@proceedings {6204,
	title = {Cost-efficient Gradient Boosting},
	year = {2017},
	author = {Peter, S. and Ferran Diego and Fred A. Hamprecht and Nadler, B}
}
@proceedings {kandemir_15_cell,
	title = {Cell event detection in phase-contrast microscopy sequences from few annotations},
	volume = {LNCS 9351},
	year = {2015},
	pages = {316-323},
	publisher = {Springer},
	doi = {10.1007/978-3-319-24574-4_38},
	author = {Kandemir, M. and Fred A. Hamprecht}
}
@article {Kappes2015b,
	title = {A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems},
	journal = {International Journal of Computer Vision},
	volume = {115},
	number = {2},
	year = {2015},
	pages = {155{\textendash}184},
	abstract = {Szeliski et al. published an influential study in 2006 on energy minimization methods for Markov random fields. This study provided valuable insights in choosing the best optimization technique for certain classes of problems. While these insights remain generally useful today, the phenomenal success of random field models means that the kinds of inference problems that have to be solved changed significantly. Specifically, the models today often include higher order interactions, flexible connectivity structures, large label-spaces of different cardinalities, or learned energy tables. To reflect these changes, we provide a modernized and enlarged study. We present an empirical comparison of more than 27 state-of-the-art optimization techniques on a corpus of 2453 energy minimization instances from diverse applications in computer vision. To ensure reproducibility, we evaluate all methods in the OpenGM 2 framework and report extensive results regarding runtime and solution quality. Key insights from our study agree with the results of Szeliski et al. for the types of models they studied. However, on new and challenging types of models our findings disagree and suggest that polyhedral methods and integer programming solvers are competitive in terms of runtime and solution quality over a large range of model types.},
	keywords = {Benchmark, Combinatorial optimization, Discrete graphical models},
	issn = {15731405},
	doi = {10.1007/s11263-015-0809-x},
	author = {Kappes, J{\"o}rg H and Bj{\"o}rn Andres and Fred A. Hamprecht and Christoph Schn{\"o}rr and Nowozin, Sebastian and Dhruv Batra and Kim, Sungwoong and Kausler, Bernhard X and Kr{\"o}ger, Thorben and Lellmann, Jan and Komodakis, Nikos and Savchynskyy, Bogdan and Carsten Rother}
}
@article {Kappes2015a,
	title = {A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems},
	journal = {International Journal of Computer Vision},
	volume = {115},
	number = {2},
	year = {2015},
	pages = {155{\textendash}184},
	abstract = {Szeliski et al. published an influential study in 2006 on energy minimization methods for Markov random fields. This study provided valuable insights in choosing the best optimization technique for certain classes of problems. While these insights remain generally useful today, the phenomenal success of random field models means that the kinds of inference problems that have to be solved changed significantly. Specifically, the models today often include higher order interactions, flexible connectivity structures, large label-spaces of different cardinalities, or learned energy tables. To reflect these changes, we provide a modernized and enlarged study. We present an empirical comparison of more than 27 state-of-the-art optimization techniques on a corpus of 2453 energy minimization instances from diverse applications in computer vision. To ensure reproducibility, we evaluate all methods in the OpenGM 2 framework and report extensive results regarding runtime and solution quality. Key insights from our study agree with the results of Szeliski et al. for the types of models they studied. However, on new and challenging types of models our findings disagree and suggest that polyhedral methods and integer programming solvers are competitive in terms of runtime and solution quality over a large range of model types.},
	keywords = {Benchmark, Combinatorial optimization, Discrete graphical models},
	isbn = {25164671.25},
	issn = {15731405},
	doi = {10.1007/s11263-015-0809-x},
	author = {Kappes, J{\"o}rg H and Bj{\"o}rn Andres and Fred A. Hamprecht and Christoph Schn{\"o}rr and Nowozin, Sebastian and Dhruv Batra and Kim, Sungwoong and Kausler, Bernhard X and Kr{\"o}ger, Thorben and Lellmann, Jan and Komodakis, Nikos and Savchynskyy, Bogdan and Carsten Rother}
}
@article {Kappes2015,
	title = {A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems},
	journal = {International Journal of Computer Vision},
	volume = {115},
	number = {2},
	year = {2015},
	pages = {155{\textendash}184},
	abstract = {Szeliski et al. published an influential study in 2006 on energy minimization methods for Markov random fields. This study provided valuable insights in choosing the best optimization technique for certain classes of problems. While these insights remain generally useful today, the phenomenal success of random field models means that the kinds of inference problems that have to be solved changed significantly. Specifically, the models today often include higher order interactions, flexible connectivity structures, large label-spaces of different cardinalities, or learned energy tables. To reflect these changes, we provide a modernized and enlarged study. We present an empirical comparison of more than 27 state-of-the-art optimization techniques on a corpus of 2453 energy minimization instances from diverse applications in computer vision. To ensure reproducibility, we evaluate all methods in the OpenGM 2 framework and report extensive results regarding runtime and solution quality. Key insights from our study agree with the results of Szeliski et al. for the types of models they studied. However, on new and challenging types of models our findings disagree and suggest that polyhedral methods and integer programming solvers are competitive in terms of runtime and solution quality over a large range of model types.},
	keywords = {Benchmark, Combinatorial optimization, Discrete graphical models},
	issn = {15731405},
	doi = {10.1007/s11263-015-0809-x},
	url = {http://hci.iwr.uni-heidelberg.de/opengm2/},
	author = {Kappes, J{\"o}rg H and Bj{\"o}rn Andres and Fred A. Hamprecht and Christoph Schn{\"o}rr and Nowozin, Sebastian and Dhruv Batra and Kim, Sungwoong and Kausler, Bernhard X and Kr{\"o}ger, Thorben and Lellmann, Jan and Komodakis, Nikos and Savchynskyy, Bogdan and Carsten Rother}
}
@article {Kappes2015,
	title = {A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems},
	journal = {Int.~J.~Comp.~Vision},
	year = {2015},
	note = {in press (preprint: arXiv:1404.0533)},
	author = {J{\"o}rg H. Kappes and Bj{\"o}rn Andres and Fred A. Hamprecht and Christoph Schn{\"o}rr and Nowozin, S. and Dhruv Batra and Kim, S. and Bernhard X. Kausler and Thorben Kr{\"o}ger and Lellmann, J. and Komodakis, N. and Savchynskyy, B. and Carsten Rother}
}
@article {kappes_15_comparative,
	title = {A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems},
	journal = {International Journal of Computer Vision},
	year = {2015},
	note = {1},
	pages = {1-30},
	doi = {10.1007/s11263-015-0809-x},
	author = {J{\"o}rg H. Kappes and Bj{\"o}rn Andres and Fred A. Hamprecht and Christoph Schn{\"o}rr and Nowozin, S. and Dhruv Batra and Kim, S. and Bernhard X. Kausler and Thorben Kr{\"o}ger and Lellmann, J. and Komodakis, N. and Savchynskyy, B. and Carsten Rother}
}
@conference {zhang_14_cell,
	title = {Cell detection and segmentation using correlation clustering},
	booktitle = {MICCAI. Proceedings},
	number = {8673},
	year = {2014},
	note = {1},
	pages = {9-16},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-319-10404-1_2},
	author = {Zhang, C. and Julian Yarkony and Fred A. Hamprecht}
}
@article {kappes-1014-bench-arxiv,
	title = {A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems},
	journal = {CoRR},
	volume = {abs/1404.0533},
	year = {2014},
	url = {http://hci.iwr.uni-heidelberg.de/opengm2/},
	author = {J{\"o}rg H. Kappes and Bj{\"o}rn Andres and Fred A. Hamprecht and Christoph Schn{\"o}rr and Nowozin, Sebastian and Dhruv Batra and Kim, Sungwoong and Bernhard X. Kausler and Thorben Kr{\"o}ger and Lellmann, Jan and Komodakis, Nikos and Savchynskyy, Bogdan and Carsten Rother}
}
@article {kappes_14_comparative,
	title = {A Comparative Study of Modern Inference Techniques for Structured
Discrete Energy Minimization Problems},
	journal = {CoRR},
	year = {2014},
	note = {1},
	url = {http://arxiv.org/abs/1404.0533},
	author = {J{\"o}rg H. Kappes and Bj{\"o}rn Andres and Fred A. Hamprecht and Christoph Schn{\"o}rr and Nowozin, S. and Dhruv Batra and Kim, S. and Bernhard X. Kausler and Thorben Kr{\"o}ger and Lellmann, J. and Komodakis, N. and Savchynskyy, B. and Carsten Rother}
}
@article {kandemir_14_computer-aided,
	title = {Computer-aided diagnosis from weak supervision: A benchmarking study},
	journal = {Computerized Medical Imaging and Graphics},
	volume = {42},
	year = {2014},
	note = {1},
	pages = {44-50},
	doi = {10.1016/j.compmedimag.2014.11.010},
	author = {Kandemir, M. and Fred A. Hamprecht}
}
@conference {beier_14_cut,
	title = {Cut, Glue and Cut: A Fast, Approximate Solver for Multicut Partitioning},
	booktitle = {2014 {IEEE} Conference on Computer Vision and Pattern Recognition, {CVPR} 2014, Columbus, OH, USA, June 23-28, 2014},
	year = {2014},
	doi = {10.1109/CVPR.2014.17},
	url = {http://dx.doi.org/10.1109/CVPR.2014.17},
	author = {Thorsten Beier and Thorben Kr{\"o}ger and J{\"o}rg H. Kappes and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@conference {kappes_13_comparative,
	title = {A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problems},
	booktitle = {CVPR 2013. Proceedings},
	year = {2013},
	note = {1},
	doi = {10.1109/CVPR.2013.175},
	author = {J{\"o}rg H. Kappes and Bj{\"o}rn Andres and Fred A. Hamprecht and Christoph Schn{\"o}rr and Nowozin, S. and Dhruv Batra and Sungwoong, K. and Bernhard X. Kausler and Lellmann, J. and Komodakis, N. and Carsten Rother}
}
@conference {Kappes-2013-benchmark,
	title = {A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problem},
	booktitle = {CVPR},
	year = {2013},
	author = {J{\"o}rg H. Kappes and Bj{\"o}rn Andres and Fred A. Hamprecht and Christoph Schn{\"o}rr and Nowozin, Sebastian and Dhruv Batra and Kim, Sungwoong and Bernhard X. Kausler and Lellmann, Jan and Komodakis, Nikos and Carsten Rother}
}
@conference {schiegg_13_conservation,
	title = {Conservation Tracking},
	booktitle = {ICCV 2013. Proceedings},
	year = {2013},
	note = {1},
	pages = {2928--2935},
	doi = {10.1109/ICCV.2013.364},
	author = {Schiegg, M. and Hanslovsky, P. and Bernhard X. Kausler and Hufnagel, L. and Fred A. Hamprecht}
}
@article {maco_13_correlative,
	title = {Correlative in vivo 2 photon and focused ion beam scanning electron microscopy of cortical neurons},
	journal = {PloS one},
	volume = {8 (2)},
	year = {2013},
	doi = {10.1371/journal.pone.0057405},
	author = {Maco, B. and Holtmaat, A. and Cantoni, M. and Anna Kreshuk and Christoph N. Straehle and Fred A. Hamprecht and G. W. Knott}
}
@conference {straehle_11_carving,
	title = {Carving: Scalable Interactive Segmentation of Neural Volume Electron Microscopy Images},
	booktitle = {MICCAI 2011, Proceedings.},
	volume = {6891},
	year = {2011},
	note = {1},
	pages = {653-660},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-642-23623-5_82},
	author = {Christoph N. Straehle and Ullrich K{\"o}the and G. W. Knott and Fred A. Hamprecht}
}
@conference {kaster_11_comparative,
	title = {Comparative Validation of Graphical Models for Learning Tumor Segmentations from Noisy Manual Annotations},
	booktitle = {LNCS},
	volume = {LNCS 6533},
	year = {2011},
	note = {1},
	pages = {74-85},
	publisher = {Springer, Heidelberg},
	organization = {Springer, Heidelberg},
	doi = {10.1007/978-3-642-18421-5_8},
	author = {F. O. Kaster and M.-A. Weber and Fred A. Hamprecht},
	editor = {Bjoern H. Menze and Bjoern H. Menze and Langs, G. and Criminisi, A. and Tu, Z.}
}
@article {kirchner_10_computational,
	title = {Computational Protein Profile Similarity Screening for Quantitative Mass Spectrometry Experiments},
	journal = {Bioinformatics},
	volume = {26 (1)},
	year = {2010},
	note = {1},
	pages = {77-83},
	doi = {10.1093/bioinformatics/btp607},
	author = {Kirchner, M. and B. Y. Renard and Ullrich K{\"o}the and Pappin, D. J. and Fred A. Hamprecht and Judith A. J. Steen and Steen, H.}
}
@conference {kaster_09_classification,
	title = {Classification of Spectroscopic Images in the DIROlab Environment},
	booktitle = {World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany},
	volume = {25/V},
	number = {25050252},
	year = {2009},
	pages = {252--255},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-642-03904-1_70},
	author = {F. O. Kaster and B. Michael Kelm and C. M. Zechmann and M.-A. Weber and Fred A. Hamprecht and Nix, O.},
	editor = {D{\"o}ssel, Olaf and Schlegel, Wolfgang C.}
}
@article {menze_09_comparison,
	title = {A Comparison of Random Forest and its Gini Importance with Standard Chemometric Methods for the Feature Selection and Classification of Spectral Data},
	journal = {BMC Bioinformatics},
	volume = {10:213},
	year = {2009},
	note = {1},
	doi = {10.1186/1471-2105-10-213},
	author = {Bjoern H. Menze and B. Michael Kelm and Masuch, R. and Himmelreich, U. and Bachert, P. and Petrich, W. and Fred A. Hamprecht}
}
@article {hanselmann_08_concise,
	title = {Concise Representation of MS Images by Probabilistic Latent Semantic Analysis},
	journal = {Analytical Chemistry},
	volume = {80},
	number = {24},
	year = {2008},
	note = {1},
	pages = {9649-9658},
	doi = {10.1021/ac801303x},
	author = {Hanselmann, M. and Kirchner, M. and B. Y. Renard and Amstalden, E. R. and Glunde, K. and Heeren, R. M. A. and Fred A. Hamprecht}
}
@article {weber_07_comparison,
	title = {Comparison of correctness of manuel and automatic evaluation of MR-spectrum with prostrate cancer},
	journal = {Der Urologe},
	volume = {46},
	number = {9},
	year = {2007},
	pages = {1252},
	doi = {10.1007/s00120-007-1488-1},
	author = {C. Weber and C. M. Zechmann and B. Michael Kelm and Zamecnik, R. and Hendricks, D. and Waldherr, R. and Fred A. Hamprecht and Delorme, S. and Bachert, P. and Ikinger, U.}
}
@conference {zechmann_06_can,
	title = {Can man still beat the machine? Automated vs. manual pattern recognition of 3D MRSI data of prostate cancer patients},
	booktitle = {Proceedings of the 16th ISMRM},
	year = {2006},
	author = {C. M. Zechmann and B. Michael Kelm and Zamecnik, P. and Ikinger, U. and Waldherr, R. and R{\"o}ll, S. and Delorme, S. and Fred A. Hamprecht and Bachert, P.}
}
@conference {kelm_06_claret,
	title = {CLARET: a tool for fully automated evaluation of MRSI with pattern recognition methods.},
	booktitle = {Bildverarbeitung f{\"u}r die Medizin 2006 - Algorithmen, Systeme, Anwendungen},
	year = {2006},
	note = {1},
	pages = {51-55},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/3-540-32137-3_7},
	url = {http://www.efmi-wg-mip.net/service/bvm2006},
	author = {B. Michael Kelm and Bjoern H. Menze and Neff, T. and C. M. Zechmann and Fred A. Hamprecht},
	editor = {H. Handels and G. Bebis and et al.}
}
@inbook {carlsohn_06_spectral,
	title = {Color image processing},
	volume = {7(17)},
	year = {2006},
	pages = {393-419},
	publisher = {CRC Press},
	organization = {CRC Press},
	chapter = {Spectral Imaging and Applications},
	author = {Carlsohn, M. F. and Bjoern H. Menze and B. Michael Kelm and Fred A. Hamprecht and Kercek, A. and Leitner, R. and Polder, G.},
	editor = {Lukac, R. and Plataniotis, K.N.}
}
@incollection {hamprecht_04_classification,
	title = {Classification},
	year = {2004},
	pages = {509-519},
	publisher = {CRC Press},
	edition = {2nd},
	author = {Fred A. Hamprecht},
	editor = {Bernd J{\"a}hne}
}
@conference {menze_04_classification,
	title = {Classification of in vivo magnetic resonance spectra},
	booktitle = {Classification in ubiquitous challenge: Proceedings of the GfKl 2004},
	year = {2004},
	pages = {362-369},
	publisher = {Springer},
	organization = {Springer},
	author = {Bjoern H. Menze and Wormit, M. and Bachert, P. and M. P. Lichy and Schlemmer, H.-P. and Fred A. Hamprecht}
}
@article {hamprecht_02_chemical,
	title = {Chemical library subset selection algorithms: a unified derivation using spatial statistics},
	journal = {Journal of Chemical Information and Computer Sciences},
	volume = {42},
	year = {2002},
	pages = {414-428},
	author = {Fred A. Hamprecht and Thiel, W. and van Gunsteren, W. F.}
}
