@conference {Brachmann2019a,
	title = {Neural-guided RANSAC: Learning where to sample model hypotheses},
	booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
	volume = {2019-Octob},
	year = {2019},
	month = {may},
	pages = {4321{\textendash}4330},
	abstract = {We present Neural-Guided RANSAC (NG-RANSAC), an extension to the classic RANSAC algorithm from robust optimization. NG-RANSAC uses prior information to improve model hypothesis search, increasing the chance of finding outlier-free minimal sets. Previous works use heuristic side-information like hand-crafted descriptor distance to guide hypothesis search. In contrast, we learn hypothesis search in a principled fashion that lets us optimize an arbitrary task loss during training, leading to large improvements on classic computer vision tasks. We present two further extensions to NG-RANSAC. Firstly, using the inlier count itself as training signal allows us to train neural guidance in a self-supervised fashion. Secondly, we combine neural guidance with differentiable RANSAC to build neural networks which focus on certain parts of the input data and make the output predictions as good as possible. We evaluate NG-RANSAC on a wide array of computer vision tasks, namely estimation of epipolar geometry, horizon line estimation and camera re-localization. We achieve superior or competitive results compared to state-of-the-art robust estimators, including very recent, learned ones.},
	isbn = {9781728148038},
	issn = {15505499},
	doi = {10.1109/ICCV.2019.00442},
	url = {http://arxiv.org/abs/1905.04132},
	author = {Brachmann, Eric and Carsten Rother}
}
@conference {Marquez-Neila2014,
	title = {Non-parametric higher-order random fields for image segmentation},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {8694 LNCS},
	number = {PART 6},
	year = {2014},
	pages = {269{\textendash}284},
	abstract = {Models defined using higher-order potentials are becoming increasingly popular in computer vision. However, the exact representation of a general higher-order potential defined over many variables is computationally unfeasible. This has led prior works to adopt parametric potentials that can be compactly represented. This paper proposes a non-parametric higher-order model for image labeling problems that uses a patch-based representation of its potentials. We use the transformation scheme of [11, 25] to convert the higher-order potentials to a pair-wise form that can be handled using traditional inference algorithms. This representation is able to capture structure, geometrical and topological information of labels from training data and to provide more precise segmentations. Other tasks such as image denoising and reconstruction are also possible. We evaluate our method on denoising and segmentation problems with synthetic and real images. {\textcopyright} 2014 Springer International Publishing.},
	keywords = {biomedical image analysis, higher-order models, image denoising, Image segmentation, random fields, structured prediction},
	isbn = {9783319105987},
	issn = {16113349},
	doi = {10.1007/978-3-319-10599-4_18},
	author = {M{\'a}rquez-Neila, Pablo and Kohli, Pushmeet and Carsten Rother and Baumela, Luis}
}
@conference {Jancsary2012b,
	title = {Non-parametric crfs for image labeling},
	booktitle = {NIPS Workshop Modern Nonparametric Methods in Machine Learning},
	number = {2},
	year = {2012},
	pages = {1{\textendash}5},
	abstract = {We introduce a powerful non-parametric image labeling framework, Regression Tree Fields (RTFs), and discuss its application to image restoration. The conditional structure and the parameters of our model are estimated from training data so as to directly optimize for popular performance measures, resulting in excellent predictive performance at low computational cost.},
	url = {http://www.nowozin.net/sebastian/papers/jancsary2012nonparametriccrf.pdf},
	author = {Jancsary, Jeremy and Nowozin, Sebastian and Carsten Rother}
}
@conference {Singaraju2010,
	title = {New appearance models for natural image matting},
	year = {2010},
	pages = {659{\textendash}666},
	abstract = {Image matting is the task of estimating a fore- and background layer from a single image. To solve this ill posed problem, an accurate modeling of the scene{\textquoteright}s appearance is necessary. Existing methods that provide a closed form solution to this problem, assume that the colors of the foreground and background layers are locally linear. In this paper, we show that such models can be an overfit when the colors of the two layers are locally constant. We derive new closed form expressions in such cases, and show that our models are more compact than existing ones. In particular, the null space of our cost function is a subset of the null space constructed by existing approaches. We discuss the bias towards specific solutions for each formulation. Experiments on synthetic and real data confirm that our compact models estimate alpha mattes more accurately than existing techniques, without the need of additional user interaction.},
	doi = {10.1109/cvpr.2009.5206491},
	author = {Singaraju, Dheeraj and Carsten Rother and Rhemann, Christoph}
}
@conference {Singaraju2009,
	title = {New appearance models for natural image matting},
	booktitle = {2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009},
	volume = {2009 IEEE},
	year = {2009},
	pages = {659{\textendash}666},
	abstract = {Image matting is the task of estimating a fore- and background layer from a single image. To solve this ill posed problem, an accurate modeling of the scene{\textquoteright}s appearance is necessary. Existing methods that provide a closed form solution to this problem, assume that the colors of the foreground and background layers are locally linear. In this paper, we show that such models can be an overfit when the colors of the two layers are locally constant. We derive new closed form expressions in such cases, and show that our models are more compact than existing ones. In particular, the null space of our cost function is a subset of the null space constructed by existing approaches. We discuss the bias towards specific solutions for each formulation. Experiments on synthetic and real data confirm that our compact models estimate alpha mattes more accurately than existing techniques, without the need of additional user interaction. {\textcopyright} 2009 IEEE.},
	isbn = {9781424439935},
	doi = {10.1109/CVPRW.2009.5206491},
	author = {Singaraju, Dheeraj and Carsten Rother and Rhemann, Christoph}
}
@conference {Rother2002,
	title = {A new approach to vanishing point detection in architectural environments},
	booktitle = {Image and Vision Computing},
	volume = {20},
	number = {9-10},
	year = {2002},
	pages = {647{\textendash}655},
	abstract = {A man-made environment is characterized by many parallel lines and orthogonal edges. In this article, a new method for detecting the three mutually orthogonal directions of such an environment is presented. Since real-time performance is not necessary for architectural applications, such as building reconstruction, a computationally intensive approach was chosen. However, this enables us to avoid one fundamental error of most other existing techniques. Compared to theirs, our approach is furthermore more rigorous, since all conditions given by three mutually orthogonal directions are identified and utilized. We assume a partly calibrated camera with unknown focal length and unknown principal point. By examining these camera parameters, which can be determined from orthogonal directions, falsely detected vanishing points may be rejected. {\textcopyright} 2002 Elsevier Science B.V. All rights reserved.},
	keywords = {Architecture, Camera calibration, Geometric constraints, Vanishing lines, Vanishing points},
	issn = {02628856},
	doi = {10.1016/S0262-8856(02)00054-9},
	author = {Carsten Rother}
}
