@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}
}
@article {Lempitsky2010,
	title = {Fusion moves for markov random field optimization},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {32},
	number = {8},
	year = {2010},
	pages = {1392{\textendash}1405},
	abstract = {The efficient application of graph cuts to Markov Random Fields (MRFs) with multiple discrete or continuous labels remains an open question. In this paper, we demonstrate one possible way of achieving this by using graph cuts to combine pairs of suboptimal labelings or solutions. We call this combination process the fusion move. By employing recently developed graph-cut-based algorithms (so-called QPBO-graph cut), the fusion move can efficiently combine two proposal labelings in a theoretically sound way, which is in practice often globally optimal. We demonstrate that fusion moves generalize many previous graph-cut approaches, which allows them to be used as building blocks within a broader variety of optimization schemes than were considered before. In particular, we propose new optimization schemes for computer vision MRFs with applications to image restoration, stereo, and optical flow, among others. Within these schemes the fusion moves are used 1) for the parallelization of MRF optimization into several threads, 2) for fast MRF optimization by combining cheap-to-compute solutions, and 3) for the optimization of highly nonconvex continuous-labeled MRFs with 2D labels. Our final example is a nonvision MRF concerned with cartographic label placement, where fusion moves can be used to improve the performance of a standard inference method (loopy belief propagation). {\textcopyright} 2006 IEEE.},
	keywords = {Combinatorial algorithms, Computer vision, Graph algorithms, Image restoration., Markov random fields, Motion, Stereo},
	issn = {01628828},
	doi = {10.1109/TPAMI.2009.143},
	author = {Lempitsky, Victor and Carsten Rother and Roth, Stefan and Blake, Andrew}
}
@article {Lempitsky2010a,
	title = {Fusion moves for markov random field optimization},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {32},
	number = {8},
	year = {2010},
	pages = {1392{\textendash}1405},
	abstract = {The efficient application of graph cuts to Markov Random Fields (MRFs) with multiple discrete or continuous labels remains an open question. In this paper, we demonstrate one possible way of achieving this by using graph cuts to combine pairs of suboptimal labelings or solutions. We call this combination process the fusion move. By employing recently developed graph-cut-based algorithms (so-called QPBO-graph cut), the fusion move can efficiently combine two proposal labelings in a theoretically sound way, which is in practice often globally optimal. We demonstrate that fusion moves generalize many previous graph-cut approaches, which allows them to be used as building blocks within a broader variety of optimization schemes than were considered before. In particular, we propose new optimization schemes for computer vision MRFs with applications to image restoration, stereo, and optical flow, among others. Within these schemes the fusion moves are used 1) for the parallelization of MRF optimization into several threads, 2) for fast MRF optimization by combining cheap-to-compute solutions, and 3) for the optimization of highly nonconvex continuous-labeled MRFs with 2D labels. Our final example is a nonvision MRF concerned with cartographic label placement, where fusion moves can be used to improve the performance of a standard inference method (loopy belief propagation). {\textcopyright} 2006 IEEE.},
	keywords = {Combinatorial algorithms, Computer vision, Graph algorithms, Image restoration., Markov random fields, Motion, Stereo},
	issn = {01628828},
	doi = {10.1109/TPAMI.2009.143},
	author = {Lempitsky, Victor and Carsten Rother and Roth, Stefan and Blake, Andrew}
}
@conference {Torresani2008,
	title = {Feature correspondence via graph matching: Models and global optimization},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {5303 LNCS},
	number = {PART 2},
	year = {2008},
	pages = {596{\textendash}609},
	abstract = {In this paper we present a new approach for establishing correspondences between sparse image features related by an unknown non-rigid mapping and corrupted by clutter and occlusion, such as points extracted from a pair of images containing a human figure in distinct poses. We formulate this matching task as an energy minimization problem by defining a complex objective function of the appearance and the spatial arrangement of the features. Optimization of this energy is an instance of graph matching, which is in general a NP-hard problem. We describe a novel graph matching optimization technique, which we refer to as dual decomposition (DD), and demonstrate on a variety of examples that this method outperforms existing graph matching algorithms. In the majority of our examples DD is able to find the global minimum within a minute. The ability to globally optimize the objective allows us to accurately learn the parameters of our matching model from training examples. We show on several matching tasks that our learned model yields results superior to those of state-of-the-art methods. {\textcopyright} 2008 Springer Berlin Heidelberg.},
	isbn = {3540886850},
	issn = {03029743},
	doi = {10.1007/978-3-540-88688-4-44},
	author = {Torresani, Lorenzo and Kolmogorov, Vladimir and Carsten Rother}
}
@conference {Torresani2008a,
	title = {Feature correspondence via graph matching: Models and global optimization},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {5303 LNCS},
	number = {PART 2},
	year = {2008},
	pages = {596{\textendash}609},
	abstract = {In this paper we present a new approach for establishing correspondences between sparse image features related by an unknown non-rigid mapping and corrupted by clutter and occlusion, such as points extracted from a pair of images containing a human figure in distinct poses. We formulate this matching task as an energy minimization problem by defining a complex objective function of the appearance and the spatial arrangement of the features. Optimization of this energy is an instance of graph matching, which is in general a NP-hard problem. We describe a novel graph matching optimization technique, which we refer to as dual decomposition (DD), and demonstrate on a variety of examples that this method outperforms existing graph matching algorithms. In the majority of our examples DD is able to find the global minimum within a minute. The ability to globally optimize the objective allows us to accurately learn the parameters of our matching model from training examples. We show on several matching tasks that our learned model yields results superior to those of state-of-the-art methods. {\textcopyright} 2008 Springer Berlin Heidelberg.},
	isbn = {3540886850},
	issn = {03029743},
	doi = {10.1007/978-3-540-88688-4-44},
	author = {Torresani, Lorenzo and Kolmogorov, Vladimir and Carsten Rother}
}
@conference {Lempitsky2008,
	title = {FusionFlow: Discrete-continuous optimization for optical flow estimation},
	booktitle = {26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR},
	year = {2008},
	abstract = {Accurate estimation of optical flow is a challenging task, which often requires addressing difficult energy optimization problems. To solve them, most top-performing methods rely on continuous optimization algorithms. The modeling accuracy of the energy in this case is often traded for its tractability. This is in contrast to the related problem of narrow-baseline stereo matching, where the top-performing methods employ powerful discrete optimization algorithms such as graph cuts and message-passing to optimize highly non-convex energies. In this paper, we demonstrate how similar non-convex energies can be formulated and optimized discretely in the context of optical flow estimation. Starting with a set of candidate solutions that are produced by fast continuous flow estimation algorithms, the proposed method iteratively fuses these candidate solutions by the computation of minimum cuts on graphs. The obtained continuous-valued fusion result is then further improved using local gradient descent. Experimentally, we demonstrate that the proposed energy is an accurate model and that the proposed discrete-continuous optimization scheme not only finds lower energy solutions than traditional discrete or continuous optimization techniques, but also leads to flow estimates that outperform the current state-of-the-art. {\textcopyright}2008 IEEE.},
	isbn = {9781424422432},
	doi = {10.1109/CVPR.2008.4587751},
	author = {Lempitsky, Victor and Roth, Stefan and Carsten Rother}
}
@article {Blake2007,
	title = {Fusion of stereo, colour and contrast},
	journal = {Springer Tracts in Advanced Robotics},
	volume = {28},
	year = {2007},
	issn = {16107438},
	doi = {10.1007/978-3-540-48113-3_27},
	url = {www.research.microsoft.com/vision/cambridge},
	author = {Blake, A and Criminisi, A and Cross, G and Kolmogorov, V and Carsten Rother}
}
