@article {Swoboda2016,
	title = {Partial Optimality by Pruning for MAP-Inference with General Graphical Models},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {38},
	number = {7},
	year = {2016},
	month = {jul},
	pages = {1370{\textendash}1382},
	publisher = {IEEE Computer Society},
	abstract = {We consider the energy minimization problem for undirected graphical models, also known as MAP-inference problem for Markov random fields which is NP-hard in general. We propose a novel polynomial time algorithm to obtain a part of its optimal non-relaxed integral solution. Our algorithm is initialized with variables taking integral values in the solution of a convex relaxation of the MAP-inference problem and iteratively prunes those, which do not satisfy our criterion for partial optimality. We show that our pruning strategy is in a certain sense theoretically optimal. Also empirically our method outperforms previous approaches in terms of the number of persistently labelled variables. The method is very general, as it is applicable to models with arbitrary factors of an arbitrary order and can employ any solver for the considered relaxed problem. Our method{\textquoteright}s runtime is determined by the runtime of the convex relaxation solver for the MAP-inference problem.},
	keywords = {energy minimization, Local polytope, MAP-inference, Markov random fields, partial optimality, persistency},
	issn = {01628828},
	doi = {10.1109/TPAMI.2015.2484327},
	author = {Swoboda, Paul and Shekhovtsov, Alexander and Kappes, Jorg Hendrik and Christoph Schn{\"o}rr and Savchynskyy, Bogdan}
}
@proceedings {6110,
	title = {Plane Wave Acoustic Superposition for Fast Ultrasound Imaging},
	year = {2016},
	author = {Ecaterina Bodnariuc and Martin F. Schiffner and Stefania Petra and Christoph Schn{\"o}rr}
}
@conference {Kappes2015c,
	title = {Probabilistic correlation clustering and image partitioning using perturbed Multicuts},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {9087},
	year = {2015},
	pages = {231{\textendash}242},
	abstract = {We exploit recent progress on globally optimal MAP inference by integer programming and perturbation-based approximations of the log-partition function. This enables to locally represent uncertainty of image partitions by approximate marginal distributions in a mathematically substantiated way, and to rectify local data term cues so as to close contours and to obtain valid partitions. Our approach works for any graphically represented problem instance of correlation clustering, which is demonstrated by an additional social network example.},
	keywords = {Correlation clustering, Multicut, Perturb and MAP},
	isbn = {9783319184609},
	issn = {16113349},
	doi = {10.1007/978-3-319-18461-6_19},
	author = {Kappes, Jorg Hendrik and Swoboda, Paul and Savchynskyy, Bogdan and Hazan, Tamir and Christoph Schn{\"o}rr}
}
@conference {Kappes2015a,
	title = {Probabilistic Correlation Clustering and Image Partitioning Using Perturbed Multicuts},
	booktitle = {Proc.~SSVM},
	year = {2015},
	publisher = {Springer},
	organization = {Springer},
	author = {J{\"o}rg H. Kappes and Swoboda, P. and Savchynskyy, B. and Hazan, T. and Christoph Schn{\"o}rr}
}
@article {swoboda_14_partial,
	title = {Partial Optimality by Pruning for MAP-inference with General GraphicalModels},
	year = {2014},
	pages = {1170-1177},
	doi = {10.1109/CVPR.2014.153},
	author = {Swoboda, P. and Savchynskyy, B. and J{\"o}rg H. Kappes and Christoph Schn{\"o}rr}
}
@conference {Swoboda-2014,
	title = {Partial Optimality by Pruning for MAP-inference with General Graphical Models},
	booktitle = {IEEE Conference on Computer Vision and Pattern Recognition 2014},
	year = {2014},
	author = {Swoboda, Paul and Savchynskyy, Bogdan and Kappes, J{\"o}rg H. and Christoph Schn{\"o}rr}
}
@conference {Swoboda-2014,
	title = {Partial Optimality by Pruning for MAP-inference with General Graphical Models},
	booktitle = {IEEE Conference on Computer Vision and Pattern Recognition 2014},
	year = {2014},
	author = {Swoboda, Paul and Savchynskyy, Bogdan and J{\"o}rg H. Kappes and Christoph Schn{\"o}rr}
}
@article {Denitiu2014a,
	title = {Phase Transitions and Cosparse Tomographic Recovery of Compound Solid Bodies from Few Projections},
	journal = {Fundamenta Informaticae},
	volume = {135},
	year = {2014},
	pages = {73{\textendash}102},
	author = {Andreea Denitiu and Petra, S. and Schn{\"o}rr, Cl. and Christoph Schn{\"o}rr}
}
@article {Denitiu2014a,
	title = {Phase Transitions and Cosparse Tomographic Recovery of Compound Solid Bodies from Few Projections},
	journal = {Fundamenta Informaticae},
	volume = {135},
	year = {2014},
	pages = {73--102},
	author = {Andreea Denitiu and Stefania Petra and Schn{\"o}rr, Cl. and Christoph Schn{\"o}rr}
}
@article {rathke2014,
	title = {Probabilistic Intra-Retinal Layer Segmentation in 3-{D} {OCT} Images Using Global Shape Regularization},
	journal = {Med. Image Anal.},
	volume = {18},
	year = {2014},
	pages = {781{\textendash}794},
	author = {Rathke, Fabian and Schmidt, Stefan and Christoph Schn{\"o}rr}
}
@article {Rathke2014,
	title = {Probabilistic Intra-Retinal Layer Segmentation in 3-D OCT Images Using Global Shape Regularization},
	journal = {Medical Image Analysis},
	volume = {18},
	number = {5},
	year = {2014},
	pages = {781-794},
	author = {Rathke, F. and Schmidt, S. and Christoph Schn{\"o}rr}
}
@conference {SwobodaSSVM13,
	title = {Partial Optimality via Iterative Pruning for the Potts Model},
	booktitle = {Scale Space and Variational Methods (SSVM 2013)},
	year = {2013},
	author = {Swoboda, Paul and Savchynskyy, Bogdan and J{\"o}rg H. Kappes and Christoph Schn{\"o}rr}
}
@conference {SwobodaSSVM13,
	title = {Partial Optimality via Iterative Pruning for the Potts Model},
	booktitle = {Scale Space and Variational Methods (SSVM 2013)},
	year = {2013},
	author = {Paul Swoboda and Bogdan Savchynskyy and J{\"o}rg H. Kappes and Christoph Schn{\"o}rr}
}
@conference {swoboda_13_partial,
	title = {Partial Optimality via Iterative Pruning for the Potts Model},
	booktitle = {Proceedings of the 4th International Conference on Scale Space and
Variational Methods in Computer Vision SSVM},
	number = {7893},
	year = {2013},
	note = {1},
	pages = {477-488},
	doi = {10.1007/978-3-642-38267-3_40},
	author = {Swoboda, P. and Savchynskyy, B. and J{\"o}rg H. Kappes and Christoph Schn{\"o}rr}
}
@conference {swoboda_13_persistency,
	title = {Persistency by Pruning for General Graphical Models},
	booktitle = {submitted to NIPS 2013.},
	year = {2013},
	author = {Swoboda, P. and Savchynskyy, B. and J{\"o}rg H. Kappes and Christoph Schn{\"o}rr}
}
@article {Vlasenko-Schnoerr-10,
	title = {Physically Consistent and Efficient Variational Denoising of Image Fluid Flow Estimates},
	journal = {IEEE Trans.~Image Proc.},
	volume = {19},
	number = {3},
	year = {2010},
	pages = {586-595},
	author = {Vlasenko, A. and Christoph Schn{\"o}rr}
}
@conference {Vlasenko-et-al-08a,
	title = {Physically Consistent Variational Denoising of Image Fluid Flow Estimates},
	booktitle = {Pattern Recognition -- 30th DAGM Symposium},
	volume = {5096},
	year = {2008},
	pages = {406--415},
	publisher = {Springer Verlag},
	organization = {Springer Verlag},
	author = {Vlasenko, A. and Christoph Schn{\"o}rr}
}
@proceedings {DAGM07,
	title = {Pattern Recognition {\textendash} 29th DAGM Symposium},
	volume = {4713},
	year = {2007},
	publisher = {Springer},
	editor = {Fred A. Hamprecht and Christoph Schn{\"o}rr and Bernd J{\"a}hne}
}
@book {hamprecht_07_pattern,
	title = {Pattern Recognition, 29th DAGM Symposium, Heidelberg, Germany, September 12-14, 2007, Proceedings},
	volume = {4713},
	year = {2007},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-540-74936-3},
	editor = {Fred A. Hamprecht and Bernd J{\"a}hne and Christoph Schn{\"o}rr}
}
@proceedings {hamprecht2007,
	title = {Pattern Recognition, 29th DAGM Symposium, Heidelberg, Germany, September 12-14},
	volume = {4713},
	year = {2007},
	publisher = {Springer},
	doi = {10.1007/978-3-540-74936-3},
	author = {Christoph Schn{\"o}rr and Bernd J{\"a}hne},
	editor = {Fred A. Hamprecht}
}
