@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 {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 {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 {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 {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 {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 {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}
}
