@article {Hullin2017,
	title = {Semantic-Aware Image Smoothing},
	journal = {Vision, Modeling, and Visualization},
	year = {2017},
	abstract = {Structure-preserving image smoothing aims to extract semantically meaningful image structure from texture, which is one of the fundamental problems in computer vision and graphics. However, it is still not clear how to define this concept. On the other hand, semantic image labeling has achieved significant progress recently and has been widely used in many computer vision tasks. In this paper, we present an interesting observation, i.e. high-level semantic image labeling information can provide a meaningful structure prior naturally. Based on this observation, we propose a simple and yet effective method, which we term semantic smoothing, by exploiting the semantic information to accomplish semantically structure-preserving image smoothing. We show that our approach outperforms the state-of-the-art approaches in texture removal by considering the semantic infor-mation for structure preservation. Also, we apply our approach to three applications: detail enhancement, edge detection, and image segmentation, and we demonstrate the effectiveness of our semantic smoothing method on these problems.},
	keywords = {Enhancement{\textemdash} Smoothing, I43 [Image Processing and Computer Vision]},
	url = {https://hci.iwr.uni-heidelberg.de/vislearn/wp-content/uploads/2014/08/paper1024_CRC.pdf},
	author = {Hullin, M and Klein, R and Schultz, T and Yao, A and Weihao Li and Omid Hosseini Jafari and Carsten Rother}
}
@conference {Sellent2016,
	title = {Stereo video deblurring},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {9906 LNCS},
	year = {2016},
	pages = {558{\textendash}575},
	abstract = {Videos acquired in low-light conditions often exhibit motion blur, which depends on the motion of the objects relative to the camera. This is not only visually unpleasing, but can hamper further processing. With this paper we are the first to show how the availability of stereo video can aid the challenging video deblurring task.We leverage 3D scene flow, which can be estimated robustly even under adverse conditions. We go beyond simply determining the object motion in two ways: First, we show how a piecewise rigid 3D scene flow representation allows to induce accurate blur kernels via local homographies. Second, we exploit the estimated motion boundaries of the 3D scene flow to mitigate ringing artifacts using an iterative weighting scheme. Being aware of 3D object motion, our approach can deal robustly with an arbitrary number of independently moving objects. We demonstrate its benefit over state-ofthe- art video deblurring using quantitative and qualitative experiments on rendered scenes and real videos.},
	keywords = {Object motion blur, Scene flow, Spatially-variant deblurring},
	isbn = {9783319464749},
	issn = {16113349},
	doi = {10.1007/978-3-319-46475-6_35},
	author = {Sellent, Anita and Carsten Rother and Roth, Stefan}
}
@techreport {Sellent,
	title = {Stereo Video Deblurring-Supplemental Material},
	year = {2016},
	abstract = {One of the key contributions in our stereo video deblurring is to employ 3D scene flow to induce blur kernels based on homographies. As the difference to inducing blur kernels from an optical flow field may seem subtle but increases deblurring performance considerably, we schematically illustrate the difference of the two ways of generating blur kernels in Fig. 10. (a) 3D motion (b) Image plane projection: Blur kernels from linear displacements (c) Image plane projection: Blur kernels from homographies Fig. 10. Inducing blur matrices: (a) Assume that 3D point P moves with a constant rigid body motion in 3D, e. g. with a yaw motion. The projection of this motion to the image plane (blue) is a circular trajectory. The corresponding 2D ground truth displacement (yellow), however, is a vector in the image plane that connects start and end point of the motion during a time interval. (b) Using optical flow, the blur kernel at a location z is approximated by identifying all pixels that, according to their spatially-variant displacement, pass through z during the time interval. The image content at point x is correctly identified as passing through z. The image content at point y is not identified correctly as its 2D displacement passes z at a distance. Instead, the image content at point{\textasciicircum}ypoint{\textasciicircum} point{\textasciicircum}y is erroneously identified as passing through z, even though{\textasciicircum}ythough{\textasciicircum} though{\textasciicircum}y has a different distance to the rotation center than z. This results in the distorted kernels shown in Fig. 3b. (c) Assuming 3D points in the vicinity of P to form a plane, we employ 3D homographies to generate blur kernels. The blur kernel at location z is thus formed by the image points whose trajectory, according to the homography, passes through z during the time interval. Consequently, y is correctly identified as passing through z, leading to the kernels shaped as circular arcs shown in Fig. 3a},
	author = {Sellent, Anita and Carsten Rother and Roth, Stefan}
}
@conference {Zouhar2015,
	title = {Semantic 3-D labeling of ear implants using a global parametric transition prior},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {9350},
	year = {2015},
	pages = {177{\textendash}184},
	abstract = {In this work we consider the problem of sematic part-labeling of 3-D meshesof ear implants. This is a challenging problem and automatic solutions are of high practical relevance, since they help to automate the design of hearing aids. The contribution of this work is a new framework which outperforms existing approaches for this task. To achieve the boost in performance we introduce the new concept of a global parametric transition prior. To our knowledge, this is the first time that such a generic prior is used for 3-D mesh processing, and it may be found useful for a large class of 3-D meshes. To foster more research on the important topic of ear implant labeling, we collected a large data set of 3-D meshes, with associated ground truth labels, which we will make publicly available.},
	isbn = {9783319245706},
	issn = {16113349},
	doi = {10.1007/978-3-319-24571-3_22},
	author = {Zouhar, Alexander and Carsten Rother and Fuchs, Siegfried}
}
@conference {Hornacek2014a,
	title = {SphereFlow: 6 DoF scene flow from RGB-D pairs},
	booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
	year = {2014},
	pages = {3526{\textendash}3533},
	abstract = {We take a new approach to computing dense scene flow between a pair of consecutive RGB-D frames. We exploit the availability of depth data by seeking correspondences with respect to patches specified not as the pixels inside square windows, but as the 3D points that are the inliers of spheres in world space. Our primary contribution is to show that by reasoning in terms of such patches under 6 DoF rigid body motions in 3D, we succeed in obtaining compelling results at displacements large and small without relying on either of two simplifying assumptions that pervade much of the earlier literature: brightness constancy or local surface planarity. As a consequence of our approach, our output is a dense field of 3D rigid body motions, in contrast to the 3D translations that are the norm in scene flow. Reasoning in our manner additionally allows us to carry out occlusion handling using a 6 DoF consistency check for the flow computed in both directions and a patchwise silhouette check to help reason about alignments in occlusion areas, and to promote smoothness of the flow fields using an intuitive local rigidity prior. We carry out our optimization in two steps, obtaining a first correspondence field using an adaptation of PatchMatch, and subsequently using alpha-expansion to jointly handle occlusions and perform regularization. We show attractive flow results on challenging synthetic and real-world scenes that push the practical limits of the aforementioned assumptions.},
	isbn = {9781479951178},
	issn = {10636919},
	doi = {10.1109/CVPR.2014.451},
	author = {Horn{\'a}{\v c}ek, Michael and Fitzgibbon, Andrew and Carsten Rother}
}
@article {Rother2011a,
	title = {Sparse Higher Order Functions of Discrete Variables{\textendash}-Representation and Optimization},
	journal = {research.microsoft.com},
	volume = {45},
	year = {2011},
	abstract = {Higher order energy functions have the ability to encode high level structural dependencies between pix-els, which have been shown to be extremely powerful for image labeling problems. Their use, however , is severely hampered in practice by the intractable complexity of representing and minimizing such functions. We observed that higher order functions encountered in computer vision are very often "sparse", i.e. many labelings of a higher order clique are equally unlikely and hence have the same high cost. In this paper, we address the problem of minimizing such sparse higher order energy functions. Our method works by transforming the problem into an equivalent quadratic function minimization problem. The resulting quadratic function can be minimized using popular message passing or graph cut based algorithms for MAP inference. Although this is primarily a theoretical paper, we also show how labeling problems such as texture denoising and inpainting can be formulated using sparse higher order energy functions. We demonstrate experimentally that for some challenging tasks our formulation is able to out-perform various state-of-the art techniques, especially the well-known patch-based approach of Freeman et al. [11]. Given the broad use of patch-based models in computer vision, we believe that our contributions will be applicable in many problem domains.},
	url = {http://research.microsoft.com/pubs/147370/RotherKohli-SparseHigherOrder.pdf},
	author = {Carsten Rother}
}
@conference {Mansfield2010,
	title = {Scene carving: Scene consistent image retargeting},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {6311 LNCS},
	number = {PART 1},
	year = {2010},
	pages = {143{\textendash}156},
	abstract = {Image retargeting algorithms often create visually disturbing distortion. We introduce the property of scene consistency, which is held by images which contain no object distortion and have the correct object depth ordering. We present two new image retargeting algorithms that preserve scene consistency. These algorithms make use of a user-provided relative depth map, which can be created easily using a simple GrabCut-style interface. Our algorithms generalize seam carving. We decompose the image retargeting procedure into (a) removing image content with minimal distortion and (b) re-arrangement of known objects within the scene to maximize their visibility. Our algorithms optimize objectives (a) and (b) jointly. However, they differ considerably in how they achieve this. We discuss this in detail and present examples illustrating the rationale of preserving scene consistency in retargeting. {\textcopyright} 2010 Springer-Verlag.},
	isbn = {3642155480},
	issn = {16113349},
	doi = {10.1007/978-3-642-15549-9_11},
	url = {www.fujifilm.com/products/3d/camera/finepix_},
	author = {Mansfield, Alex and Gehler, Peter and Van Gool, Luc and Carsten Rother}
}
@conference {Rhemann2010,
	title = {A spatially varying PSF-based prior for alpha matting},
	booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
	year = {2010},
	pages = {2149{\textendash}2156},
	abstract = {In this paper we considerably improve on a state-of-theart alpha matting approach by incorporating a new prior which is based on the image formation process. In particular, we model the prior probability of an alpha matte as the convolution of a high-resolution binary segmentation with the spatially varying point spread function (PSF) of the camera. Our main contribution is a new and efficient deconvolution approach that recovers the prior model, given an approximate alpha matte. By assuming that the PSF is a kernel with a single peak, we are able to recover the binary segmentation with an MRF-based approach, which exploits flux and a new way of enforcing connectivity. The spatially varying PSF is obtained via a partitioning of the image into regions of similar defocus. Incorporating our new prior model into a state-of-the-art matting technique produces results that outperform all competitors, which we confirm using a publicly available benchmark. {\textcopyright}2010 IEEE.},
	isbn = {9781424469840},
	issn = {10636919},
	doi = {10.1109/CVPR.2010.5539894},
	author = {Rhemann, Christoph and Carsten Rother and Kohli, Pushmeet and Gelautz, Margrit}
}
@conference {Bleyer2010,
	title = {Surface stereo with soft segmentation},
	booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
	year = {2010},
	pages = {1570{\textendash}1577},
	abstract = {This paper proposes a new stereo model which encodes the simple assumption that the scene is composed of a few, smooth surfaces. A key feature of our model is the surface-based representation, where each pixel is assigned to a 3D surface (planes or B-splines). This representation enables several important contributions: Firstly, we formulate a higher-order prior which states that pixels of similar appearance are likely to belong to the same 3D surface. This enables to incorporate the very popular color segmentation constraint in a soft and principled way. Secondly, we use a global MDL prior to penalize the number of surfaces. Thirdly, we are able to incorporate, in a simple way, a prior which favors low curvature surfaces. Fourthly, we improve the asymmetric occlusion model by disallowing pixels of the same surface to occlude each other. Finally, we use the known fusion move approach which enables a powerful optimization of our model, despite the infinite number of possible labelings (surfaces). {\textcopyright}2010 IEEE.},
	isbn = {9781424469840},
	issn = {10636919},
	doi = {10.1109/CVPR.2010.5539783},
	author = {Bleyer, Michael and Carsten Rother and Kohli, Pushmeet}
}
@conference {Bleyer2009,
	title = {A stereo approach that handles the matting problem via imagewarping},
	booktitle = {2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009},
	volume = {2009 IEEE},
	year = {2009},
	pages = {501{\textendash}508},
	abstract = {We propose an algorithm that simultaneously extracts disparities and alpha matting information given a stereo image pair. Our method divides the reference image into a set of overlapping, partially transparent color segments. Each segment pixel is assigned an alpha value and color. The disparity inside the segment is modeled via a plane. The goodness of alphas, colors and disparity planes is measured by a new energy function. Its basic idea is to use the three parameters for generating artificial views representing the left and right images. If alphas, colors and disparity planes are correct, these artificial images should be very similar to the real ones. For generating the artificial right view, we warp all pixels of the left into the geometry of the right image using the disparity planes. We introduce the assumption of constant solidity in order to correctly model how pixels{\textquoteright} alpha values are affected by the warping operation. Experimental results on the Middlebury set show that our algorithm gives good results in comparison to the state-of-the-art in stereo matching. {\textcopyright}2009 IEEE.},
	keywords = {Alpha Matting, Stereo Matching, Stereo Matting},
	isbn = {9781424439935},
	doi = {10.1109/CVPRW.2009.5206656},
	author = {Bleyer, Michael and Gelautz, Margrit and Carsten Rother and Rhemann, Christoph}
}
@techreport {Singaraju,
	title = {Supplementary material for New Appearance Models for Image Matting},
	year = {2009},
	author = {Singaraju, D and Carsten Rother and Rhemann, C}
}
