learning with Distributional Estimates. CoRR abs/1210.4909 (2012) - 2011
- [j15]Björn Voss, Michael Hanselmann, Bernhard Y. Renard, Martin S. Lindner, Ullrich Köthe, Marc Kirchner, Fred A. Hamprecht:
SIMA: Simultaneous Multiple Alignment of LC/MS Peak Lists. Bioinform. 27(7): 987-993 (2011) - [j14]Frederik O. Kaster, Bernd Merkel, Oliver Nix, Fred A. Hamprecht:
An object-oriented library for systematic training and comparison of classifiers for computer-assisted tumor diagnosis from MRSI measurements. Comput. Sci. Res. Dev. 26(1-2): 65-85 (2011) - [c25]Björn Andres, Jörg H. Kappes, Thorsten Beier, Ullrich Köthe, Fred A. Hamprecht:
Probabilistic image segmentation with closedness constraints. ICCV 2011: 2611-2618 - [c24]Anna Kreshuk, Christoph N. Straehle, Christoph Sommer, Ullrich Köthe, Graham Knott, Fred A. Hamprecht:
Automated segmentation of synapses in 3D EM data. ISBI 2011: 220-223 - [c23]Christoph Sommer, Christoph N. Straehle, Ullrich Köthe, Fred A. Hamprecht:
Ilastik: Interactive learning and segmentation toolkit. ISBI 2011: 230-233 - [c22]Xinghua Lou, Frederik O. Kaster, Martin S. Lindner, Bernhard X. Kausler, Ullrich Köthe, Burkhard Hockendorf, Jochen Wittbrodt, Heike Jänicke, Fred A. Hamprecht:
Deltr: Digital embryo lineage tree reconstructor. ISBI 2011: 1557-1560 - [c21]Christoph N. Straehle, Ullrich Köthe, Graham Knott, Fred A. Hamprecht:
Carving: Scalable Interactive Segmentation of Neural Volume Electron Microscopy Images. MICCAI (1) 2011: 653-660 - [c20]Xinghua Lou, Fred A. Hamprecht:
Structured Learning for Cell Tracking. NIPS 2011: 1296-1304 - [c19]Bjoern H. Menze, B. Michael Kelm, Daniel Nicolas Splitthoff, Ullrich Köthe, Fred A. Hamprecht:
On Oblique Random Forests. ECML/PKDD (2) 2011: 453-469 - [c18]Sven Wanner, Christoph Sommer, Roland Rocholz, Michael Jung, Fred A. Hamprecht, Bernd Jähne:
A Framework for Interactive Visualization and Classification of Dynamical Processes at the Water Surface. VMV 2011: 199-206 - [i4]Jan Funke, Björn Andres, Fred A. Hamprecht, Albert Cardona, Matthew Cook:
Multi-Hypothesis CRF-Segmentation of Neural Tissue in Anisotropic EM Volumes. CoRR abs/1109.2449 (2011) - 2010
- [j13]Marc Kirchner, Bernhard Y. Renard, Ullrich Köthe, Darryl J. Pappin, Fred A. Hamprecht, Hanno Steen, Judith A. J. Steen:
Computational protein profile similarity screening for quantitative mass spectrometry experiments. Bioinform. 26(1): 77-83 (2010) - [j12]Xinghua Lou, Marc Kirchner, Bernhard Y. Renard, Ullrich Köthe, Sebastian Boppel, Christian Graf, Chung-Tien Lee, Judith A. J. Steen, Hanno Steen, Matthias P. Mayer, Fred A. Hamprecht:
Deuteration distribution estimation with improved sequence coverage for HX/MS experiments. Bioinform. 26(12): 1535-1541 (2010) - [c17]Frederik O. Kaster, Stephan Kassemeyer, Bernd Merkel, Oliver Nix, Fred A. Hamprecht:
An Object-oriented Library for Systematic Training and Comparison of Classifiers for Computer-assisted Tumor Diagnosis from MRSI Measurements. Bildverarbeitung für die Medizin 2010: 97-101 - [c16]Björn Andres, Jörg H. Kappes, Ullrich Köthe, Christoph Schnörr, Fred A. Hamprecht:
An Empirical Comparison of Inference Algorithms for Graphical Models with Higher Order Factors Using OpenGM. DAGM-Symposium 2010: 353-362 - [c15]Frederik O. Kaster, Bjoern H. Menze, Marc-André Weber, Fred A. Hamprecht:
Comparative Validation of Graphical Models for Learning Tumor Segmentations from Noisy Manual Annotations. MCV 2010: 74-85 - [c14]Ullrich Köthe, Björn Andres, Thorben Kröger, Fred A. Hamprecht:
Geometric Analysis of 3D Electron Microscopy Data. WADGMM 2010: 93-108 - [i3]Björn Andres, Ullrich Köthe, Thorben Kröger, Fred A. Hamprecht:
Runtime-Flexible Multi-dimensional Arrays and Views for C++98 and C++0x. CoRR abs/1008.2909 (2010) - [i2]Björn Andres, Jörg H. Kappes, Ullrich Köthe, Fred A. Hamprecht:
The Lazy Flipper: MAP Inference in Higher-Order Graphical Models by Depth-limited Exhaustive Search. CoRR abs/1009.4102 (2010) - [i1]Björn Andres, Ullrich Köthe, Thorben Kröger, Fred A. Hamprecht:
How to Extract the Geometry and Topology from Very Large 3D Segmentations. CoRR abs/1009.6215 (2010)
2000 – 2009
- 2009
- [j11]Bjoern H. Menze, B. Michael Kelm, Ralf Masuch, Uwe Himmelreich, Peter Bachert, Wolfgang Petrich, Fred A. Hamprecht:
A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data. BMC Bioinform. 10 (2009) - [j10]Stefan Trittler, Fred A. Hamprecht:
Near optimum sampling design and an efficient algorithm for single tone frequency estimation. Digit. Signal Process. 19(4): 628-639 (2009) - [j9]Mark Jäger, Fred A. Hamprecht:
Principal Component Imagery for the Quality Monitoring of Dynamic Laser Welding Processes. IEEE Trans. Ind. Electron. 56(4): 1307-1313 (2009) - [j8]B. Michael Kelm, Bjoern H. Menze, Oliver Nix, Christian M. Zechmann, Fred A. Hamprecht:
Estimating Kinetic Parameter Maps From Dynamic Contrast-Enhanced MRI Using Spatial Prior Knowledge. IEEE Trans. Medical Imaging 28(10): 1534-1547 (2009) - [c13]Björn Andres, Ullrich Köthe, Andreea Bonea, Boaz Nadler, Fred A. Hamprecht:
Quantitative Assessment of Image Segmentation Quality by Random Walk Relaxation Times. DAGM-Symposium 2009: 502-511 - [c12]Michael Hanselmann, Ullrich Köthe, Bernhard Y. Renard, Marc Kirchner, Ron M. A. Heeren, Fred A. Hamprecht:
Multivariate Watershed Segmentation of Compositional Data. DGCI 2009: 180-192 - 2008
- [j7]Bernhard Y. Renard, Marc Kirchner, Hanno Steen, Judith A. J. Steen, Fred A. Hamprecht:
NITPICK: peak identification for mass spectrometry data. BMC Bioinform. 9 (2008) - [j6]H. Rapp, Mario Frank, Fred A. Hamprecht, Bernd Jähne:
A theoretical and experimental investigation of the systematic errors and statistical uncertainties of Time-Of-Flight-cameras. Int. J. Intell. Syst. Technol. Appl. 5(3/4): 402-413 (2008) - [j5]Mark Jäger, Silke Humbert, Fred A. Hamprecht:
Sputter Tracking for the Automatic Monitoring of Industrial Laser-Welding Processes. IEEE Trans. Ind. Electron. 55(5): 2177-2184 (2008) - [j4]Mark Jäger, Christian Knoll, Fred A. Hamprecht:
Weakly Supervised Learning of a Classifier for Unusual Event Detection. IEEE Trans. Image Process. 17(9): 1700-1708 (2008) - [c11]Björn Andres, Claudia Kondermann, Daniel Kondermann, Ullrich Köthe, Fred A. Hamprecht, Christoph S. Garbe:
On errors-in-variables regression with arbitrary covariance and its application to optical flow estimation. CVPR 2008 - [c10]Björn Andres, Ullrich Köthe, Moritz Helmstaedter, Winfried Denk, Fred A. Hamprecht:
Segmentation of SBFSEM Volume Data of Neural Tissue by Hierarchical Classification. DAGM-Symposium 2008: 142-152 - 2007
- [c9]Björn Andres, Fred A. Hamprecht, Christoph S. Garbe:
Selection of Local Optical Flow Models by Means of Residual Analysis. DAGM-Symposium 2007: 72-81 - [c8]L. Görlitz, Bjoern H. Menze, Marc-André Weber, B. Michael Kelm, Fred A. Hamprecht:
Semi-supervised Tumor Detection in Magnetic Resonance Spectroscopic Images Using Discriminative Random Fields. DAGM-Symposium 2007: 224-233 - [e1]Fred A. Hamprecht, Christoph Schnörr, Bernd Jähne:
Pattern Recognition, 29th DAGM Symposium, Heidelberg, Germany, September 12-14, 2007, Proceedings. Lecture Notes in Computer Science 4713, Springer 2007, ISBN 978-3-540-74933-2 [contents] - 2006
- [c7]Bjoern H. Menze, B. Michael Kelm, Daniel Heck, Matthias Lichy, Fred A. Hamprecht:
Machine-Based Rejection of Low-Quality Spectra and Estimation of Brain Tumor Probabilities from Magnetic Resonance Spectroscopic Images. Bildverarbeitung für die Medizin 2006: 31-35 - [c6]B. Michael Kelm, Bjoern H. Menze, Thomas Neff, Christian M. Zechmann, Fred A. Hamprecht:
CLARET: A Tool for Fully Automated Evaluation of MRSI with Pattern Recognition Methods. Bildverarbeitung für die Medizin 2006: 51-55 - [c5]B. Michael Kelm, Natalie Mueller, Bjoern H. Menze, Fred A. Hamprecht:
Bayesian Estimation of Smooth Parameter Maps for Dynamic Contrast-Enhanced MR Images with Block-ICM. CVPR Workshops 2006: 96 - [c4]Bjoern H. Menze, B. Michael Kelm, Fred A. Hamprecht:
From Eigenspots to Fisherspots - Latent Spaces in the Nonlinear Detection of Spot Patterns in a Highly Varying Background. GfKl 2006: 255-262 - 2005
- [j3]Hans R. Künsch, Erik Agrell, Fred A. Hamprecht:
Optimal lattices for sampling. IEEE Trans. Inf. Theory 51(2): 634-647 (2005) - 2004
- [c3]Sören Hader, Fred A. Hamprecht:
Two-stage Classification with Automatic Feature Selection for an Industrial Application. GfKl 2004: 137-144 - [c2]Bjoern H. Menze, Michael Wormit, Peter Bachert, Matthias Lichy, Heinz-Peter Schlemmer, Fred A. Hamprecht:
Classification of In Vivo Magnetic Resonance Spectra. GfKl 2004: 362-369 - 2002
- [j2]Fred A. Hamprecht, Walter Thiel, Wilfred F. van Gunsteren:
Chemical Library Subset Selection Algorithms: A Unified Derivation Using Spatial Statistics. J. Chem. Inf. Comput. Sci. 42(2): 414-428 (2002) - [c1]Heiko Eisele, Fred A. Hamprecht:
A New Approach for Defect Detection in X-ray CT Images. DAGM-Symposium 2002: 345-352
1990 – 1999
- 1998
- [j1]Michael Bühl, Fred A. Hamprecht:
Theoretical investigations of NMR chemical shifts and reactivities of oxovanadium(v) compounds. J. Comput. Chem. 19(2): 113-122 (1998)
Coauthor Index
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