GRK 1653:  Spatio/Temporal Probabilistic Graphical Models and Applications in Image Analysis

• Subject Area: Computer Science ; Mathematics
• Term: from 2010 to 2019
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 159280219

• Final Report Year: 2020

Final Report Abstract

Probabilistic graphical models provide a consistent framework for the statistical modeling and the computational analysis of scientific empirical data. The past decade has witnessed a significant increase in respecive research in the field of image analysis and related application areas, driven by thesynergy between statistics, pattern recognition, computer vision and machine learning. The objective is to devise models that enable to infer a coherent global interpretation of noise and ambiguous local image measurements, taking into account spatiotemporal context in images and videos, and domain-specific contextual knowledge. Applications of probabilistic graphical models to such large-scale problems raise numerous research problems of modeling and algorithm design for inference and learning, requiring interdisciplinary expertise in applied mathematics, computer science and physics, besides a profound knowledge of the respective application areas. The basic intention of the Research Training Group is to gather experts from these fields and to establish a coherent research and study program on probabilistic graphical models, with a focus on spatial and spatiotemporal models and their applications in image analysis. The project treats methodological basic research on an equal footing with challenging scientific applications of image analysis in environmental science, life sciences and industry. The Research Training Group will provide a scientifically unique environment for study, collaboration and innovative research on probabilistic graphical models across disciplines, producing highly-qualified candidates for research careers in academia and industry.

Publications

• (2010). „A Study of Parts-Based Object Class Detection Using Complete Graphs“. In: Int. J. Comp. Vision 87.1-2, S. 93–117
  Bergtholdt, M., J. H. Kappes, S. Schmidt und C. Schnörr
  (See online at https://doi.org/10.1007/s11263-009-0209-1)
• (2010). „An Empirical Comparison of Inference Algorithms for Graphical Models with Higher Order Factors Using OpenGM“. In: Pattern Recognition, Proc. 32th DAGM Symposium
  Andres, B., J. H. Kappes, U. Köthe, C. Schnörr und F. Hamprecht
  (See online at https://doi.org/10.1007/978-3-642-15986-2_36)
• (2011). „Bayesian inference for general Gaussian graphical models with application to multivariate lattice data“. In: Journal of the American Statistical Association 106, S. 1418–1433
  Dobra, A., A. Lenkoski und A. Rodriguez
  (See online at https://doi.org/10.1198/jasa.2011.tm10465)
• (2011). „DELTR: Digital Embryo Lineage Tree Reconstructor“. In: Eighth IEEE International Symposium on Biomedical Imaging (ISBI), S. 1557–1560
  Lou, X., F. Kaster, M. Lindner, B. Kausler, U. Köthe, B. Höckendorf, J. Wittbrodt, H. Jänicke und F. A. Hamprecht
  (See online at https://doi.org/10.1109/ISBI.2011.5872698)
• (2011). „Globally Optimal Image Partitioning by Multicuts“. In: EMMCVPR. Springer
  Kappes, J. H., M. Speth, B. Andres, G. Reinelt und C. Schnörr
  (See online at https://doi.org/10.1007/978-3-642-23094-3_3)
• (2011). „Order Preserving and Shape Prior Constrained Intra- Retinal Layer Segmentation in Optical Coherence Tomography“. In: MICCAI. Hrsg. von G. Fichtinger, A. L. Martel und T. M. Peters. Bd. 6893. Lecture Notes in Computer Science. Springer, S. 370– 377
  Rathke, F., S. Schmidt und C. Schnörr
  (See online at https://doi.org/10.1007/978-3-642-23626-6_46)
• (2011). „Probabilistic Image Segmentation with Closedness Constraints“. In: ICCV
  Andres, B., J. H. Kappes, T. Beier, U. Köthe und F. Hamprecht
  (See online at https://doi.org/10.1109/ICCV.2011.6126550)
• (2011). „Sparse covariance estimation in heterogeneous samples“. In: Electronic Journal of Statistics 5, S. 981–1014
  Rodriguez, A., A. Lenkoski und A. Dobra
  (See online at https://doi.org/10.1214/11-ejs634)
• (2011). „Video parsing for abnormality detection“. In: IEEE International Conference on Computer Vision, ICCV, S. 2415–2422
  Antic, B. und B. Ommer
  (See online at https://doi.org/10.1109/ICCV.2011.6126525)
• (2012). „A Bayesian Approach to Spaceborn Hyperspectral Optical Flow Estimation on Dust Aerosols“. In: Proceedings of the International Geoscience and Remote Sensing Symposium, S. 256–259
  Bachl, F. E., P. Fieguth und C. S. Garbe
  (See online at https://doi.org/10.1109/IGARSS.2012.6351589)
• (2012). „A Discrete Chain Graph Model for 3d+t Cell Tracking with High Misdetection Robustness“. In: ECCV
  Kausler, B. X., M. Schiegg, B. Andres, M. Lindner, U. Köthe, H. Leitte, J. Wittbrodt, L. Hufnagel und F. A. Hamprecht
  (See online at https://doi.org/10.1007/978-3-642-33712-3_11)
• (2012). „Classifying and Tracking Dust Plumes from Passive Remote Sensing“. In: Proceedings of the ESA, SOLAS & EGU Joint Conference ‘Earth Observation for Ocean-Atmosphere Interaction Science’. Bd. 703. ESA Special Publication, S1–3
  Bachl, F. E. und C. S. Garbe
  
• (2012). „Multivariate Probabilistic Forecasting Using Ensemble Bayesian Model Averaging and Copulas“. In: Quarterly Journal of the Royal Meteorological Society 139.673, S. 982–991
  Möller, A., A. Lenkoski und T. Thorarinsdottir
  (See online at https://doi.org/10.1002/qj.2009)
• (2012). „Robust FDI Determinants“. In: Journal of Maroeconomics 34, S. 637–651
  Eicher, T. S., L. Helfman und A. Lenkoski
  (See online at https://doi.org/10.1016/j.jmacro.2012.01.010)
• (2012). „Robust Multiple-Instance Learning with Superbags“. In: Computer Vision - ACCV, Revised Selected Papers, Part II
  Antic, B. und B. Ommer
  (See online at https://doi.org/10.1007/978-3-642-37444-9_19)
• (2012). „The Lazy Flipper: Efficient Depth-limited Exhaustive Search in Discrete Graphical Models“. In: ECCV
  Andres, B., J. H. Kappes, T. Beier, U. Köthe und F. Hamprecht
  (See online at https://doi.org/10.1007/978-3-642-33786-4_12)
• (2013). „A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problems“. In: IEEE Conference on Computer Vision and Pattern Recognition
  Kappes, J. H., B. Andres, F. A. Hamprecht, C. Schnörr, S. Nowozin, D. Batra, S. Kim, B. X. Kausler, J. Lellmann, N. Komodakis und C. Rother
  (See online at https://doi.org/10.1109/CVPR.2013.175)
• (2013). „A Hierarchical Approach to Optimal Transport“. In: Scale Space and Variational Methods (SSVM), S. 452–464
  Schmitzer, B. und C. Schnörr
  (See online at https://doi.org/10.1007/978-3-642-38267-3_38)
• (2013). „Bayesian Inference on Integrated Continuity Fluid Flows and their Application to Dust Aerosols“. In: Proceedings of the International Geoscience and Remote Sensing Symposium, S. 2246– 2249
  Bachl, F. E., P. Fieguth und C. S. Garbe
  (See online at https://doi.org/10.1109/IGARSS.2013.6723264)
• (2013). „Chapter Three - Zinc Finger Proteins and the 3D Organization of Chromosomes“. In: Organisation of Chromosomes. Hrsg. von R. Donev. Bd. 90. Advances in Protein Chemistry and Structural Biology. Academic Press, S. 67–117
  Feinauer, C. J., A. Hofmann, S. Goldt, L. Liu, G. Maté und D. W. Heermann
  (See online at https://doi.org/10.1016/B978-0-12-410523-2.00003-1)
• (2013). „Graphical and Topological Analysis of the Cell Nucleus“. Diss. Faculty of Physics und Astronomy, Heidelberg University
  Maté, G.
  (See online at https://doi.org/10.11588/heidok.00016119)
• (2013). „Less Is More: Video Trimming for Action Recognition“. In: 2013 IEEE International Conference on Computer Vision Workshops
  Antic, B., T. Milbich und B. Ommer
  (See online at https://doi.org/10.1109/ICCVW.2013.73)
• (2013). „Modelling convex shape priors and matching based on the Gromov-Wasserstein distance“. In: Journal of Mathematical Imaging and Vision 46.1, S. 143–159
  Schmitzer, B. und C. Schnörr
  (See online at https://doi.org/10.1007/s10851-012-0375-6)
• (2013). „Object Segmentation by Shape Matching with Wasserstein Modes“. In: Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), S. 123–136
  Schmitzer, B. und C. Schnörr
  (See online at https://doi.org/10.1007/978-3-642-40395-8_10)
• (2013). „Towards Efficient and Exact MAP- Inference for Large Scale Discrete Computer Vision Problems via Combinatorial Optimization“. In: CVPR
  Kappes, J. H., M. Speth, G. Reinelt und C. Schnörr
  (See online at https://doi.org/10.1109/CVPR.2013.229)
• (2013). „Tracking-by-Assignment as a Probabilistic Graphical Model with Applications in Developmental Biology“. Diss. Faculty of Physics und Astronomy
  Kausler, B.
  (See online at https://doi.org/10.11588/heidok.00015296)
• (2014). „A topological similarity measure for proteins“. In: Biochimica et Biophysica Acta (BBA) - Biomembranes 1838.4. Viral Membrane Proteins - Channels for Cellular Networking, S. 1180–1190
  Maté, G., A. Hofmann, N. Wenzel und D. W. Heermann
  (See online at https://doi.org/10.1016/j.bbamem.2013.08.019)
• (2014). „Exact Solutions for Discrete Graphical Models: Multicuts and Reduction Techniques“. Diss. Faculty of Mathematics und Computer Science, Heidelberg University
  Speth, M.
  (See online at https://doi.org/10.11588/heidok.00017173)
• (2014). „Isometry Invariant Shape Priors for Variational Image Segmentation“. Diss. Faculty of Mathematics und Computer Science, Heidelberg University
  Schmitzer, B.
  (See online at https://doi.org/10.11588/heidok.00016873)
• (2014). „Latent Structured Models for Video Understanding“. Diss. Faculty of Mathematics und Computer Science, Heidelberg University
  Antic, B.
  (See online at https://dx.doi.org/10.11588/heidok.00017157)
• (2014). „Learning Latent Constituents for Recognition of Group Activities in Video“. In: Computer Vision - ECCV
  Antic, B. und B. Ommer
  (See online at https://doi.org/10.1007/978-3-319-10590-1_3)
• (2014). „Modeling of Locally Scaled Spatial Point Processes, and Applications in Image Analysis“. Diss. Faculty of Mathematics und Computer Science, Heidelberg University
  Didden, E.-M.
  (See online at https://doi.org/10.11588/heidok.00017757)
• (2014). „Multivariate and Spatial Ensemble Postprocessing Methods“. Diss. Faculty of Mathematics und Computer Science, Heidelberg University
  Möller, A.
  (See online at https://doi.org/10.11588/heidok.00017066)
• (2014). „Persistence intervals of fractals“. In: Physica A: Statistical Mechanics and its Applications 405, S. 252–259
  Maté, G. und D. W. Heermann
  (See online at https://doi.org/10.1016/j.physa.2014.03.037)
• (2014). „Probabilistic Intra-Retinal Layer Segmentation in 3-D OCT Images Using Global Shape Regularization“. In: Medical Image Analysis 18.5, S. 781–794
  Rathke, F., S. Schmidt und C. Schnörr
  (See online at https://doi.org/10.1016/j.media.2014.03.004)
• (2014). „Statistical analysis of protein ensembles“. In: Frontiers in Physics
  Maté, G. und D. W. Heermann
  (See online at https://doi.org/10.3389/fphy.2014.00020)
• (2014). „The affinely invariant distance correlation“. In: Bernoulli 20.4, S. 2305–2330
  Dueck, J., D. Edelmann, T. Gneiting und D. Richards
  (See online at https://doi.org/10.3150/13-BEJ558)
• (2014). „Two-Stage Bayesian Model Averaging in the Endogenous Variable Model“. In: Econometric Reviews 33, S. 122–151
  Lenkoski, A., T. S. Eicher und A. E. Raftery
  (See online at https://doi.org/10.1080/07474938.2013.807150)
• (2012). „Weakly Convex Coupling Continuous Cuts and Shape Priors“. In: Scale Space and Variational Methods (SSVM), S. 423–434
  Schmitzer, B. und C. Schnörr
  (See online at https://doi.org/10.1007/978-3-642-24785-9_36)
• (2015). „A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems“. In: International Journal of Computer Vision 115.2, S. 155–184
  Kappes, J. H., B. Andres, F. A. Hamprecht, C. Schnörr, S. Nowozin, D. Batra, S. Kim, B. X. Kausler, T. Kröger, J. Lellmann, N. Komodakis, B. Savchynskyy und C. Rother
  (See online at https://doi.org/10.1007/s11263-015-0809-x)
• (2015). „A Computational Approach to Log-Concave Density Estimation“. In: An. St. Univ. Ovidius Constanta 23.3, S. 151–166
  Rathke, F. und C. Schnörr
  (See online at https://doi.org/10.1515/auom-2015-0053)
• (2015). „A Convex Relaxation Approach to the Affine Subspace Clustering Problem“. In: Pattern Recognition - 37th German Conference, GCPR 2015, Aachen, Germany, October 7-10, 2015, Proceedings, S. 67–78
  Silvestri, F., G. Reinelt und C. Schnörr
  (See online at https://doi.org/10.1007/978-3-319-24947-6_6)
• (2015). „A generalization of an integral arising in the theory of distance correlation“. In: Statistics & Probability Letters 97, S. 116–119
  Dueck, J., D. Edelmann und D. Richards
  (See online at https://doi.org/10.1016/j.spl.2014.11.012)
• (2015). „A generalized Potts model for confocal microscopy images“. In: Int. J. Modern Physics 29.8, S. 1550048
  Maté, G. und D. W. Heermann
  (See online at https://doi.org/10.1142/S0217979215500484)
• (2015). „Adaptive Dictionary-Based Spatio-Temporal Flow Estimation for Echo PIV“. In: Proc. EMMCVPR
  Bodnariuc, E., A. Gurung, S. Petra und C. Schnörr
  (See online at https://doi.org/10.1007/978-3-319-14612-6_28)
• (2015). „Adaptive Sharpening of Multimodal Distributions“. In: Proc. CVCS. IEEE, S. 1–4
  Åström, F., M. Felsberg und H. Scharr
  (See online at https://doi.org/10.1109/CVCS.2015.7274890)
• (2015). „Bayesian Hierarchical Models for Remote Assessment of Atmospheric Dust“. Diss. Faculty of Mathematics und Computer Science
  Bachl, F. E.
  (See online at https://dx.doi.org/10.11588/heidok.00018257)
• (2015). „Bayesian Motion Estimation for Dust Aerosols“. In: The Annals of Applied Statistics 9.3, S. 1298–1327
  Bachl, F. E., A. Lenkoski, T. L. Thorarinsdottir und C. S. Garbe
  (See online at https://doi.org/10.1214/15-AOAS835)
• (2015). „Dependencies in Complex Systems“. Diss. Faculty of Mathematics und Computer Science, Heidelberg University
  Dueck, J.
  (See online at https://doi.org/10.11588/heidok.00018619)
• (2015). „Estimating Vehicle Ego-Motion and Piecewise Planar Scene Structure from Optical Flow in a Continuous Framework“. In: German Conference on Pattern Recognition. Springer, S. 41–52
  Neufeld, A., J. Berger, F. Becker, F. Lenzen und C. Schnörr
  (See online at https://doi.org/10.1007/978-3-319-24947-6_4)
• (2015). „Globally Optimal Joint Image Segmentation and Shape Matching based on Wasserstein Modes“. In: J. Math. Imag. Vision 52.3, S. 436–458
  Schmitzer, B. und C. Schnörr
  (See online at https://doi.org/10.1007/s10851-014-0546-8)
• (2015). „Improving 3d em data segmentation by joint optimization over boundary evidence and biological priors“. In: International Symposium on Biomedical Imaging
  Krasowski, N., T. Beier, G. Knott, U. Koethe, F. Hamprecht und A. Kreshuk
  (See online at https://doi.org/10.1109/ISBI.2015.7163929)
• (2015). „On Coupled Regularization for Non-convex Variational Image Enhancement“. In: Proc. ACPR. IEEE, S. 786–790
  Åström, F. und C. Schnörr
  (See online at https://doi.org/10.1109/ACPR.2015.7486610)
• (2015). „Per-Sample Kernel Adaptation for Visual Recognition and Grouping“. In: 2015 IEEE International Conference on Computer Vision, ICCV
  Antic, B. und B. Ommer
  (See online at https://doi.org/10.1109/ICCV.2015.148)
• (2015). „Probabilistic Correlation Clustering and Image Partitioning Using Perturbed Multicuts“. In: Proc. SSVM. LNCS. Springer
  Kappes, J., P. Swoboda, B. Savchynskyy, T. Hazan und C. Schnörr
  (See online at https://doi.org/10.1007/978-3-319-18461-6_19)
• (2015). „Probabilistic Graphical Models for Medical Image Segmentation“. Diss. Faculty of Mathematics und Computer Science, Heidelberg University
  Rathke, F.
  (See online at https://doi.org/10.11588/heidok.00018173)
• (2015). „Radiation Induced Chromatin Conformation Changes Analysed by Fluorescent Localization Microscopy, Statistical Physics, and Graph Theory“. In: PLOS One 10.6, e0128555
  Zhang, Y., G. Maté, P. Müller, S. Hillebrandt, M. Krufczik, M. Bach, R. Kaufmann, M. Hausmann und D. W. Heermann
  (See online at https://doi.org/10.1371/journal.pone.0128555)
• (2015). „Second Order Minimum Energy Filtering on SE(3) with Nonlinear Measurement Equations“. In: Scale Space and Variational Methods. Springer, S. 397–409
  Berger, J., A. Neufeld, F. Becker, F. Lenzen und C. Schnörr
  (See online at https://doi.org/10.1007/978-3-319-18461-6_32)
• (2015). „Shape from Texture using Locally Scaled Point Processes“. In: Image Anal. Stereol. 34.3, S. 161–170
  Didden, E.-M., T. Thorarinsdottir, A. Lenkoski und C. Schnörr
  (See online at https://doi.org/10.5566/ias.1078)
• (2015). „Solution-Driven Adaptive Total Variation Regularization“. In: Scale Space and Variational Methods. Springer International Publishing, S. 203–215
  Lenzen, F. und J. Berger
  (See online at https://doi.org/10.1007/978-3-319-18461-6_17)
• (2015). „Spatiotemporal Parsing of Motor Kinematics for Assessing Stroke Recovery“. In: Medical Image Computing and Computer-Assisted Intervention - MICCAI
  Antic, B., U. Büchler, A. Wahl, M. E. Schwab und B. Ommer
  (See online at https://doi.org/10.1007/978-3-319-24574-4_56)
• (2015). „Structures of Multivariate Dependence“. Diss. Faculty of Mathematics und Computer Science, Heidelberg University
  Edelmann, D.
  (See online at https://doi.org/10.11588/heidok.00018975)
• (2016). „A Geometric Approach to Image Labeling“. In: Proc. ECCV. Springer, S. 139–154
  Åström, F., S. Petra, B. Schmitzer und C. Schnörr
  (See online at https://doi.org/10.1007/978-3-319-46454-1_9)
• (2016). „Automated Segmentation for Connectomics Utilizing higher-Order Biological Priors“. Diss. Faculty of Physics und Astronomy, Heidelberg University
  Krasowski, N.
  (See online at https://doi.org/10.11588/heidok.00021617)
• (2016). „Automated Segmentation for Connectomics Utilizing Higher-Order Biological Priors“. Doctoral Thesis. Ruprecht-Karls-Universität Heidelberg, Faculty of Mathematics und Computer Science
  Krasowski, N. E.
  (See online at https://doi.org/10.11588/heidok.00021617)
• (2016). „CliqueCNN: Deep Unsupervised Exemplar Learning“. In: Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS)
  Bautista, M., A. Sanakoyeu, E. Sutter und B. Ommer
  (See online at https://doi.org/10.48550/arXiv.1608.08792)
• (2016). „Color Image Regularization via Channel Mixing and Half Quadratic Minimization“. In: Proc. ICIP. IEEE, S. 4007–4011
  Åström, F.
  (See online at https://doi.org/10.1109/ICIP.2016.7533112)
• (2016). „Data Adaptive Inference for Locally Stationary Processes“. Diss. Faculty of Mathematics und Computer Science, Heidelberg University
  Richter, S.
  (See online at https://doi.org/10.11588/heidok.00022308)
• (2016). „Distances, Gegenbauer Expansions, Curls, and Dimples: On Dependence Measures for Random Fields“. Diss. Faculty of Mathematics und Computer Science, Heidelberg University
  Fiedler, J.
  (See online at https://doi.org/10.11588/heidok.00022193)
• (2016). „Double-Opponent Vectorial Total Variation“. In: Proc. ECCV. Springer, S. 644–659. ISBN: 978-3-319-46475-6
  Åström, F. und C. Schnörr
  (See online at https://doi.org/10.1007/978-3-319-46475-6_40)
• (2016). „Higher-order Segmentation via Multicuts“. In: Comp. Vision Image Understanding 143, S. 104–119
  Kappes, J., M. Speth, G. Reinelt und C. Schnörr
  (See online at https://doi.org/10.1016/j.cviu.2015.11.005)
• (2016). „Joint Recursive Monocular Filtering of Camera Motion and Disparity Map“. In: 38th German Conference on Pattern Recognition. Springer
  Berger, J. und C. Schnörr
  (See online at https://doi.org/10.1007/978-3-319-45886-1_19)
• (2016). „Parametric Dictionary-Based Velocimetry for Echo PIV“. In: Proc. GCPR
  Bodnariuc, E., S. Petra, C. Poelma und C. Schnörr
  (See online at https://doi.org/10.1007/978-3-319-45886-1_27)
• (2016). „Plane Wave Acoustic Superposition for Fast Ultrasound Imaging“. In: Proc. IUS
  Bodnariuc, E., M. Schiffner, S. Petra und C. Schnörr
  (See online at https://doi.org/10.1109/ULTSYM.2016.7728894)
• (2016). „Second Order Minimum Energy Filtering“. Diss. Faculty of Mathematics und Computer Science, Heidelberg University
  Berger, J.
  (See online at https://dx.doi.org/10.11588/heidok.00022530)
• (2016). „Source Localization of Reaction- Diffusion Models for Brain Tumors“. In: Proc. GCPR. Springer, S. 414–425
  Jaroudi, R., G. Baravdish, F. Åström und B. T. Johansson
  (See online at https://doi.org/10.1007/978-3-319-45886-1_34)
• (2016). „The Assignment Manifold: A smooth model for image labeling“. In: Proc. CVPR. DIFF- CVML, S. 1–9
  Åström, F., S. Petra, B. Schmitzer und C. Schnörr
  (See online at https://doi.org/10.1109/CVPRW.2016.124)
• (2017). „A Dual Ascent Framework for Lagrangean Decomposition of Combinatorial Problems“. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  Swoboda, P., J. Kuske und B. Savchynskyy
  (See online at https://doi.org/10.1109/CVPR.2017.526)
• (2017). „A Geometric Approach for Color Image Regularization“. In: J. Comp. Vision Image Understanding 165. S. 43–59
  Åström, F. und C. Schnörr
  (See online at https://doi.org/10.1016/j.cviu.2017.10.013)
• (2017). „A Novel Convex Relaxation for Non-binary Discrete Tomography“. In: Scale Space and Variational Methods in Computer Vision. Springer, S. 235–246
  Kuske, J., P. Swoboda und S. Petra
  (See online at https://doi.org/10.1007/978-3-319-58771-4_19)
• (2017). „An objective comparison of cell-tracking algorithms“. In: Nature methods 14.12, S. 1141
  Ulman, V., M. Maška, K. Magnusson, O. Ronneberger, C. Haubold, S. Wolf, N. Harder, P. Matula, P. Matula, D. Svoboda, M. Radojevic u. a.
  (See online at https://doi.org/10.1038/nmeth.4473)
• (2017). „Compressed Motion Sensing“. In: Scale Space and Variational Methods in Computer Vision: 6th International Conference, SSVM 2017, Kolding, Denmark, June 4-8, 2017. Springer International Publishing, S. 602–613. ISBN: 978-3-319-58771-4
  Dalitz, R., S. Petra und C. Schnörr
  (See online at https://doi.org/10.1007/978-3-319-58771-4_48)
• (2017). „Deep Semantic Feature Matching“. In: Proc. CVPR
  Ufer, N. und B. Ommer
  (See online at https://doi.org/10.1109/CVPR.2017.628)
• (2017). „Distance correlation coefficients for Lancaster distributions“. In: Journal of Multivariate Analysis 154, S. 19–39
  Dueck, J., D. Edelmann und D. Richards
  (See online at https://doi.org/10.1016/j.jmva.2016.10.012)
• (2017). „Geometric Image Labeling with Global Convex Labeling Constraints“. In: Proc. EMMCVPR. LNCS. Springer
  Zern, A., K. Rohr und C. Schnörr
  (See online at https://doi.org/10.1007/978-3-319-78199-0_35)
• (2017). „Gradient Flows on a Riemannian Submanifold for Discrete Tomography“. In: Proc. GCPR
  Zisler, M., F. Savarino, S. Petra und C. Schnörr
  (See online at https://doi.org/10.1007/978-3-319-66709-6_24)
• (2017). „Graphical Model Parameter Learning by Inverse Linear Programming“. In: Proc. SSVM. Springer, S. 323–334. ISBN: 978-3-319-58771-4
  Trajkovska, V., P. Swoboda, F. Åström und S. Petra
  (See online at https://doi.org/10.1007/978-3-319-58771-4_26)
• (2017). „Graphical Model Parameter Learning by Inverse Linear Programming“. In: Scale Space and Variational Methods in Computer Vision: 6th International Conference, SSVM 2017, Kolding, Denmark, June 4-8, 2017, Proceedings. Hrsg. von F. Lauze, Y. Dong und A. B. Dahl. LNCS 10302. Springer, S. 323–334
  Trajkovska, V., P. Swoboda, F. Åström und S. Petra
  (See online at https://doi.org/10.1007/978-3-319-58771-4_26)
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