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Image Segmentation

Contact: Claudia Niewenhuis, Maria Klodt

Image segmentation aims at partitioning an image into n disjoint regions. Since this problem is highly ambiguous additional information is indispensible. This can be given as user input, e.g. scribbles on the image, additional constraints such as the center of gravity and the major axes of the object or learned from a given database. We formulate mostly convex energy functionals to solve this problem.

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Book Chapters Journal Articles Conference and Workshop Papers Other Publications 
Book Chapters
2013
[]Moment Constraints in Convex Optimization for Segmentation and Tracking (M. Klodt, F. Steinbruecker and D. Cremers), Chapter in Advanced Topics in Computer Vision, Springer, 2013.  [bibtex] [pdf]
2011
[]Image Segmentation with Shape Priors: Explicit Versus Implicit Representations (D. Cremers), Chapter in Handbook of Mathematical Methods in Imaging, Springer, 2011.  [bibtex] [pdf]
Book Chapters Journal Articles Conference and Workshop Papers Other Publications 
Journal Articles
2016
[]q-Space Deep Learning: Twelve-Fold Shorter and Model-Free Diffusion MRI Scans (V. Golkov, A. Dosovitskiy, J. I. Sperl, M. I. Menzel, M. Czisch, P. Sämann, T. Brox and D. Cremers), In IEEE Transactions on Medical Imaging, volume 35, 2016. Special Issue on Deep Learning [bibtex] [pdf]Special Issue on Deep Learning
2013
[]A Survey and Comparison of Discrete and Continuous Multi-label Optimization Approaches for the Potts Model (C. Nieuwenhuis, E. Toeppe and D. Cremers), In International Journal of Computer Vision, volume 104, 2013. (Code available) [bibtex] [pdf]
[]Spatially Varying Color Distributions for Interactive Multi-Label Segmentation (C. Nieuwenhuis and D. Cremers), In IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 35, 2013. (Code available) [bibtex] [pdf]
2012
[]Optimal Solutions for Semantic Image Decomposition (D. Cremers), In Image and Vision Computing, volume 30, 2012.  [bibtex] [pdf]
[]A linear framework for region-based image segmentation and inpainting involving curvature penalization (T. Schoenemann, F. Kahl, S. Masnou and D. Cremers), In International Journal of Computer Vision, volume 99, 2012.  [bibtex] [pdf]
Book Chapters Journal Articles Conference and Workshop Papers Other Publications 
Conference and Workshop Papers
2018
[]q-Space Novelty Detection in Short Diffusion MRI Scans of Multiple Sclerosis (V. Golkov, A. Vasilev, F. Pasa, I. Lipp, W. Boubaker, E. Sgarlata, F. Pfeiffer, V. Tomassini, D. K. Jones and D. Cremers), In International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, 2018.  [bibtex] [pdf]
2017
[]Multiframe Scene Flow with Piecewise Rigid Motion (Golyanik, V., Kim, K., Maier, R., Nießner, M., Stricker, D. and Kautz, J.), In International Conference on 3D Vision (3DV), 2017. ([slides] [poster] [supplementary]) [bibtex] [pdf]
[]Multi-View Deep Learning for Consistent Semantic Mapping with RGB-D Cameras (L. Ma, J. Stueckler, C. Kerl and D. Cremers), In International Conference on Intelligent Robots and Systems (IROS), 2017.  [bibtex] [pdf]
2016
[]FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-based CNN Architecture (C. Hazirbas, L. Ma, C. Domokos and D. Cremers), In Asian Conference on Computer Vision, 2016. ([code]) [bibtex] [pdf]
[]CPA-SLAM: Consistent Plane-Model Alignment for Direct RGB-D SLAM (L. Ma, C. Kerl, J. Stueckler and D. Cremers), In International Conference on Robotics and Automation (ICRA), 2016.  [bibtex] [pdf]
[]Model-Free Novelty-Based Diffusion MRI (V. Golkov, T. Sprenger, J. I. Sperl, M. I. Menzel, M. Czisch, P. Sämann and D. Cremers), In IEEE International Symposium on Biomedical Imaging (ISBI), 2016.  [bibtex] [pdf]
2015
[]Video Segmentation with Just a Few Strokes (N. Nagaraja, F. R. Schmidt and T. Brox), In IEEE International Conference on Computer Vision (ICCV), 2015.  [bibtex] [pdf]
[]Entropy Minimization for Convex Relaxation Approaches (M. Souiai, M. R. Oswald, Y. Kee, J. Kim, M. Pollefeys and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2015. (accepted) [bibtex] [pdf]
[]Motion Cooperation: Smooth Piece-Wise Rigid Scene Flow from RGB-D Images (M. Jaimez, M. Souiai, J. Stueckler, J. Gonzalez-Jimenez and D. Cremers), In Proc. of the Int. Conference on 3D Vision (3DV), 2015. ([video]) [bibtex] [pdf]
[]q-Space Deep Learning for Twelve-Fold Shorter and Model-Free Diffusion MRI Scans (V. Golkov, A. Dosovitskiy, P. Sämann, J. I. Sperl, T. Sprenger, M. Czisch, M. I. Menzel, P. A. Gómez, A. Haase, T. Brox and D. Cremers), In Medical Image Computing and Computer Assisted Intervention (MICCAI), 2015.  [bibtex] [pdf]
[]Using Diffusion and Structural MRI for the Automated Segmentation of Multiple Sclerosis Lesions (P.A. Gómez, T. Sprenger, A.A. López, J.I. Sperl, B. Fernandez, M. Molina-Romero, X. Liu, V. Golkov, M. Czisch, P. Saemann, M.I. Menzel and B.H. Menze), In International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, 2015.  [bibtex]
[]A Fast Projection Method for Connectivity Constraints in Image Segmentation (J. Stühmer and D. Cremers), In Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR) (X.-C. Tai, E. Bae, T. F. Chan, M. Lysaker, eds.), 2015.  [bibtex] [pdf]
[]Optimizing the Relevance-Redundancy Tradeoff for Efficient Semantic Segmentation (C. Hazirbas, J. Diebold and D. Cremers), In Scale Space and Variational Methods in Computer Vision (SSVM), 2015. ([code]) [bibtex] [doi] [pdf]Oral Presentation
[]Interactive Multi-label Segmentation of RGB-D Images (J. Diebold, N. Demmel, C. Hazirbas, M. Möller and D. Cremers), In Scale Space and Variational Methods in Computer Vision (SSVM), 2015. ([code]) [bibtex] [doi] [pdf]
2014
[]Co-Sparse Textural Similarity for Interactive Segmentation (C. Nieuwenhuis, S. Hawe, M. Kleinsteuber and D. Cremers), In European Conference on Computer Vision (ECCV), 2014.  [bibtex] [pdf]
[]Flow and Color Inpainting for Video Completion (M. Strobel, J. Diebold and D. Cremers), In German Conference on Pattern Recognition (GCPR), 2014.  [bibtex] [doi] [pdf]Oral Presentation
2013
[]Fast Trust Region for Segmentation (L. Gorelick, F. R. Schmidt and Y. Boykov), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013.  [bibtex] [pdf]
[]Tree Shape Priors with Connectivity Constraints using Convex Relaxation on General Graphs (J. Stühmer, P. Schröder and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2013.  [bibtex] [pdf]Oral Presentation
[]Proportion Priors for Image Sequence Segmentation (C. Nieuwenhuis, E. Strekalovskiy and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2013. (oral presentation) [bibtex] [pdf] [video]
[]Total Variation Regularization for Functions with Values in a Manifold (J. Lellmann, E. Strekalovskiy, S. Koetter and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2013.  [bibtex] [pdf]
[]Volume Constraints for Single View Reconstruction (E. Toeppe, C. Nieuwenhuis and D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013.  [bibtex]
[]Proximity Priors for Variational Semantic Segmentation and Recognition (J. Bergbauer, C. Nieuwenhuis, M. Souiai and D. Cremers), In ICCV Workshop on Graphical Models for Scene Understanding, 2013.  [bibtex] [doi] [pdf]
[]Convex Optimization for Scene Understanding (M. Souiai, C. Nieuwenhuis, E. Strekalovskiy and D. Cremers), In ICCV Workshop on Graphical Models for Scene Understanding, 2013.  [bibtex] [pdf]
2012
[]Segmentation with non-linear regional constraints via line-search cuts (L. Gorelick, F. R. Schmidt, Y. Boykov, A. Delong and A. Ward), In European Conference on Computer Vision (ECCV), Springer, volume 7572, 2012.  [bibtex] [pdf]
[]Hausdorff Distance Constraint for Multi-Surface Segmentation (F. R. Schmidt and Y. Boykov), In European Conference on Computer Vision (ECCV), Springer, volume 7572, 2012.  [bibtex] [pdf]
[]Wehrli 2.0: An Algorithm for ”Tidying up Art” (N. Ufer, M. Souiai and D. Cremers), In VISART “Where Computer Vision Meets Art” workshop, ECCV 2012, Springer, 2012.  [bibtex] [pdf]
[]Nonmetric Priors for Continuous Multilabel Optimization (E. Strekalovskiy, C. Nieuwenhuis and D. Cremers), In European Conference on Computer Vision (ECCV), Springer, 2012.  [bibtex] [pdf]
2011
[]Interactive Segmentation with Super-Labels (A. Delong, L. Gorelick, F. R. Schmidt, O. Veksler and Y. Boykov), In Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), Springer, volume 6819, 2011.  [bibtex] [pdf]
[]A Convex Framework for Image Segmentation with Moment Constraints (M. Klodt and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2011.  [bibtex] [pdf]
[]Space-Varying Color Distributions for Interactive Multiregion Segmentation: Discrete versus Continuous Approaches (C. Nieuwenhuis, E. Toeppe and D. Cremers), In Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), 2011.  [bibtex] [pdf]
2010
[]Interactive Motion Segmentation (C. Nieuwenhuis, B. Berkels and M. Rumpf), In Pattern Recognition (Proc. DAGM), Springer, volume 6376, 2010.  [bibtex] [pdf]
2007
[]A probabilistic level set formulation for interactive organ segmentation (D. Cremers, O. Fluck, M. Rousson and S. Aharon), In Proc. of the SPIE Medical Imaging, 2007.  [bibtex] [pdf]
2006
[]4D shape priors for level set segmentation of the left myocardium in SPECT sequences (T. Kohlberger, D. Cremers, M. Rousson and R. Ramaraj), In Medical Image Computing and Computer Assisted Intervention (MICCAI), volume 4190, 2006.  [bibtex] [pdf]
[]GPU histogram computation (O. Fluck, S. Aharon, D. Cremers and M. Rousson), In ACM SIGGRAPH posters and demos, 2006.  [bibtex] [pdf]
[]Statistical priors for combinatorial optimization: efficient solutions via Graph Cuts (D. Cremers and L. Grady), In European Conference on Computer Vision (ECCV) (A. Leonardis, H. Bischof, A. Pinz, eds.), Springer, volume 3953, 2006.  [bibtex] [pdf]
[]Variational motion segmentation with level sets (T. Brox, A. Bruhn and J. Weickert), In European Conference on Computer Vision (ECCV) (A. Leonardis, H. Bischof, A. Pinz, eds.), Springer, volume 3951, 2006.  [bibtex] [pdf]
2005
[]Efficient kernel density estimation of shape and intensity priors for level set segmentation (M. Rousson and D. Cremers), In Medical Image Computing and Computer Assisted Intervention (MICCAI), volume 1, 2005.  [bibtex] [pdf]
[]One-shot integral invariant shape priors for variational segmentation (S. Manay, D. Cremers, A. J. Yezzi and S. Soatto), In Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR) (A. Rangarajan, B. Vemuri, A. L. Yuille, eds.), volume 3757, 2005.  [bibtex]
2002
[]Statistical shape knowledge in variational motion segmentation (D. Cremers and C. Schnörr), In 1st Internat. Workshop on Generative-Model-Based Vision (A. Pece, Y. N. Wu, R. Larsen, eds.), Univ. of Copenhagen, 2002. (http://www.diku.dk/research/published/2002/02-01) [bibtex]
Book Chapters Journal Articles Conference and Workshop Papers Other Publications 
Other Publications
2014
[]Feature Selection and Learning for Semantic Segmentation (Caner Hazirbas), Master's thesis, Technical University Munich, 2014.  [bibtex] [pdf]
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