@conference {Hornacek2013,
	title = {Depth super resolution by rigid body self-similarity in 3D},
	booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
	year = {2013},
	pages = {1123{\textendash}1130},
	abstract = {We tackle the problem of jointly increasing the spatial resolution and apparent measurement accuracy of an input low-resolution, noisy, and perhaps heavily quantized depth map. In stark contrast to earlier work, we make no use of ancillary data like a color image at the target resolution, multiple aligned depth maps, or a database of high-resolution depth exemplars. Instead, we proceed by identifying and merging patch correspondences within the input depth map itself, exploiting patch wise scene self-similarity across depth such as repetition of geometric primitives or object symmetry. While the notion of {\textquoteright}single-image{\textquoteright} super resolution has successfully been applied in the context of color and intensity images, we are to our knowledge the first to present a tailored analogue for depth images. Rather than reason in terms of patches of 2D pixels as others have before us, our key contribution is to proceed by reasoning in terms of patches of 3D points, with matched patch pairs related by a respective 6 DoF rigid body motion in 3D. In support of obtaining a dense correspondence field in reasonable time, we introduce a new 3D variant of Patch Match. A third contribution is a simple, yet effective patch up scaling and merging technique, which predicts sharp object boundaries at the target resolution. We show that our results are highly competitive with those of alternative techniques leveraging even a color image at the target resolution or a database of high-resolution depth exemplars. {\textcopyright} 2013 IEEE.},
	keywords = {dense matching, depth super resolution, optimization},
	issn = {10636919},
	doi = {10.1109/CVPR.2013.149},
	author = {Horn{\'a}{\v c}ek, Michael and Rhemann, Christoph and Gelautz, Margrit and Carsten Rother}
}
@article {Hosni2013,
	title = {Fast cost-volume filtering for visual correspondence and beyond},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {35},
	number = {2},
	year = {2013},
	pages = {504{\textendash}511},
	abstract = {Many computer vision tasks can be formulated as labeling problems. The desired solution is often a spatially smooth labeling where label transitions are aligned with color edges of the input image. We show that such solutions can be efficiently achieved by smoothing the label costs with a very fast edge-preserving filter. In this paper, we propose a generic and simple framework comprising three steps: 1) constructing a cost volume, 2) fast cost volume filtering, and 3) Winner-Takes-All label selection. Our main contribution is to show that with such a simple framework state-of-the-art results can be achieved for several computer vision applications. In particular, we achieve 1) disparity maps in real time whose quality exceeds those of all other fast (local) approaches on the Middlebury stereo benchmark, and 2) optical flow fields which contain very fine structures as well as large displacements. To demonstrate robustness, the few parameters of our framework are set to nearly identical values for both applications. Also, competitive results for interactive image segmentation are presented. With this work, we hope to inspire other researchers to leverage this framework to other application areas. {\textcopyright} 2012 IEEE.},
	keywords = {Interactive Image Segmentation, optical flow, Stereo Matching},
	issn = {01628828},
	doi = {10.1109/TPAMI.2012.156},
	author = {Hosni, Asmaa and Rhemann, Christoph and Bleyer, Michael and Carsten Rother and Gelautz, Margrit}
}
@conference {Bleyer2012a,
	title = {Extracting 3D scene-consistent object proposals and depth from stereo images},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {7576 LNCS},
	number = {PART 5},
	year = {2012},
	pages = {467{\textendash}481},
	abstract = {This work combines two active areas of research in computer vision: unsupervised object extraction from a single image, and depth estimation from a stereo image pair. A recent, successful trend in unsupervised object extraction is to exploit so-called "3D scene-consistency", that is enforcing that objects obey underlying physical constraints of the 3D scene, such as occupancy of 3D space and gravity of objects. Our main contribution is to introduce the concept of 3D scene-consistency into stereo matching. We show that this concept is beneficial for both tasks, object extraction and depth estimation. In particular, we demonstrate that our approach is able to create a large set of 3D scene-consistent object proposals, by varying e.g. the prior on the number of objects. After automatically ranking the proposals we show experimentally that our results are considerably closer to ground truth than state-of-the-art techniques which either use stereo or monocular images. We envision that our method will build the front-end of a future object recognition system for stereo images. {\textcopyright} 2012 Springer-Verlag.},
	keywords = {Object Segmentation, Stereo Matching},
	isbn = {9783642337147},
	issn = {03029743},
	doi = {10.1007/978-3-642-33715-4_34},
	author = {Bleyer, Michael and Rhemann, Christoph and Carsten Rother}
}
@conference {Bleyer2012,
	title = {Extracting 3D scene-consistent object proposals and depth from stereo images},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {7576 LNCS},
	number = {PART 5},
	year = {2012},
	pages = {467{\textendash}481},
	abstract = {This work combines two active areas of research in computer vision: unsupervised object extraction from a single image, and depth estimation from a stereo image pair. A recent, successful trend in unsupervised object extraction is to exploit so-called "3D scene-consistency", that is enforcing that objects obey underlying physical constraints of the 3D scene, such as occupancy of 3D space and gravity of objects. Our main contribution is to introduce the concept of 3D scene-consistency into stereo matching. We show that this concept is beneficial for both tasks, object extraction and depth estimation. In particular, we demonstrate that our approach is able to create a large set of 3D scene-consistent object proposals, by varying e.g. the prior on the number of objects. After automatically ranking the proposals we show experimentally that our results are considerably closer to ground truth than state-of-the-art techniques which either use stereo or monocular images. We envision that our method will build the front-end of a future object recognition system for stereo images. {\textcopyright} 2012 Springer-Verlag.},
	isbn = {9783642337147},
	issn = {03029743},
	doi = {10.1007/978-3-642-33715-4_34},
	url = {http://vision.middlebury.edu/stereo/},
	author = {Bleyer, Michael and Rhemann, Christoph and Carsten Rother}
}
@article {Kohli2012,
	title = {User-centric learning and evaluation of interactive segmentation systems},
	journal = {International Journal of Computer Vision},
	volume = {100},
	number = {3},
	year = {2012},
	month = {dec},
	pages = {261{\textendash}274},
	abstract = {Many successful applications of computer vision to image or video manipulation are interactive by nature. However, parameters of such systems are often trained neglecting the user. Traditionally, interactive systems have been treated in the same manner as their fully automatic counterparts. Their performance is evaluated by computing the accuracy of their solutions under some fixed set of user interactions. In this paper, we study the problem of evaluating and learning interactive segmentation systems which are extensively used in the real world. The key questions in this context are how to measure (1) the effort associated with a user interaction, and (2) the quality of the segmentation result as perceived by the user. We conduct a user study to analyze user behavior and answer these questions. Using the insights obtained from these experiments, we propose a framework to evaluate and learn interactive segmentation systems which brings the user in the loop. The framework is based on the use of an active robot user-a simulated model of a human user. We show how this approach can be used to evaluate and learn parameters of state-of-the-art interactive segmentation systems. We also show how simulated user models can be integrated into the popular max-margin method for parameter learning and propose an algorithm to solve the resulting optimisation problem. {\textcopyright} 2012 The Author(s).},
	keywords = {Image segmentation, Interactive systems, Learning},
	issn = {09205691},
	doi = {10.1007/s11263-012-0537-4},
	author = {Kohli, Pushmeet and Nickisch, Hannes and Carsten Rother and Rhemann, Christoph}
}
@conference {He2011,
	title = {A global sampling method for alpha matting},
	booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
	year = {2011},
	pages = {2049{\textendash}2056},
	abstract = {Alpha matting refers to the problem of softly extracting the foreground from an image. Given a trimap (specifying known foreground/background and unknown pixels), a straightforward way to compute the alpha value is to sample some known foreground and background colors for each unknown pixel. Existing sampling-based matting methods often collect samples near the unknown pixels only. They fail if good samples cannot be found nearby. In this paper, we propose a global sampling method that uses all samples available in the image. Our global sample set avoids missing good samples. A simple but effective cost function is defined to tackle the ambiguity in the sample selection process. To handle the computational complexity introduced by the large number of samples, we pose the sampling task as a correspondence problem. The correspondence search is efficiently achieved by generalizing a randomized algorithm previously designed for patch matching[3]. A variety of experiments show that our global sampling method produces both visually and quantitatively high-quality matting results. {\textcopyright} 2011 IEEE.},
	isbn = {9781457703942},
	issn = {10636919},
	doi = {10.1109/CVPR.2011.5995495},
	author = {He, Kaiming and Rhemann, Christoph and Carsten Rother and Tang, Xiaoou and Sun, Jian}
}
@conference {Bleyer2011a,
	title = {PatchMatch Stereo - Stereo Matching with Slanted Support Windows},
	year = {2011},
	pages = {14.1{\textendash}14.11},
	abstract = {Common local stereo methods match support windows at integer-valued disparities. The implicit assumption that pixels within the support region have constant disparity does not hold for slanted surfaces and leads to a bias towards reconstructing frontoparallel surfaces. This work overcomes this bias by estimating an individual 3D plane at each pixel onto which the support region is projected. The major challenge of this approach is to find a pixels optimal 3D plane among all possible planes whose number is infinite. We show that an ideal algorithm to solve this problem is PatchMatch 1 that we extend to find an approximate nearest neighbor according to a plane. In addition to Patch- Matchs spatial propagation scheme, we propose (1) view propagation where planes are propagated among left and right views of the stereo pair and (2) temporal propagation where planes are propagated from preceding and consecutive frames of a video when doing temporal stereo. Adaptive support weights are used in matching cost aggregation to improve results at disparity borders. We also show that our slanted support windows can be used to compute a cost volume for global stereo methods, which allows for explicit treatment of occlusions and can handle large untextured regions. In the results we demonstrate that our method reconstructs highly slanted surfaces and achieves impressive disparity details with sub-pixel precision. In the Middlebury table, our method is currently top-performer among local methods and takes rank 2 among approximately 110 competitors if sub-pixel precision is considered.},
	doi = {10.5244/c.25.14},
	author = {Bleyer, Michael and Rhemann, Christoph 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 {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 {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 {Rhemann2009b,
	title = {A perceptually motivated online benchmark for image matting},
	booktitle = {2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009},
	volume = {2009 IEEE},
	year = {2009},
	pages = {1826{\textendash}1833},
	abstract = {The availability of quantitative online benchmarks for low-level vision tasks such as stereo and optical flow has led to significant progress in the respective fields. This paper introduces such a benchmark for image matting. There are three key factors for a successful benchmarking system: (a) a challenging, high-quality ground truth test set; (b) an online evaluation repository that is dynamically updated with new results; (c) perceptually motivated error functions. Our new benchmark strives to meet all three criteria. We evaluated several matting methods with our benchmark and show that their performance varies depending on the error function. Also, our challenging test set reveals problems of existing algorithms, not reflected in previously reported results. We hope that our effort will lead to considerable progress in the field of image matting, and welcome the reader to visit our benchmark at www.alphamatting.com. {\textcopyright} 2009 IEEE.},
	isbn = {9781424439935},
	doi = {10.1109/CVPRW.2009.5206503},
	author = {Rhemann, Christoph and Carsten Rother and Wang, Jue and Gelautz, Margrit and Kohli, Pushmeet and Rott, Pamela}
}
@conference {Rhemann2009a,
	title = {A perceptually motivated online benchmark for image matting},
	booktitle = {2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009},
	volume = {2009 IEEE},
	year = {2009},
	pages = {1826{\textendash}1833},
	abstract = {The availability of quantitative online benchmarks for low-level vision tasks such as stereo and optical flow has led to significant progress in the respective fields. This paper introduces such a benchmark for image matting. There are three key factors for a successful benchmarking system: (a) a challenging, high-quality ground truth test set; (b) an online evaluation repository that is dynamically updated with new results; (c) perceptually motivated error functions. Our new benchmark strives to meet all three criteria. We evaluated several matting methods with our benchmark and show that their performance varies depending on the error function. Also, our challenging test set reveals problems of existing algorithms, not reflected in previously reported results. We hope that our effort will lead to considerable progress in the field of image matting, and welcome the reader to visit our benchmark at www.alphamatting.com. {\textcopyright} 2009 IEEE.},
	keywords = {Benchmark, Evaluation, Matting},
	isbn = {9781424439935},
	doi = {10.1109/CVPRW.2009.5206503},
	url = {www.alphamatting.com.},
	author = {Rhemann, Christoph and Carsten Rother and Wang, Jue and Gelautz, Margrit and Kohli, Pushmeet and Rott, Pamela}
}
@conference {Rhemann2009,
	title = {A perceptually motivated online benchmark for image matting},
	booktitle = {2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009},
	volume = {2009 IEEE},
	year = {2009},
	pages = {1826{\textendash}1833},
	abstract = {The availability of quantitative online benchmarks for low-level vision tasks such as stereo and optical flow has led to significant progress in the respective fields. This paper introduces such a benchmark for image matting. There are three key factors for a successful benchmarking system: (a) a challenging, high-quality ground truth test set; (b) an online evaluation repository that is dynamically updated with new results; (c) perceptually motivated error functions. Our new benchmark strives to meet all three criteria. We evaluated several matting methods with our benchmark and show that their performance varies depending on the error function. Also, our challenging test set reveals problems of existing algorithms, not reflected in previously reported results. We hope that our effort will lead to considerable progress in the field of image matting, and welcome the reader to visit our benchmark at www.alphamatting.com. {\textcopyright} 2009 IEEE.},
	isbn = {9781424439935},
	doi = {10.1109/CVPRW.2009.5206503},
	url = {www.alphamatting.com.},
	author = {Rhemann, Christoph and Carsten Rother and Wang, Jue and Gelautz, Margrit and Kohli, Pushmeet and Rott, Pamela}
}
@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}
}
@conference {Rhemann2008a,
	title = {High resolution matting via interactive trimap segmentation},
	booktitle = {26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR},
	year = {2008},
	abstract = {We present a new approach to the matting problem which splits the task into two steps: interactive trimap extraction followed by trimap-based alpha matting. By doing so we gain considerably in terms of speed and quality and are able to deal with high resolution images. This paper has three contributions: (i) a new trimap segmentation method using parametric max-flow; (ii) an alpha matting technique for high resolution images with a new gradient preserving prior on alpha; (iii) a database of 27 ground truth alpha mattes of still objects, which is considerably larger than previous databases and also of higher quality. The database is used to train our system and to validate that both our trimap extraction and our matting method improve on state-of-the-art techniques. {\textcopyright}2008 IEEE.},
	isbn = {9781424422432},
	doi = {10.1109/CVPR.2008.4587441},
	author = {Rhemann, Christoph and Carsten Rother and Rav-Acha, Alex and Sharp, Toby}
}
@conference {Rhemann2008b,
	title = {High resolution matting via interactive trimap segmentation},
	booktitle = {26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR},
	year = {2008},
	abstract = {We present a new approach to the matting problem which splits the task into two steps: interactive trimap extraction followed by trimap-based alpha matting. By doing so we gain considerably in terms of speed and quality and are able to deal with high resolution images. This paper has three contributions: (i) a new trimap segmentation method using parametric max-flow; (ii) an alpha matting technique for high resolution images with a new gradient preserving prior on alpha; (iii) a database of 27 ground truth alpha mattes of still objects, which is considerably larger than previous databases and also of higher quality. The database is used to train our system and to validate that both our trimap extraction and our matting method improve on state-of-the-art techniques. {\textcopyright}2008 IEEE.},
	isbn = {9781424422432},
	doi = {10.1109/CVPR.2008.4587441},
	author = {Rhemann, Christoph and Carsten Rother and Rav-Acha, Alex and Sharp, Toby}
}
@conference {Rhemann2008c,
	title = {High resolution matting via interactive trimap segmentation},
	booktitle = {26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR},
	year = {2008},
	abstract = {We present a new approach to the matting problem which splits the task into two steps: interactive trimap extraction followed by trimap-based alpha matting. By doing so we gain considerably in terms of speed and quality and are able to deal with high resolution images. This paper has three contributions: (i) a new trimap segmentation method using parametric max-flow; (ii) an alpha matting technique for high resolution images with a new gradient preserving prior on alpha; (iii) a database of 27 ground truth alpha mattes of still objects, which is considerably larger than previous databases and also of higher quality. The database is used to train our system and to validate that both our trimap extraction and our matting method improve on state-of-the-art techniques. {\textcopyright}2008 IEEE.},
	isbn = {9781424422432},
	doi = {10.1109/CVPR.2008.4587441},
	author = {Rhemann, Christoph and Carsten Rother and Rav-Acha, Alex and Sharp, Toby}
}
@conference {Rhemann2008,
	title = {Improving color modeling for alpha matting},
	booktitle = {BMVC 2008 - Proceedings of the British Machine Vision Conference 2008},
	year = {2008},
	abstract = {This paper addresses the problem of extracting an alpha matte from a single photograph given a user-defined trimap. A crucial part of this task is the color modeling step where for each pixel the optimal alpha value, together with its confidence, is estimated individually. This forms the data term of the objective function. It comprises of three steps: (i) Collecting a candidate set of potential fore- and background colors; (ii) Selecting high confidence samples from the candidate set; (iii) Estimating a sparsity prior to remove blurry artifacts. We introduce novel ideas for each of these steps and show that our approach considerably improves over state-of-the-art techniques by evaluating it on a large database of 54 images with known high-quality ground truth.},
	doi = {10.5244/C.22.115},
	author = {Rhemann, Christoph and Carsten Rother and Gelautz, Margrit}
}
