@conference {Tourani2020,
	title = {Taxonomy of Dual Block-Coordinate Ascent Methods for Discrete Energy Minimization},
	booktitle = {AISTATS 2020},
	year = {2020},
	abstract = {We consider the maximum-a-posteriori inference problem in discrete graphical models and study solvers based on the dual block-coordinate ascent rule. We map all existing solvers in a single framework, allowing for a better understanding of their design principles. We theoretically show that some block-optimizing updates are sub-optimal and how to strictly improve them. On a wide range of problem instances of varying graph connec-tivity, we study the performance of existing solvers as well as new variants that can be obtained within the framework. As a result of this exploration we build a new state-of-the art solver, performing uniformly better on the whole range of test instances.},
	url = {https://gitlab.com/},
	author = {Tourani, Siddharth and Shekhovtsov, Alexander and Carsten Rother and Savchynskyy, Bogdan}
}
@proceedings {6267,
	title = {Trust your Model: Light Field Depth Estimation with inline Occlusion Handling},
	year = {2018},
	doi = {10.1109/CVPR.2018.00476},
	author = {Hendrik Schilling and Maximilian Diebold and Carsten Rother and Bernd J{\"a}hne}
}
@conference {Schilling2018,
	title = {Trust your Model: Light Field Depth Estimation with Inline Occlusion Handling},
	booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
	year = {2018},
	pages = {4530{\textendash}4538},
	abstract = {We address the problem of depth estimation from light-field images. Our main contribution is a new way to handle occlusions which improves general accuracy and quality of object borders. In contrast to all prior work we work with a model which directly incorporates both depth and occlusion, using a local optimization scheme based on the PatchMatch algorithm. The key benefit of this joint approach is that we utilize all available data, and not erroneously discard valuable information in pre-processing steps. We see the benefit of our approach not only at improved object boundaries, but also at smooth surface reconstruction, where we outperform even methods which focus on good surface regularization. We have evaluated our method on a public light-field dataset, where we achieve state-of-the-art results in nine out of twelve error metrics, with a close tie for the remaining three.},
	isbn = {9781538664209},
	issn = {10636919},
	doi = {10.1109/CVPR.2018.00476},
	author = {Schilling, Hendrik and Maximilian Diebold and Carsten Rother and Bernd J{\"a}hne}
}
@conference {Glocker2010,
	title = {TriangleFlow: Optical flow with triangulation-based higher-order likelihoods},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {6313 LNCS},
	number = {PART 3},
	year = {2010},
	pages = {272{\textendash}285},
	abstract = {We use a simple yet powerful higher-order conditional random field (CRF) to model optical flow. It consists of a standard photo-consistency cost and a prior on affine motions both modeled in terms of higher-order potential functions. Reasoning jointly over a large set of unknown variables provides more reliable motion estimates and a robust matching criterion. One of the main contributions is that unlike previous region-based methods, we omit the assumption of constant flow. Instead, we consider local affine warps whose likelihood energy can be computed exactly without approximations. This results in a tractable, so-called, higher-order likelihood function. We realize this idea by employing triangulation meshes which immensely reduce the complexity of the problem. Optimization is performed by hierarchical fusion moves and an adaptive mesh refinement strategy. Experiments show that we achieve high-quality motion fields on several data sets including the Middlebury optical flow database. {\textcopyright} 2010 Springer-Verlag.},
	isbn = {364215557X},
	issn = {16113349},
	doi = {10.1007/978-3-642-15558-1_20},
	url = {http://vision.middlebury.edu/flow/},
	author = {Glocker, Ben and Heibel, T. Hauke and Navab, Nassir and Kohli, Pushmeet and Carsten Rother}
}
@article {Shotton2009,
	title = {TextonBoost for image understanding: Multi-class object recognition and segmentation by jointly modeling texture, layout, and context},
	journal = {International Journal of Computer Vision},
	volume = {81},
	number = {1},
	year = {2009},
	pages = {2{\textendash}23},
	abstract = {This paper details a new approach for learning a discriminative model of object classes, incorporating texture, layout, and context information efficiently. The learned model is used for automatic visual understanding and semantic segmentation of photographs. Our discriminative model exploits texture-layout filters, novel features based on textons, which jointly model patterns of texture and their spatial layout. Unary classification and feature selection is achieved using shared boosting to give an efficient classifier which can be applied to a large number of classes. Accurate image segmentation is achieved by incorporating the unary classifier in a conditional random field, which (i) captures the spatial interactions between class labels of neighboring pixels, and (ii) improves the segmentation of specific object instances. Efficient training of the model on large datasets is achieved by exploiting both random feature selection and piecewise training methods. High classification and segmentation accuracy is demonstrated on four varied databases: (i) the MSRC 21-class database containing photographs of real objects viewed under general lighting conditions, poses and viewpoints, (ii) the 7-class Corel subset and (iii) the 7-class Sowerby database used in He et al. (Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 695-702, June 2004), and (iv) a set of video sequences of television shows. The proposed algorithm gives competitive and visually pleasing results for objects that are highly textured (grass, trees, etc.), highly structured (cars, faces, bicycles, airplanes, etc.), and even articulated (body, cow, etc.). {\textcopyright} 2007 Springer Science+Business Media, LLC.},
	keywords = {Boosting, Conditional random field, Context, image understanding, Layout, Object recognition, Piecewise training, Segmentation, Semantic image segmentation, Textons, Texture},
	issn = {09205691},
	doi = {10.1007/s11263-007-0109-1},
	url = {http://jamie.shotton.org/work/code.html},
	author = {Shotton, Jamie and Winn, John and Carsten Rother and Criminisi, Antonio}
}
@conference {Shotton2006,
	title = {TextonBoost: Joint appearance, shape and context modeling for multi-class object recognition and segmentation},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {3951 LNCS},
	year = {2006},
	pages = {1{\textendash}15},
	abstract = {This paper proposes a new approach to learning a discriminative model of object classes, incorporating appearance, shape and context information efficiently. The learned model is used for automatic visual recognition and semantic segmentation of photographs. Our discriminative model exploits novel features, based on textons, which jointly model shape and texture. Unary classification and feature selection is achieved using shared boosting to give an efficient classifier which can be applied to a large number of classes. Accurate image segmentation is achieved by incorporating these classifiers in a conditional random field. Efficient training of the model on very large datasets is achieved by exploiting both random feature selection and piecewise training methods. High classification and segmentation accuracy are demonstrated on three different databases: i) our own 21-object class database of photographs of real objects viewed under general lighting conditions, poses and viewpoints, ii) the 7-class Corel subset and iii) the 7-class Sowerby database used in [1]. The proposed algorithm gives competitive results both for highly textured (e.g. grass, trees), highly structured (e.g. cars, faces, bikes, aeroplanes) and articulated objects (e.g. body, cow). {\textcopyright} Springer-Verlag Berlin Heidelberg 2006.},
	keywords = {aeroplanes) and articulated objects (eg body, bikes, cow), faces, g grass, highly structured (eg cars, trees)},
	isbn = {3540338322},
	issn = {16113349},
	doi = {10.1007/11744023_1},
	url = {http://research.microsoft.com/vision/cambridge/recognition/.},
	author = {Shotton, Jamie and Winn, John and Carsten Rother and Criminisi, Antonio}
}
