@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 {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}
}
