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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.

Related publications

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