~gerald-mwangi/+junk/Thesis

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@Article{koetheRieszTrans,
  Title                    = {Riesz-Transforms vs. Derivatives: On the Relationship Between the Boundary Tensor and the Energy Tensor},
  Author                   = {k\"{o}the, Ulrich and Felsberg, Michael},
  Journal                  = {Scale Space and PDE Methods in Computer Vision},
  Year                     = {2005},
  Pages                    = {179-191},
  Volume                   = {3459},

  Owner                    = {zickzack},
  Timestamp                = {2011.03.23}
}

@Book{adrian2010particle,
  Title                    = {Particle image velocimetry},
  Author                   = {Adrian, R. and Westerweel, J.},
  Publisher                = {Cambridge University Press},
  Year                     = {2010}
}

@Article{AgarwalSnavelyRomeInADay,
  Title                    = {Building rome in a day},
  Author                   = {Agarwal, S. and Furukawa, Y. and Snavely, N. and Simon, I. and Curless, B. and Seitz, S. M. and Szeliski, R.},
  Journal                  = {Communications of the ACM},
  Year                     = {2011},
  Number                   = {10},
  Pages                    = {105--112},
  Volume                   = {54},

  Owner                    = {gmwangi},
  Publisher                = {ACM},
  Timestamp                = {2015.02.05}
}

@Article{AmbrosioApproxFunctionalTConverg,
  Title                    = {Approximation of functional depending on jumps by elliptic functional via t-convergence},
  Author                   = {Ambrosio, L. and Tortorelli, V. M.},
  Journal                  = {Communications on Pure and Applied Mathematics},
  Year                     = {1990},
  Pages                    = {999-1036},
  Volume                   = {XLIII},

  Owner                    = {zickzack},
  Timestamp                = {2011.09.06}
}

@Article{AmiazFlowContSeg,
  Title                    = {Dense discontinuous optical flow via contour-based seg- mentation},
  Author                   = {Amiaz, T. and Kiryati, N},
  Journal                  = {Proc. ICIP},
  Year                     = {2005},
  Pages                    = {1264-1267},
  Volume                   = {3},

  Abstract                 = {We propose a new algorithm for dense optical flow com- putation. Dense optical flow schemes are challenged by the presence of motion discontinuities. In state of the art optical flow methods, over-smoothing of flow discontinu- ities accounts for most of the error. A breakthrough in the performance of optical flow computation has recently been achieved by Brox et al. Our algorithm embeds their functional within a contour-based segmentation framework. Piecewise-smooth flow fields are accommodated and flow boundaries are crisp. Experimental results show the superi- ority of our algorithm with respect to alternative techniques.},
  Owner                    = {zickzack},
  Timestamp                = {2010.08.02}
}

@Article{Amolins2007,
  author    = {Amolins, Krista and Zhang, Yun and Dare, Peter},
  title     = {Wavelet based image fusion techniques�An introduction, review and comparison},
  year      = {2007},
  volume    = {62},
  number    = {4},
  pages     = {249--263},
  journal   = {ISPRS Journal of Photogrammetry and Remote Sensing},
  owner     = {gerald},
  publisher = {Elsevier},
  timestamp = {2013.04.12},
}

@Article{AmroImageFusionSurvey,
  Title                    = {A survey of classical methods and new trends in pansharpening of multispectral images.},
  Author                   = {Amro, Israa and Mateos, Javier and Vega, Miguel and Molina, Rafael and Katsaggelos, Aggelos K},
  Journal                  = {EURASIP J. Adv. Sig. Proc.},
  Year                     = {2011},
  Pages                    = {79},
  Volume                   = {2011},

  Owner                    = {gerald},
  Timestamp                = {2016.04.26}
}

@Article{AndersonExpCharacterOfTOF,
  Title                    = {Experimental Characterization of Commercial Flash Ladar Devices},
  Author                   = {Anderson, Dean and Herman, Herman and Kelly, Alonzo},
  Journal                  = {International Conference of Sensing and Technology},
  Year                     = {2005},

  Owner                    = {zickzack},
  Timestamp                = {2010.08.12}
}

@Article{AndresSchnoerrCompInferAlgGraphMod,
  Title                    = {An Empirical Comparison of Inference Algorithms for Graphical Models with Higher Order Factors Using OpenGM},
  Author                   = {Andres, Bj\"{o}rn and Kappes, J\"{o}rg H. and Schn\"{o}rr, Christoph},
  Journal                  = {Pattern Recognition, Proc. 32th DAGM Symposium},
  Year                     = {2010},

  Owner                    = {zickzack},
  Timestamp                = {2010.08.30}
}

@InProceedings{AryaMountANN,
  Title                    = {Approximate Nearest Neighbor Queries in Fixed Dimensions.},
  Author                   = {Arya, S. and Mount, D. M.},
  Booktitle                = {SODA},
  Year                     = {1993},
  Pages                    = {271--280},
  Volume                   = {93},

  Owner                    = {gmwangi},
  Timestamp                = {2015.02.05}
}

@Article{AzevedoLaplaceMethInfer,
  Title                    = {Laplace's Method Approximations for Probabilistic Inference in Belief Networks with Continuous Variables},
  Author                   = {Azevedo-Filho, A. and Schachter, R. D.},
  Journal                  = {Uncertainty in Artificial Intelligence},
  Year                     = {1994},

  Owner                    = {zickzack},
  Timestamp                = {2010.12.07}
}

@InProceedings{van2012sensor,
  Title                    = {Sensor Fusion for Depth Estimation, including TOF and Thermal Sensors},
  Author                   = {van Baar, J. and Beardsley, P. and Pollefeys, M. and Gross, M.},
  Booktitle                = {3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT), 2012 Second International Conference on},
  Year                     = {2012},
  Organization             = {IEEE},
  Pages                    = {472--478}
}

@Article{baker2011database,
  Title                    = {A database and evaluation methodology for optical flow},
  Author                   = {Baker, S. and Scharstein, D. and Lewis, J. P. and Roth, S. and Black, M. J. and Szeliski, R.},
  Journal                  = {International Journal of Computer Vision},
  Year                     = {2011},
  Number                   = {1},
  Pages                    = {1--31},
  Volume                   = {92},

  Publisher                = {Springer}
}

@InProceedings{Baker-middlebuRubberWh2007,
  Title                    = {A database and evaluation methodology for optical flow},
  Author                   = {Baker, S. and Scharstein, D. and Lewis, J. P. and Roth, S. and Black, M. J. and Szeliski, R.},
  Year                     = {2011},
  Number                   = {1},
  Pages                    = {1--31},
  Publisher                = {Springer},
  Volume                   = {92},

  Journal                  = {International Journal of Computer Vision}
}

@InProceedings{BarMotionBlur,
  Title                    = {A Variational Framework for Simultaneous Motion Estimation and Restoration of Motion-Blurred Video},
  Author                   = {Bar, L. and Berkels, B. and Rumpf, M. and Sapiro, G.},
  Booktitle                = {Proc. IEEE 11th Int. Conf. Computer Vision ICCV 2007},
  Year                     = {2007},
  Pages                    = {1--8},

  Doi                      = {10.1109/ICCV.2007.4409009},
  Owner                    = {zickzack},
  Timestamp                = {2011.01.18}
}

@Article{BarrettGradientFlowOfClosedCurve,
  Title                    = {Numerical approximation of gradient flows for closed curves in Rd},
  Author                   = {Barrett, John W. and Garcke, Harald and N\"{u}rnberg, Robert},
  Journal                  = {Journal of Numerical Analysis},
  Year                     = {2010},
  Pages                    = {4-60},
  Volume                   = {30},

  Owner                    = {zickzack},
  Timestamp                = {2011.02.15}
}

@Article{Barron-PerfOptFlow,
  Title                    = {Performance of Optical Flow Techniques},
  Author                   = {Barron, J. L. and Fleet, D. J. and Beauchemin, S. S.},
  Journal                  = {International Journal of Computer Vision},
  Year                     = {1994},
  Pages                    = {43-77},
  Volume                   = {12, no 1},

  Abstract                 = {While different optical flow techniques continue to appear, there has been a lack of quantitative evaluation of existing methods. For a common set of real and synthetic image sequences, we report the results of a number of regularly cited opti- cal ow techniques, including instances of differential, matching, energy-based and phase-based methods. Our comparisons are primarily empirical, and concentrate on the accuracy, reliability and density of the velocity measurements; they show that performance can di er signi cantly among the techniques we implemented.},
  Owner                    = {zickzack},
  Timestamp                = {2010.07.23}
}

@Article{BergholdtObjDetecGraphMod,
  Title                    = {A Study of Parts-Based Object Class Detection Using Complete Graphs},
  Author                   = {Bergtholdt, Martin and Kappes, J\"{o}rg Schmidt, Stefan and Schn\"{o}rr, Christoph},
  Journal                  = {International Journal of Computer Vision},
  Year                     = {2009},

  Abstract                 = {Object detection is one of the key components in modern computer vision systems. While the detection of a specific rigid object under changing viewpoints was consid- ered hard just a few years ago, current research strives to detect and recognize classes of non-rigid, articulated ob- jects. Hampered by the omnipresent confusing information due to clutter and occlusion, the focus has shifted from holistic approaches for object detection to representations of individual object parts linked by structural information, along with richer contextual descriptions of object config- urations. Along this line of research, we present a prac- ticable and expandable probabilistic framework for parts- based object class representation, enabling the detection of rigid and articulated object classes in arbitrary views. We investigate learning of this representation from labelled training images and infer globally optimal solutions to the contextual MAP-detection problem, using A∗ -search with a novel lower-bound as admissible heuristic. An assessment of the inference performance of Belief-Propagation and Tree- Reweighted Belief Propagation is obtained as a by-product. The generality of our approach is demonstrated on four dif- ferent datasets utilizing domain dependent information cues.},
  Owner                    = {zickzack},
  Timestamp                = {2010.08.06}
}

@Article{BertelliLengthAreaRegMultLevelSet,
  Title                    = {On the Length and Area Regularization for Multiphase Level Set Segmentation},
  Author                   = {Bertelli, L. and Chandrasekaran, S. and Gibou, F.},
  Journal                  = {International Journal of Computer Vision},
  Year                     = {2010},
  Pages                    = {267-282},
  Volume                   = {90},

  Owner                    = {zickzack},
  Timestamp                = {2011.02.10}
}

@InProceedings{Bhattacharya2009,
  author       = {Bhattacharya, Mahua and Das, Arpita},
  title        = {Registration of multimodality medical imaging of brain using particle swarm optimization},
  booktitle    = {Proceedings of the first international conference on intelligent human computer interaction},
  year         = {2009},
  organization = {Springer},
  pages        = {131--139},
  owner        = {gerald},
  timestamp    = {2013.04.12},
}

@InProceedings{Bhattacharya2007,
  author       = {Bhattacharya, Mahua and Das, Arpita},
  title        = {Multi resolution medical image registration using maximization of mutual information \& optimization by genetic algorithm},
  booktitle    = {Nuclear Science Symposium Conference Record, 2007. NSS'07. IEEE},
  year         = {2007},
  volume       = {4},
  organization = {IEEE},
  pages        = {2961--2964},
  owner        = {gerald},
  timestamp    = {2013.04.12},
}

@InCollection{Biesdorf2009,
  author    = {Biesdorf, A. and W{\"o}rz, S. and Kaiser, H. and Stippich, C. and Rohr, K.},
  title     = {Hybrid spline-based multimodal registration using local measures for joint entropy and mutual information},
  booktitle = {Medical Image Computing and Computer-Assisted Intervention},
  year      = {2009},
  publisher = {Springer},
  pages     = {607--615},
  owner     = {gerald},
  timestamp = {2013.04.12},
}

@Book{BigunBook,
  Title                    = {Vision with Direction A Systematic Introduction to Image Processing and Computer Vision},
  Author                   = {Bigun, J.},
  Publisher                = {Springer},
  Year                     = {2006},

  Owner                    = {gerald},
  Timestamp                = {2012.03.12}
}

@Article{BigunSymmetryGenStructTensor,
  Title                    = {Recognition by Symmetry Derivatives and the Generalized Structure Tensor},
  Author                   = {Bigun, J. and Bigun, T. and Nilsson, K.},
  Journal                  = {IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE},
  Year                     = {2004},
  Pages                    = {1590-1605},
  Volume                   = {26 no. 12},

  Owner                    = {zickzack},
  Timestamp                = {2011.03.15}
}

@Article{Bigun1987,
  Title                    = {Optimal Orientation Detection of Linear Symmetry},
  Author                   = {Bigun, J. and Granlund, G. H.},
  Journal                  = {Proceedings of the IEEE First International Conference on Computer Vision : London, Great Britain},
  Year                     = {1987},
  Pages                    = {433-438},

  Abstract                 = {The problem of optimal detection of orientation in arbitrary neighborhoods is solved in the least squares sense. It is shown that this corresponds to fitting an axis in the Fourier domain of the n-dimensional neighborhood, the solution of which is a well known solution of a matrix eigenvalue problem. The eigenvalues are the variance or inertia with respect to the axes given by their respective eigen vectors. The orientation is taken as the axis given by the least eigenvalue. Moreover it is shown that the necessary computations can be pursued in the spatial domain without doing a Fourier transformation. An implementation for 2-D is presented. Two certainty measures are given corresponding to the orientation estimate. These are the relative or the absolute distances between the two eigenvalues, revealing whether the fitted axis is much better than an axis orthogonal to it. The result of the implementation is verified by experiments which confirm an accurate orientation estimation and reliable certainty measure in the presence of additive noise at high level as well as low levels.},
  Owner                    = {gerald},
  Timestamp                = {2011.11.21}
}

@Article{birchfield-klt-derivation,
  Title                    = {Derivation of kanade-lucas-tomasi tracking equation},
  Author                   = {Birchfield, S.},
  Journal                  = {unpublished notes},
  Year                     = {1997},

  Owner                    = {gmwangi},
  Timestamp                = {2015.02.05}
}

@Book{BishopGraphMod,
  Title                    = {Pattern Recognition and Machine Learning},
  Author                   = {C. M. Bishop},
  Publisher                = {Springer},
  Year                     = {2006},

  Owner                    = {zickzack},
  Timestamp                = {2010.08.06}
}

@Article{BlackAnandanRobustOpticalFlow,
  Title                    = {The robust estimation of multiple motions: Parametric and piecewise-smooth flow fields},
  Author                   = {Black, Michael J and Anandan, Paul},
  Journal                  = {Computer vision and image understanding},
  Year                     = {1996},
  Number                   = {1},
  Pages                    = {75--104},
  Volume                   = {63},

  Owner                    = {gerald},
  Publisher                = {Elsevier},
  Timestamp                = {2015.10.19}
}

@Book{BoydConvexOptim,
  Title                    = {Convex Optimization},
  Author                   = {Boyd, Stephen and Vandenberghe, Lieven},
  Publisher                = {Cambridge Press},
  Year                     = {2004},

  Owner                    = {zickzack},
  Timestamp                = {2010.10.27}
}

@InProceedings{BoykovGeodesicsViaGraphCut,
  Title                    = {Computing geodesics and minimal surfaces via graph cuts},
  Author                   = {Boykov, Y. and Kolmogorov, V. },
  Booktitle                = {Proc. Ninth IEEE Int Computer Vision Conf},
  Year                     = {2003},
  Pages                    = {26--33},

  Doi                      = {10.1109/ICCV.2003.1238310},
  Owner                    = {zickzack},
  Timestamp                = {2011.01.27}
}

@Article{BoykovMRFGraphCutPottsMod,
  Title                    = {Markov Random Fields with Efficient Approximations},
  Author                   = {Boykov, Yuri and Veksler, Olga and Zabih, Ramin},
  Journal                  = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition table of contents Page: 648 Year of Publication: 1998},
  Year                     = {1998},
  Pages                    = {648},

  Abstract                 = {Markov Random Fields (MRF’s) can be used for a wide variety of vision problems. In this paper we fo- cus on MRF’s with two-valued clique potentials, which form a generalized Potts model. We show that the maximum a posteriori estimate of such an MRF can be obtained by solving a multiway minimum cut prob- lem on a graph. We develop efficient algorithms for computing good approximations to the minimum mul- tiway cut. The visual correspondence problem can be formulated as an MRF in our framework; this yields quite promising results on real data with ground truth. We also apply our techniques to MRF’s with linear clique potentials.},
  Owner                    = {zickzack},
  Timestamp                = {2010.08.06}
}

@Article{BrediesMathemBildverarbeitung,
  Title                    = {Mathematische Bildverarbeitung},
  Author                   = {Bredies, Kristian and Lorenz, Dirk},
  Journal                  = {Vieweg+ Teubner},
  Year                     = {2011},
  Number                   = {6},
  Pages                    = {12},
  Volume                   = {4},

  Owner                    = {gerald},
  Publisher                = {Springer},
  Timestamp                = {2016.01.29}
}

@Article{BrownLoweImageStitchSIFT,
  Title                    = {Automatic Panoramic Image Stiching using Invariant Features},
  Author                   = {Brown, Mathew and Lowe, David G.},
  Journal                  = {International Journal of Computer Vision},
  Year                     = {2007},
  Pages                    = {59-73},
  Volume                   = {74 issue 1},

  Owner                    = {gerald},
  Timestamp                = {2012.05.15}
}

@Article{Brox-ThOfWarp,
  Title                    = {High Accurancy Optical Flow Esimation Based on a Theory of warping},
  Author                   = {Brox, T. and Bruhn, A. and Papenberg, N. and Weickert, J.},
  Journal                  = {Computer Vision-ECCV},
  Year                     = {2004},
  Pages                    = {25-36},
  Volume                   = {3024/2004},

  Abstract                 = {We study an energy functional for computing optical flow that com- bines three assumptions: a brightness constancy assumption, a gradient constancy assumption, and a discontinuity-preserving spatio-temporal smoothness constraint. In order to allow for large displacements, linearisations in the two data terms are strictly avoided. We present a consistent numerical scheme based on two nested fixed point iterations. By proving that this scheme implements a coarse-to-fine warping strategy, we give a theoretical foundation for warping which has been used on a mainly experimental basis so far. Our evaluation demonstrates that the novel method gives significantly smaller angular errors than previous techniques for optical flow estimation. We show that it is fairly insensitive to parameter vari- ations, and we demonstrate its excellent robustness under noise.},
  Owner                    = {zickzack},
  Timestamp                = {2010.07.23}
}

@Article{BroxStatIntMumShah,
  Title                    = {On the Statistical Interpretation of the Piecewise Smooth Mumford-Shah Functional},
  Author                   = {Brox, Thomas and Cremers, Daniel},
  Journal                  = {LNCS},
  Year                     = {2007},
  Pages                    = {203-213},
  Volume                   = {4485},

  Abstract                 = {In region-based image segmentation, two models dominate the field: the Mumford-Shah functional and statistical approaches based on Bayesian inference. Whereas the latter allow for numerous ways to describe the statistics of intensities in regions, the first includes spa- tially smooth approximations. In this paper, we show that the piece- wise smooth Mumford-Shah functional is a first order approximation of Bayesian a-posteriori maximization where region statistics are computed in local windows. This equivalence not only allows for a statistical inter- pretation of the full Mumford-Shah functional. Inspired by the Bayesian model, it also offers to formulate an extended Mumford-Shah functional that takes the variance of the data into account.},
  Owner                    = {zickzack},
  Timestamp                = {2010.08.02}
}

@Article{Bruhn-CombLocGlobMeth,
  Title                    = {Lucas/Kanade Meets Horn/Schunck: Combining Local and Global Optic Flow Methods},
  Author                   = {Bruhn, T. and Weickert, J. and Schn\"{o}rr, C.},
  Journal                  = {International Journal of Computer Vision},
  Year                     = {2004},
  Pages                    = {211-231},
  Volume                   = {61,no 3},

  Owner                    = {zickzack},
  Timestamp                = {2010.07.23}
}

@Article{CalvettiHypermodelsBayesImaging,
  Title                    = {Hypermodels in the Bayesian imaging framework},
  Author                   = {Calvetti, Daniela and Somersalo, Erkki},
  Journal                  = {Inverse Problems},
  Year                     = {2008},
  Pages                    = {Article: 034013},
  Volume                   = {24 issue 3},

  Owner                    = {zickzack},
  Timestamp                = {2011.04.06}
}

@Article{CasellaGibbsSampler,
  Title                    = {Explaining the Gibbs Sampler},
  Author                   = {Casella, George and George, Edward I.},
  Journal                  = {The American Statistician},
  Year                     = {1992},
  Pages                    = {167-174},
  Volume                   = {46},

  Owner                    = {zickzack},
  Timestamp                = {2010.08.17}
}

@Article{CasellesGeoActCont,
  Title                    = {Geodesic Active Contours},
  Author                   = {Caselles, Vincent and Kimmel, Ron and Sapiro, Guillermo},
  Journal                  = {International Journal of Computer Vision},
  Pages                    = {61-79},
  Volume                   = {22 no 1},

  Owner                    = {zickzack},
  Timestamp                = {2011.02.14}
}

@InProceedings{FieguthCavanAutocal,
  Title                    = {Autocalibration: Finding infinity in a projective reconstruction},
  Author                   = {Cavan, N. and Fieguth, P. and Clausi, D. A.},
  Booktitle                = {Computer and Robot Vision (CRV), 2011 Canadian Conference on},
  Year                     = {2011},
  Organization             = {IEEE},
  Pages                    = {197--203},

  Owner                    = {gmwangi},
  Timestamp                = {2015.02.05}
}

@Article{Chambolle2004,
  author    = {Chambolle, A.},
  title     = {An algorithm for total variation minimization and applications},
  year      = {2004},
  volume    = {20},
  number    = {1-2},
  pages     = {89--97},
  journal   = {Journal of Mathematical imaging and vision},
  owner     = {gerald},
  publisher = {Springer},
  timestamp = {2013.04.12},
}

@InProceedings{ChefdHotel2001,
  author       = {Chefd'Hotel, Christophe and Hermosillo, Gerardo and Faugeras, Olivier},
  title        = {A variational approach to multi-modal image matching},
  booktitle    = {Variational and Level Set Methods in Computer Vision, 2001. Proceedings. IEEE Workshop on},
  year         = {2001},
  organization = {IEEE},
  pages        = {21--28},
  owner        = {gerald},
  timestamp    = {2013.04.12},
}

@Article{ChenImageSegGMRF-EM-Alg,
  Title                    = {{Image segmentation using multiresolution wavelet analysis and expectation-maximization (EM) algorithm for digital mammography}},
  Author                   = {Chen, CH and Lee, GG},
  Journal                  = {{INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY}},
  Year                     = {1997},
  Number                   = {{5}},
  Pages                    = {{491-504}},
  Volume                   = {{8}},

  Abstract                 = {{This article presents a novel algorithm for image segmentation via the use of the multiresolution wavelet analysis and the expectation maximization (EM) algorithm. The development of a multiresolution wavelet feature extraction scheme is based on the Gaussian Markov random field (GMRF) assumption in mammographic image modeling. Mammographic images are hierarchically decomposed into different resolutions. In general, larger breast lesions are characterized by coarser resolutions, whereas higher resolutions show finer and more detailed anatomical structures. These hierarchical variations in the anatomical features displayed by multiresolution decomposition are further quantified through the application of the Gaussian Markov random field. Because of its uniqueness in locality, adaptive features based on the nonstationary assumption of GMRF are defined for each pixel of the mammogram. Fibroadenomas are then segmented via the fuzzy C-means algorithm using these localized features. Subsequently, the segmentation results are further enhanced via the introduction of a maximum a posteriori (MAP) segmentation estimation scheme based on the Bayesian learning paradigm. Gibbs priors or Gibbs random fields have also been incorporated into the learning scheme of the present research with very effective outcomes. In this article, the EM algorithm for MAP estimation is formulated. The EM algorithm provides an iterative and computationally simple algorithm based on the incomplete data concept. (C) 1997 John Wiley \& Sons, Inc.}},
  Address                  = {{605 THIRD AVE, NEW YORK, NY 10158-0012}},
  Affiliation              = {{Chen, CH (Reprint Author), UNIV MASSACHUSETTS,DEPT ELECT \& COMP ENGN,285 OLD WESTPORT RD,N DARTMOUTH,MA 02747.}},
  Doc-delivery-number      = {{XW388}},
  ISSN                     = {{0899-9457}},
  Journal-iso              = {{Int. J. Imaging Syst. Technol.}},
  Keywords                 = {{wavelet decomposition; Gaussian Markov random field; adaptive feature extraction; fuzzy c-means clustering; Gibbs distribution; expectation maximization}},
  Keywords-plus            = {{MARKOV RANDOM-FIELDS; SPATIAL-INTERACTION; MAXIMUM-LIKELIHOOD; MODELS}},
  Language                 = {{English}},
  Number-of-cited-references = {{40}},
  Publisher                = {{JOHN WILEY \& SONS INC}},
  Subject-category         = {{Engineering, Electrical \& Electronic; Optics; Imaging Science \& Photographic Technology}},
  Times-cited              = {{8}},
  Type                     = {{Article}},
  Unique-id                = {{ISI:A1997XW38800011}}
}

@Article{CremersSchnoerrSchapeStatMumfSeg,
  Title                    = {Nonlinear Shape Statistics in Mumford–Shah Based Segmentation},
  Author                   = {Cremers, Daniel and Kohlberger, Timo and Schn\"{o}rr, Christoph},
  Journal                  = {LNCS},
  Year                     = {2002},
  Pages                    = {93-108},
  Volume                   = {2351},

  Abstract                 = {We present a variational integration of nonlinear shape statistics into a Mumford–Shah based segmentation process. The non- linear statistics are derived from a set of training silhouettes by a novel method of density estimation which can be considered as an extension of kernel PCA to a stochastic framework. The idea is to assume that the training data forms a Gaussian distri- bution after a nonlinear mapping to a potentially higher–dimensional feature space. Due to the strong nonlinearity, the corresponding density estimate in the original space is highly non–Gaussian. It can capture essentially arbitrary data distributions (e.g. multiple clusters, ring– or banana–shaped manifolds). Applications of the nonlinear shape statistics in segmentation and track- ing of 2D and 3D objects demonstrate that the segmentation process can incorporate knowledge on a large variety of complex real–world shapes. It makes the segmentation process robust against misleading information due to noise, clutter and occlusion.},
  Owner                    = {zickzack},
  Timestamp                = {2010.08.04}
}

@Article{CremersSoattoVarMotEstSegBayesApp,
  Title                    = {Motion Competition: {A} variational framework for piecewise parametric motion segmentation},
  Author                   = {Cremers, Daniel and Soatto, Stefano},
  Journal                  = {International Journal of Computer Vision},
  Year                     = {2005},

  Month                    = {May},
  Number                   = {3},
  Pages                    = {249-265},
  Volume                   = {62},

  Abstract                 = {We present a novel variational approach for segmenting the image plane into a set of regions of parametric motion on the basis of two consecutive frames from an image sequence. Our model is based on a conditional probability for the spatio-temporal image gradient, given a particular velocity model, and on a geometric prior on the estimated motion field favoring motion boundaries of minimal length. Exploiting the Bayesian framework, we derive a cost functional which depends on parametric motion models for each of a set of regions and on the boundary separating these regions. The resulting functional can be interpreted as an extension of the Mumford-Shah functional from intensity segmentation to motion segmentation. In contrast to most alternative approaches, the problems of segmentation and motion estimation are jointly solved by continuous minimization of a single functional. Minimizing this functional with respect to its dynamic variables results in an eigenvalue problem for the motion parameters and in a gradient descent evolution for the motion discontinuity set. We propose two different representations of this motion boundary: an explicit spline-based implementation which can be applied to the motion-based tracking of a single moving object, and an implicit multiphase level set implementation which allows for the segmentation of an arbitrary number of multiply connected moving objects. Numerical results both for simulated ground truth experiments and for real-world seque}
}

@Article{Crum2004,
  author    = {Crum, WR and Hartkens, T and Hill, DLG},
  title     = {Non-rigid image registration: theory and practice},
  year      = {2004},
  volume    = {77},
  number    = {suppl 2},
  pages     = {S140--S153},
  journal   = {British journal of radiology},
  owner     = {gerald},
  publisher = {Br Inst Radiology},
  timestamp = {2013.04.12},
}

@Article{cubero2011advances,
  Title                    = {Advances in machine vision applications for automatic inspection and quality evaluation of fruits and vegetables},
  Author                   = {Cubero, S. and Aleixos, N. and Molt{\'o}, E. and G{\'o}mez-Sanchis, J. and Blasco, J.},
  Journal                  = {Food and Bioprocess Technology},
  Year                     = {2011},
  Number                   = {4},
  Pages                    = {487--504},
  Volume                   = {4},

  Publisher                = {Springer}
}

@InProceedings{CuiTheobalt3DShapeToFSuperRes,
  Title                    = {3D shape scanning with a time-of-flight camera},
  Author                   = {Cui, Yan and Schuon, Sebastian and Chan, Derek and Thrun, Sebastian and Theobalt, Christian},
  Booktitle                = {Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR)},
  Year                     = {2010},
  Pages                    = {1173--1180},

  Doi                      = {10.1109/CVPR.2010.5540082},
  Owner                    = {zickzack},
  Timestamp                = {2010.09.22}
}

@Article{daffara2012thermal,
  Title                    = {Thermal quasi-reflectography: a new imaging tool in art conservation},
  Author                   = {Daffara, Claudia and Ambrosini, Dario and Pezzati, Luca and Paoletti, Domenica},
  Journal                  = {Optics Express},
  Year                     = {2012},
  Number                   = {13},
  Pages                    = {14746--14753},
  Volume                   = {20},

  Publisher                = {Optical Society of America}
}

@Article{DalMutto2010,
  Title                    = {A probabilistic approach to tof and stereo data fusion},
  Author                   = {Dal Mutto, Carlo and Zanuttigh, Pietro and Cortelazzo, Guido M},
  Journal                  = {3DPVT, Paris, France},
  Year                     = {2010},
  Volume                   = {2},

  Owner                    = {gerald},
  Timestamp                = {2013.04.12}
}

@InProceedings{Dawant2002,
  author       = {Dawant, Benoit M},
  title        = {Non-rigid registration of medical images: purpose and methods, a short survey},
  booktitle    = {Biomedical Imaging, 2002. Proceedings. 2002 IEEE International Symposium on},
  year         = {2002},
  organization = {IEEE},
  pages        = {465--468},
  owner        = {gerald},
  timestamp    = {2013.04.12},
}

@Article{DellenOptFlowFourier,
  Title                    = {Computing local velocity estimates of image sequences by constructive interference of global Fourier components},
  Author                   = {Dellen, Babette K. and W\"{o}rg\"{o}tter, Florentin},
  Year                     = {2007},

  Owner                    = {zickzack},
  Timestamp                = {2010.11.13}
}

@Article{DescombesGibbsFieldsRejMCMC,
  Title                    = {The Gibbs fields approach and related dynamics in image processing},
  Author                   = {Descombes and Zhizhina},
  Journal                  = {Condensed Matter Physics},
  Year                     = {2008},
  Pages                    = {293-312},
  Volume                   = {11 no 2(54)},

  Owner                    = {zickzack},
  Timestamp                = {2010.11.04}
}

@Article{DiebelMRFRangeSensing,
  Title                    = {An Application of Markov Random Fields to Range Sensing},
  Author                   = {Diebel, James and Thrun, Sebastian},
  Journal                  = {Advances in Neural Information Processing Systems},
  Pages                    = {291-298},
  Volume                   = {18},

  Abstract                 = {This paper describes a highly successful application of MRFs to the prob- lem of generating high-resolution range images. A new generation of range sensors combines the capture of low-resolution range images with the acquisition of registered high-resolution camera images. The MRF in this paper exploits the fact that discontinuities in range and coloring tend to co-align. This enables it to generate high-resolution, low-noise range images by integrating regular camera images into the range data. We show that by using such an MRF, we can substantially improve over existing range imaging technology.},
  Owner                    = {zickzack},
  Timestamp                = {2010.09.29}
}

@Article{DikmenGMRFGibbsAudioSourceMod,
  Title                    = {Gamma Markov Random Fields for Audio Source Modeling},
  Author                   = {Dikmen, O. and Cemgil, A. T.},
  Journal                  = {IEEE Transactions on Audio, Speech, and Language Processing},
  Year                     = {2010},
  Number                   = {3},
  Pages                    = {589--601},
  Volume                   = {18},

  Doi                      = {10.1109/TASL.2009.2031778},
  Owner                    = {zickzack},
  Timestamp                = {2010.11.04}
}

@Article{DroskeMumfLevelSetImReg,
  Title                    = {A MUMFORD-SHAH LEVEL-SET APPROACH FOR GEOMETRIC IMAGE REGISTRATION},
  Author                   = {Droske, M. and Ring, W.},
  Journal                  = {Mathematics Subject Classification},
  Year                     = {2005},

  Owner                    = {zickzack},
  Timestamp                = {2010.08.04}
}

@Article{DroskeJointSegRegistration2007,
  Title                    = {Multiscale joint segmentation and registration of image morphology},
  Author                   = {Droske, M. and Rumpf, M.},
  Journal                  = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
  Year                     = {2007},
  Number                   = {12},
  Pages                    = {2181--2194},
  Volume                   = {29},

  Abstract                 = {Multimodal image registration significantly benefits from previous denoising and structure segmentation and vice versa. In particular, combined information of different image modalities makes segmentation significantly more robust. Indeed, fundamental tasks in image processing are highly interdependent. A variational approach is presented, which combines the detection of corresponding edges, an edge preserving denoising, and the morphological registration via a nonrigid deformation for a pair of images with structural correspondence. The morphology of an image function is split into a singular part consisting of the edge set and a regular part represented by the field of normals on the ensemble of level sets. A Mumford- Shah type free discontinuity problem is applied to treat the singular morphology and the matching of corresponding edges under the deformation. The matching of the regular morphology is quantified by a second contribution, which compares deformed normals and normals at deformed positions. Finally, a nonlinear elastic energy controls the deformation itself and ensures smoothness and injectivity. A multiscale approach that is based on a phase field approximation leads to an effective and efficient algorithm. Numerical experiments underline the robustness of the presented approach and show applications on medical images.},
  Owner                    = {gmwangi},
  Publisher                = {IEEE},
  Timestamp                = {2015.01.14}
}

@Article{DroskeWillmoreFlowLevelSet,
  Title                    = {A level set formulation forWillmore flow},
  Author                   = {Droske, M. and Rumpf, M.},
  Journal                  = {Interfaces and Free Boundaries},
  Year                     = {2004},
  Pages                    = {361-378},
  Volume                   = {6 issue 3},

  Owner                    = {zickzack},
  Timestamp                = {2011.02.15}
}

@Article{DziukWillmoreFlow,
  Title                    = {Computational parametricWillmore flow},
  Author                   = {Dziuk, Gerhard},
  Journal                  = {Numerical Mathematics},
  Year                     = {2008},
  Pages                    = {55-80},
  Volume                   = {111},

  Owner                    = {zickzack},
  Timestamp                = {2011.02.15}
}

@Article{EladBilatFilter02,
  Title                    = {On the origin of the bilateral filter and ways to improve it},
  Author                   = {Elad, M.},
  Journal                  = IEEE_J_IP,
  Year                     = {2002},
  Number                   = {10},
  Pages                    = {1141--1151},
  Volume                   = {11},

  Doi                      = {10.1109/TIP.2002.801126},
  Owner                    = {gerald},
  Timestamp                = {2012.03.05}
}

@Article{El-Maadi2007,
  author    = {El-Maadi, Amar and Gr{\'e}goire, Vincent and St-Laurent, Louis and Torresan, H{\'e}l{\`e}ne and Turgeon, Benoit and Pr{\'e}vost, Donald and H{\'e}bert, Patrick and Laurendeau, Denis and Ricard, Benoit and Maldague, Xavier},
  title     = {Visible and infrared imagery for surveillance applications: software and hardware considerations},
  year      = {2007},
  volume    = {4},
  number    = {1},
  pages     = {25--40},
  journal   = {Quantitative InfraRed Thermography Journal},
  owner     = {gerald},
  publisher = {Taylor \& Francis},
  timestamp = {2013.04.12},
}

@Article{ZehiriMumfShahGraphCutSeg,
  Title                    = {GRAPH CUT OPTIMIZATION FOR THE MUMFORD-SHAH MODEL},
  Author                   = {El-Zehiri, N. and Xu, S. and Sahoo, P. and Elmaghraby, A.},
  Journal                  = {Proceedings of the Seventh IASTED International Conference Visualization, Imaging and Image Processing},
  Year                     = {2007},

  Owner                    = {zickzack},
  Timestamp                = {2011.01.24}
}

@InProceedings{EssDepthObjectDetGraphMod,
  Title                    = {Depth and Appearance for Mobile Scene Analysis},
  Author                   = {Ess, Andreas and Leibe, Bastian and Van Gool, Luc},
  Booktitle                = {Proc. IEEE 11th Int. Conf. Computer Vision ICCV 2007},
  Pages                    = {1--8},

  Abstract                 = {In this paper, we address the challenging problem of si- multaneous pedestrian detection and ground-plane estima- tion from video while walking through a busy pedestrian zone. Our proposed system integrates robust stereo depth cues, ground-plane estimation, and appearance-based ob- ject detection in a principled fashion using a graphical model. Object-object occlusions lead to complex interac- tions in this model that make an exact solution computa- tionally intractable. We therefore propose a novel iterative approach that first infers scene geometry using Belief Prop- agation and then resolves interactions between objects us- ing a global optimization procedure. This approach leads to a robust solution in few iterations, while allowing ob- ject detection to benefit from geometry estimation and vice versa. We quantitatively evaluate the performance of our proposed approach on several challenging test sequences showing strolls through busy shopping streets. Compar- isons to various baseline systems show that it outperforms both a system using no scene geometry and one just relying on Structure-from-Motion without dense stereo.},
  Doi                      = {4409092},
  Owner                    = {zickzack},
  Timestamp                = {2010.08.09}
}

@Article{EsteparRiemCurvFlow,
  Title                    = {riemannian mean curvature flow},
  Author                   = {Est\`{e}par, Ra\`{u}l San Jos\`{e} and Haker, Steve and Westin, Carl-Fredrik},
  Journal                  = {Lecture Notes in Computer Science},
  Year                     = {2005},
  Pages                    = {613-620},
  Volume                   = {3804},

  Owner                    = {zickzack},
  Timestamp                = {2011.02.14}
}

@Book{FaugerasCrossCorrMatching,
  Title                    = {Variational principles, surface evolution, pde's, level set methods and the stereo problem},
  Author                   = {Faugeras, Olivier and Keriven, Renaud},
  Publisher                = {IEEE},
  Year                     = {2002},

  Owner                    = {gerald},
  Timestamp                = {2015.11.03}
}

@Article{FayyadDiscretization,
  Title                    = {On the Handling of Continuous-Valued Attributes in Decision Tree Generation},
  Author                   = {Fayyad, Usama M. and Irani, Keki B.},
  Journal                  = {Machine Learning},
  Year                     = {1992},
  Pages                    = {87-102},
  Volume                   = {8},

  Owner                    = {zickzack},
  Timestamp                = {2011.05.09}
}

@PhdThesis{FehrDiss,
  Title                    = {Local Invariant Features for 3D Image Analysis},
  Author                   = {Fehr, Janis},
  School                   = {University of Heidelberg},
  Year                     = {2009},

  Owner                    = {zickzack},
  Timestamp                = {2011.07.22}
}

@Article{OlverMovingFrame2,
  Title                    = {Moving coframes: II. Regularization and theoretical foundations},
  Author                   = {Fels, Mark and Olver, Peter J},
  Journal                  = {Acta Applicandae Mathematica},
  Year                     = {1999},
  Number                   = {2},
  Pages                    = {127--208},
  Volume                   = {55},

  Owner                    = {gerald},
  Publisher                = {Springer},
  Timestamp                = {2016.05.16}
}

@Article{OlverMovingFrame1,
  author    = {Fels, Mark and Olver, Peter J},
  title     = {Moving coframes: I. A practical algorithm},
  year      = {1998},
  volume    = {51},
  number    = {2},
  pages     = {161--213},
  journal   = {Acta Applicandae Mathematica},
  owner     = {gerald},
  publisher = {Springer},
  timestamp = {2016.05.16},
}

@Article{FerraroActiveVision,
  Title                    = {Coupling the world with the observer: from analysis of information to active vision},
  Author                   = {Ferraro, M. and Boccignone, G.},
  Journal                  = {Spatial Vision},
  Year                     = {2009},
  Pages                    = {361-381},
  Volume                   = {22 no. 5},

  Abstract                 = {In this paper we define the content of information in an image and show how it can be computed by taking into account different levels of resolution, in the framework of information theory and the thermodynamics of irreversible transformations. The results thus obtained will eventually be exploited to derive a mechanism for active exploration of visual space suitable to perform a dynamic coupling between the agent and its environment.},
  Owner                    = {gerald},
  Timestamp                = {2012.03.12}
}

@Article{FerraroTransfInvLieGroup,
  Title                    = {Relationship between integral transform invariances and Lie group theory},
  Author                   = {Ferraro, Mario and Caelli, Terry M.},
  Journal                  = {Journal of the Optical Society of America A},
  Year                     = {1988},
  Pages                    = {738-742},
  Volume                   = {5 no 5},

  Owner                    = {zickzack},
  Timestamp                = {2011.03.17}
}

@Book{FieguthStatImProc,
  Title                    = {Statistical image processing and multidimensional modeling},
  Author                   = {Fieguth, Paul},
  Publisher                = {Springer Science \& Business Media},
  Year                     = {2010},

  Owner                    = {gerald},
  Timestamp                = {2016.05.14}
}

@Article{FleischmanBilatMeshDenoising,
  Title                    = {Bilateral Mesh Denoising},
  Author                   = {Fleischman, Shachar and Drori, Iddo and Cohen-Or, Daniel},
  Journal                  = {Proceedings of SIG-GRAPH},
  Year                     = {2003},
  Pages                    = {950-953},

  Owner                    = {zickzack},
  Timestamp                = {2010.09.29}
}

@InCollection{Frahm_et_al_ReconstructionFromPhotoCollection,
  Title                    = {Building Rome on a cloudless day},
  Author                   = {Frahm, J. and Fite-Georgel, P. and Gallup, D. and Johnson, T. and Raguram, R. and Wu, C. and Jen, Y. and Dunn, E. and Clipp, B. and Lazebnik, S. and others},
  Booktitle                = {Computer Vision--ECCV 2010},
  Publisher                = {Springer},
  Year                     = {2010},
  Pages                    = {368--381},

  Owner                    = {gmwangi},
  Timestamp                = {2015.02.05}
}

@InProceedings{FrahmPollefeysRansacQuasiDegenerateData,
  Title                    = {RANSAC for (quasi-) degenerate data (QDEGSAC)},
  Author                   = {Frahm, J. and Pollefeys, M.},
  Booktitle                = {Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on},
  Year                     = {2006},
  Organization             = {IEEE},
  Pages                    = {453--460},
  Volume                   = {1},

  Owner                    = {gmwangi},
  Timestamp                = {2015.02.05}
}

@Article{FrankToFDenoising,
  Title                    = {Denoising of continuous-wave time-of-flight depth images using confidence measures},
  Author                   = {Frank, Mario and Plaue, Mathias and Hamprecht, Fred},
  Journal                  = {Optical Engineering},
  Year                     = {2009},
  Pages                    = {077003},
  Volume                   = {48},

  Owner                    = {zickzack},
  Timestamp                = {2011.01.18}
}

@Article{FrankJaehneTherExpAnalTOF,
  Title                    = {Theoretical and experimental error analysis of continuous-wave time-of-flight range cameras},
  Author                   = {Frank, Mario and Plaue, Mathias and Rapp, Holger and K\"{o}the, Ulrich and J\"{a}hne, Bernd and Hamprecht, Fred A.},
  Journal                  = {Optical Engineering},
  Year                     = {2009},
  Volume                   = {48},

  Owner                    = {zickzack},
  Timestamp                = {2010.08.12}
}

@Article{Gies2006,
  Title                    = {{Worldline algorithms for Casimir configurations}},
  Author                   = {Gies, Holger and Klingm\"{u}ller, Klaus},
  Journal                  = {Phys. Rev.},
  Year                     = {2006},
  Pages                    = {045002},
  Volume                   = {D74},

  Archiveprefix            = {arXiv},
  Doi                      = {10.1103/PhysRevD.74.045002},
  Eprint                   = {quant-ph/0605141},
  Owner                    = {zickzack},
  Slaccitation             = {%%CITATION = QUANT-PH/0605141;%%},
  Timestamp                = {2010.09.03}
}

@Article{Gies2006a,
  Title                    = {{Quantum energies with worldline numerics}},
  Author                   = {Gies, Holger and Klingm\"{u}ller, Klaus},
  Journal                  = {J. Phys.},
  Year                     = {2006},
  Pages                    = {6415-6422},
  Volume                   = {A39},

  Archiveprefix            = {arXiv},
  Doi                      = {10.1088/0305-4470/39/21/S36},
  Eprint                   = {hep-th/0511092},
  Owner                    = {zickzack},
  Slaccitation             = {%%CITATION = HEP-TH/0511092;%%},
  Timestamp                = {2010.09.03}
}

@Article{gonzalez2012single,
  Title                    = {Single image rectification of thermal images for geometric studies in fa{\c{c}}ade inspections},
  Author                   = {Gonz{\'a}lez-Jorge, H. and Lag{\"u}ela, S. and Krelling, P. and Armesto, J. and Mart{\'\i}nez-S{\'a}nchez, J.},
  Journal                  = {Infrared Physics \& Technology},
  Year                     = {2012},

  Publisher                = {Elsevier}
}

@InCollection{Guetter2005,
  author    = {Guetter, Christoph and Xu, Chenyang and Sauer, Frank and Hornegger, Joachim},
  title     = {Learning based non-rigid multi-modal image registration using Kullback-Leibler divergence},
  booktitle = {Medical Image Computing and Computer-Assisted Intervention--MICCAI 2005},
  year      = {2005},
  publisher = {Springer},
  pages     = {255--262},
  owner     = {gerald},
  timestamp = {2013.04.12},
}

@Article{HammersleyMRFonGraphs,
  Title                    = {Markov fields on finite graphs and lattices},
  Author                   = {Hammersley, J. M. and Clifford, P.},
  Year                     = {1971},

  Owner                    = {zickzack},
  Timestamp                = {2010.08.23}
}

@Article{HandlovicovaMeanCurvLinearize,
  Title                    = {Stability and constency of the semi-implicit co-volume scheme for regularized mean curvature flow equation in level set formulation},
  Author                   = {Handlovi\u{c}ov\´{a}, Angela and Mikula, Karol},
  Journal                  = {Applications of mathematics},
  Year                     = {2008},
  Pages                    = {105-129},
  Volume                   = {53 no. 2},

  Owner                    = {zickzack},
  Timestamp                = {2011.02.15}
}

@Article{HardieInfraRedSuperRes,
  Title                    = {High-resolution image reconstruction from a sequence of rotated and translated frames and its application to an infrared imaging system},
  Author                   = {Hardie, Russell C and Barnard, Kenneth J and Bognar, John G and Armstrong, Ernest E and Watson, Edward A},
  Journal                  = {Optical Engineering},
  Year                     = {1998},
  Number                   = {1},
  Pages                    = {247--260},
  Volume                   = {37},

  Owner                    = {gerald},
  Publisher                = {International Society for Optics and Photonics},
  Timestamp                = {2016.01.29}
}

@Article{HardieSpacialImageResEnhancement,
  Title                    = {MAP Estimation for Hyperspectral Image Resolution Enhancement Using an Auxiliary Sensor},
  Author                   = {R. C. Hardie and M. T. Eismann and G. L. Wilson},
  Journal                  = {IEEE Transactions on Image Processing},
  Year                     = {2004},
  Number                   = {4},
  Volume                   = {13},

  Owner                    = {gerald},
  Timestamp                = {2015.09.24}
}

@Article{HartleySturmTriangulation,
  Title                    = {Triangulation},
  Author                   = {Hartley, R. and Sturm, P.},
  Journal                  = {Computer vision and image understanding},
  Year                     = {1997},
  Number                   = {2},
  Pages                    = {146--157},
  Volume                   = {68},

  Owner                    = {gmwangi},
  Publisher                = {Elsevier},
  Timestamp                = {2015.02.05}
}

@Book{Hartley-Zisserman-multiple_view_geometry_book,
  Title                    = {Multiple view geometry in computer vision},
  Author                   = {Hartley, R. and Zisserman, A.},
  Publisher                = {Cambridge university press},
  Year                     = {2003},

  Owner                    = {gmwangi},
  Timestamp                = {2015.02.05}
}

@Article{HeinrichProjectiveMerge,
  Title                    = {Robust Maximum Likelihood Structure Invariant Merging of Projective Reconstructions},
  Author                   = {Heinrich, Stuart B and Snyder, Wesley E},
  Journal                  = {reconstruction},
  Pages                    = {265},
  Volume                   = {1},

  Owner                    = {gerald},
  Timestamp                = {2015.10.21}
}

@Article{HelinHierachModelMumfShah,
  Title                    = {HIERARCHICAL MODELS IN STATISTICAL INVERSE PROBLEMS AND THE MUMFORD-SHAH FUNCTIONAL},
  Author                   = {Helin, T. and Lassas, M.},
  Journal                  = {Inverse Problems},
  Year                     = {2011},
  Pages                    = {Article Nr: 015008},
  Volume                   = {27 issue 1},

  Owner                    = {zickzack},
  Timestamp                = {2011.04.06}
}

@Article{Hinton-CD,
  Title                    = {Training Products of Experts by Minimizing Contrastive Divergence},
  Author                   = {Hinton, Geoffrey E.},
  Journal                  = {Neural Computation},
  Year                     = {2002},
  Pages                    = {1771-1800},
  Volume                   = {14},

  Owner                    = {zickzack},
  Timestamp                = {2010.07.23}
}

@Article{HirschmuellerSemiGlobalMatching,
  Title                    = {Stereo processing by semiglobal matching and mutual information},
  Author                   = {Hirschm{\"u}ller, Heiko},
  Journal                  = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
  Year                     = {2008},
  Number                   = {2},
  Pages                    = {328--341},
  Volume                   = {30},

  Owner                    = {gerald},
  Publisher                = {IEEE},
  Timestamp                = {2015.10.19}
}

@InProceedings{hirschmuller2009stereo,
  Title                    = {Stereo matching in the presence of sub-pixel calibration errors},
  Author                   = {Hirschmuller, H. and Gehrig, S.},
  Booktitle                = {Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on},
  Year                     = {2009},
  Organization             = {IEEE},
  Pages                    = {437--444}
}

@Article{Hong2008,
  author    = {Hong, G. and Zhang, Y.},
  title     = {Comparison and improvement of wavelet-based image fusion},
  year      = {2008},
  volume    = {29},
  number    = {3},
  pages     = {673--691},
  journal   = {International Journal of Remote Sensing},
  owner     = {gerald},
  publisher = {Taylor \& Francis},
  timestamp = {2013.04.12},
}

@Article{Horn-Schunck,
  Title                    = {Determining Optical Flow},
  Author                   = {Horn, B. K.P. and Schunck, B. G.},
  Journal                  = {Artificial Intelligence},
  Year                     = {1981},
  Pages                    = {185-203},
  Volume                   = {17, issues 1-3},

  Owner                    = {zickzack},
  Timestamp                = {2010.07.23}
}

@Book{JaehneBook,
  Title                    = {Digital Image Processing},
  Author                   = {J\"{a}hne, Bernd},
  Publisher                = {Springer},
  Year                     = {2005},

  Owner                    = {gerald},
  Timestamp                = {2011.11.16}
}

@Article{Jiang2014,
  Title                    = {Regularizing properties of the Mumford--Shah functional for imaging applications},
  Author                   = {Jiang, Ming and Maass, Peter and Page, Thomas},
  Journal                  = {Inverse Problems},
  Year                     = {2014},
  Number                   = {3},
  Pages                    = {035007},
  Volume                   = {30},

  Abstract                 = {The Mumford-Shah functional was originally introduced for image denoising and segmentation problems, and is of interest because it provides a regularization of image edges in addition to images. Recently, this functional has emerged as a regularization technique for several imaging applications, such as x-ray tomography, electric impedance tomography, image deblurring and SPECT. In this context of operator equations it is necessary to understand its regularization properties and to determine its range of applicability. Following the approach of Rondi and Santosa, we exploit an L-infinity-constraint on the images, however in contrast to this approach we achieve convergence results not only for the images but also for the edge sets in the sense of sigma-convergence introduced by Maso et al. The analysis exploits an assumption on the decay properties of the fidelity term. Under the above two conditions, we establish the stability of the Mumford-Shah regularization for perturbations in the data. Moreover we present a parameter choice rule which ensures, that the reconstructed images and edges converge to the true image and its edges as the noise level goes to zero. We demonstrate the applications of the Mumford-Shah regularization to some linear and nonlinear imaging problems, namely image deblurring, x-ray tomography and two-dimensional diffuse optical tomography.},
  Owner                    = {gmwangi},
  Publisher                = {IOP Publishing},
  Timestamp                = {2015.01.14}
}

@PhdThesis{Jin-wavelet,
  Title                    = {Wavelet-based Image and Video Processing},
  Author                   = {Jin, Fu},
  School                   = {Waterloo},
  Year                     = {2004},

  Owner                    = {zickzack},
  Timestamp                = {2010.07.23}
}

@Article{JordanGraphMod,
  Title                    = {Graphical Models},
  Author                   = {Michael I. Jordan},
  Journal                  = {Statistical Science},
  Year                     = {2004},

  Abstract                 = {Statistical applications in fields such as bioinformatics, information retrieval, speech processing, im- age processing and communications often involve large-scale models in which thousands or millions of random variables are linked in complex ways. Graphical models provide a general methodology for approaching these problems, and indeed many of the models developed by researchers in these applied fields are instances of the general graphical model formalism. We review some of the basic ideas underlying graphical models, including the algorithmic ideas that allow graphical models to be deployed in large-scale data analysis problems. We also present examples of graphical models in bioinformatics, error-control coding and language processing},
  Owner                    = {zickzack},
  Timestamp                = {2010.08.06}
}

@Article{JordanIntroVarMethInGraph,
  Title                    = {An Introduction to Variational Methods for Graphical Models},
  Author                   = {Jordan, Michael I. and Gharamani, Zoubin and Jaakola, Tommi S. and Saul, Lawrence K.},
  Journal                  = {Machine Learning},
  Year                     = {1999},
  Pages                    = {183-233},
  Volume                   = {37},

  Owner                    = {zickzack},
  Timestamp                = {2010.08.03}
}

@Article{JoshiLiMAP-HMM,
  Title                    = {A computationally efficient approach to the estimation of two- and three-dimensional hidden Markov models},
  Author                   = {Joshi, D. and Jia Li and Wang, J. Z. },
  Journal                  = ieee_j_ip,
  Year                     = {2006},
  Number                   = {7},
  Pages                    = {1871--1886},
  Volume                   = {15},

  Doi                      = {10.1109/TIP.2006.877039},
  Owner                    = {zickzack},
  Timestamp                = {2010.11.04}
}

@Article{KangSegMufShah2014,
  Title                    = {Multiphase image segmentation via equally distanced multiple well potential},
  Author                   = {Kang, S. H. and March, R.},
  Journal                  = {Journal of Visual Communication and Image Representation},
  Year                     = {2014},

  Owner                    = {gmwangi},
  Publisher                = {Elsevier},
  Timestamp                = {2015.01.14}
}

@Article{KatoMultiGridMRFClassif,
  Title                    = {A Hierachical Markov Random Field Model and Multitemperature Annealing for Parallel Image Classification},
  Author                   = {Kato, Z. and Berthod, M. and Zerubia, J.},
  Journal                  = {Graphical Models and Image Processing},
  Year                     = {1996},
  Pages                    = {18-37},
  Volume                   = {58, No. 1},

  Owner                    = {zickzack},
  Timestamp                = {2011.11.03}
}

@Article{KichenassamyConformalCurvFlow,
  Title                    = {Conformal curvature flows: From phase transitions to active vision},
  Author                   = {Kichenassamy, Satyanad and Kumar, Arun and Olver, Peter and Tannenbaum, Allen and Yezzi, Anthony Jr.},
  Journal                  = {Archive for rational mechanics and analysis},
  Year                     = {1996},
  Pages                    = {275-301},
  Volume                   = {134 issue 3},

  Owner                    = {zickzack},
  Timestamp                = {2011.02.16}
}

@InProceedings{KimMultiViewTOF,
  Title                    = {Multi-view image and ToF sensor fusion for dense 3D reconstruction},
  Author                   = {Kim, Young Min and Theobalt, Christian and Diebel, James and Kosecka, Jana and Miscusik, Branislav and Thrun, Sebastian},
  Booktitle                = {Proc. IEEE 12th Int Computer Vision Workshops (ICCV Workshops) Conf},
  Year                     = {2009},
  Pages                    = {1542--1549},

  Abstract                 = {Multi-view stereo methods frequently fail to properly re- construct 3D scene geometry if visible texture is sparse or the scene exhibits difficult self-occlusions. Time-of-Flight (ToF) depth sensors can provide 3D information regardless of texture but with only limited resolution and accuracy. To find an optimal reconstruction, we propose an integrated multi-view sensor fusion approach that combines informa- tion from multiple color cameras and multiple ToF depth sensors. First, multi-view ToF sensor measurements are combined to obtain a coarse but complete model. Then, the initial model is refined by means of a probabilistic multi- view fusion framework, optimizing over an energy function that aggregates ToF depth sensor information with multi- view stereo and silhouette constraints. We obtain high qual- ity dense and detailed 3D models of scenes challenging for stereo alone, while simultaneously reducing complex noise of ToF sensors.},
  Doi                      = {10.1109/ICCVW.2009.5457430},
  Owner                    = {zickzack},
  Timestamp                = {2010.08.11}
}

@Book{KirillovLieGroup,
  Title                    = {An introduction to Lie groups and Lie algebras},
  Author                   = {Kirillov, Alexander A},
  Publisher                = {Cambridge University Press Cambridge},
  Year                     = {2008},
  Volume                   = {113},

  Owner                    = {gerald},
  Timestamp                = {2016.05.14}
}

@InProceedings{kolb2010time,
  Title                    = {Time-of-Flight Cameras in Computer Graphics},
  Author                   = {Kolb, Andreas and Barth, Erhardt and Koch, Reinhard and Larsen, Rasmus},
  Booktitle                = {Computer Graphics Forum},
  Year                     = {2010},
  Organization             = {Wiley Online Library},
  Pages                    = {141--159},
  Volume                   = {29, no. 1}
}

@Article{KolbToFDescription,
  Title                    = {Time-of-Flight Cameras in Computer Graphics},
  Author                   = {Kolb, A. and Barth, E. and Koch, R. and Larsen, R.},
  Journal                  = {COMPUTER GRAPHICS},
  Year                     = {2010},
  Pages                    = {141-159},
  Volume                   = {29, no. 1},

  Owner                    = {zickzack},
  Timestamp                = {2010.08.27}
}

@InProceedings{KolmogorovBoykovMetricsGeocut,
  Title                    = {What metrics can be approximated by geo-cuts, or global optimization of length/area and flux},
  Author                   = {Kolmogorov, V. and Boykov, Y.},
  Booktitle                = {Proc. Tenth IEEE Int. Conf. Computer Vision ICCV 2005},
  Year                     = {2005},
  Pages                    = {564--571},
  Volume                   = {1},

  Doi                      = {10.1109/ICCV.2005.252},
  Owner                    = {zickzack},
  Timestamp                = {2011.01.27}
}

@Article{KolmogorovGraphModStereoSegColContrast,
  Title                    = {Probabilistic fusion of stereo with color and contrast for bi-layer segmentation},
  Author                   = {Kolmogorov, V. and Criminisi, A. and Blake, A. and Cross, G. and Rother, C.},
  Journal                  = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  Year                     = {2006},
  Pages                    = {1480-1492},
  Volume                   = {28, no. 9},

  Abstract                 = {This paper describes models and algorithms for the real- time segmentation of foreground from background layers in stereo video sequences. Automatic separation of layers from color/contrast or from stereo alone is known to be error- prone. Here, color, contrast and stereo matching informa- tion are fused to infer layers accurately and efficiently. The first algorithm, Layered Dynamic Programming (LDP), solves stereo in an extended 6-state space that represents both fore- ground/background layers and occluded regions. The stereo- match likelihood is then fused with a contrast-sensitive color model that is learned on the fly, and stereo disparities are obtained by dynamic programming. The second algorithm, Layered Graph Cut (LGC), does not directly solve stereo. Instead the stereo match likelihood is marginalized over dis- parities to evaluate foreground and background hypotheses, and then fused with a contrast-sensitive color model like the one used in LDP. Segmentation is solved efficiently by ternary graph cut. Both algorithms are evaluated with respect to ground truth data and found to have similar performance, substantially better than either stereo or color/contrast alone. However, their characteristics with respect to computational efficiency are rather different. The algorithms are demonstrated in the application of background substitution and shown to give good quality composite video output.},
  Owner                    = {zickzack},
  Timestamp                = {2010.08.06}
}

@Article{KolmogorovGraphCutEnMin,
  Title                    = {What Energy Functions Can Be Minimized via Graph Cuts},
  Author                   = {Kolmogorov, Vladimir and Zabih, Ramin},
  Journal                  = {IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE},
  Year                     = {2004},
  Pages                    = {147-159},
  Volume                   = {26, no 2},

  Owner                    = {zickzack},
  Timestamp                = {2011.01.24}
}

@Article{Kondermann-SurfMeasure,
  Title                    = {Postprocessing of Optical Flows Via Surface Measures and Motion Inpainting},
  Author                   = {Kondermann, Claudia and Kondermann, Daniel and Garbe, Christoph S.},
  Journal                  = {Lecture Notes in Computer Science},
  Year                     = {2008},
  Pages                    = {355-364},
  Volume                   = {5096},

  Owner                    = {zickzack},
  Timestamp                = {2010.07.23}
}

@InCollection{kondermann2012performance,
  Title                    = {On performance analysis of optical flow algorithms},
  Author                   = {Kondermann, Daniel and Abraham, Steffen and Brostow, Gabriel and F{\"o}rstner, Wolfgang and Gehrig, Stefan and Imiya, Atsushi and J{\"a}hne, Bernd and Klose, Felix and Magnor, Marcus and Mayer, Helmut and others},
  Booktitle                = {Outdoor and Large-Scale Real-World Scene Analysis},
  Publisher                = {Springer Berlin Heidelberg},
  Year                     = {2012},
  Pages                    = {329--355}
}

@Article{KschischangFactorGrSumProd,
  Title                    = {Factor Graphs and the Sum-Product Algorithm},
  Author                   = {Kschischang, Frank R. and Frey, Brendan J. and Loeliger, Hans-Andrea},
  Journal                  = {IEEE TRANSACTIONS ON INFORMATION THEORY},
  Year                     = {2001},
  Volume                   = {47, No2},

  Owner                    = {zickzack},
  Timestamp                = {2010.08.06}
}

@Article{LeGuyaderJointSegRegistration2011,
  Title                    = {A combined segmentation and registration framework with a nonlinear elasticity smoother},
  Author                   = {Le Guyader, C. and Vese, L. A.},
  Journal                  = {Computer Vision and Image Understanding},
  Year                     = {2011},
  Number                   = {12},
  Pages                    = {1689--1709},
  Volume                   = {115},

  Owner                    = {gmwangi},
  Publisher                = {Elsevier},
  Timestamp                = {2015.01.14}
}

@Article{LeiferQuantumGraphMod,
  Title                    = {Quantum Graphical Models and Belief},
  Author                   = {Leifer, M. S. and Poulin, D.},
  Year                     = {2008},

  Owner                    = {zickzack},
  Timestamp                = {2010.08.25}
}

@InProceedings{LempitskyDiscrOptimOptFlow,
  Title                    = {FusionFlow: Discrete-continuous optimization for optical flow estimation},
  Author                   = {Lempitsky, Victor and Roth, Stefan and Rother, Carsten},
  Booktitle                = {Proc. IEEE Conf. Computer Vision and Pattern Recognition CVPR 2008},
  Year                     = {2008},
  Pages                    = {1--8},

  Doi                      = {10.1109/CVPR.2008.4587751},
  Owner                    = {zickzack},
  Timestamp                = {2010.07.23}
}

@Article{LenzLieGroupColorVis,
  Title                    = {Lie Methods For Color Robot Vision},
  Author                   = {Lenz, Reiner},
  Journal                  = {Robotica},
  Year                     = {2008},
  Pages                    = {453-464},
  Volume                   = {26},

  Owner                    = {zickzack},
  Timestamp                = {2011.07.15}
}

@Article{LieSIFTFlow,
  Title                    = {SIFT flow: dense correspondence across difference scenes and Its Applications},
  Author                   = {Liu, Ce and Yuen, Jenny and Torralba, Antonio},
  Journal                  = {IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE},
  Year                     = {2011},
  Pages                    = {978-994},
  Volume                   = {33 issue 5},

  Owner                    = {gerald},
  Timestamp                = {2012.07.05}
}

@Article{FieguthCompressedSensing,
  Title                    = {Compressed Sensing for Robust Texture Classication},
  Author                   = {Liu, Li and Fieguth, Paul and Kuang, Gangyao},
  Journal                  = {Lecture Notes in Computer Science},
  Year                     = {2011},
  Pages                    = {383-396},
  Volume                   = {6491/2011},

  Owner                    = {gerald},
  Timestamp                = {2012.03.12}
}

@Article{LoweSIFT,
  Title                    = {Distictive Image Features from Scale-Invariant Keypoints},
  Author                   = {Lowe, David G.},
  Journal                  = {International Journal of Computer Vision},
  Year                     = {2004},

  Owner                    = {gerald},
  Timestamp                = {2012.05.09}
}

@InProceedings{LukasKanadeImageReg,
  Title                    = {An iterative image registration technique with an application to stereo vision.},
  Author                   = {Lucas, B. D. and Kanade, T. and others},
  Booktitle                = {IJCAI},
  Year                     = {1981},
  Pages                    = {674--679},
  Volume                   = {81},

  Owner                    = {gmwangi},
  Timestamp                = {2015.02.05}
}

@Conference{Ma-StModOptFlow,
  Title                    = {Statistical Modeling of Optical Flow},
  Author                   = {Ma, Dongmin and Prinet, Veronique and Cassisa, Cyril},
  Booktitle                = {Fifth International Conference on Image and Graphics},
  Year                     = {2009},

  Journal                  = {Fifth International Conference on Image and Graphics},
  Owner                    = {zickzack},
  Timestamp                = {2010.07.23}
}

@Article{Mallat1989,
  Title                    = {A theory for multiresolution signal decomposition: the wavelet representation},
  Author                   = {Mallat, Stephane G.},
  Journal                  = ieee_j_pami,
  Year                     = {1989},
  Number                   = {7},
  Pages                    = {674--693},
  Volume                   = {11},

  Doi                      = {10.1109/34.192463},
  Owner                    = {zickzack},
  Timestamp                = {2010.07.23}
}

@Book{MansfieldInvarCalc,
  Title                    = {A practical guide to the invariant calculus},
  Author                   = {Mansfield, Elizabeth Louise},
  Publisher                = {Cambridge University Press},
  Year                     = {2010},
  Volume                   = {26},

  Owner                    = {gerald},
  Timestamp                = {2016.05.14}
}

@Article{RueMartinoINLA2007,
  Title                    = {Approximate Bayesian inference for hierarchical Gaussian Markov random field models},
  Author                   = {Martino, Sara and Rue, H{\aa}vard},
  Journal                  = {Journal of Statistical Planning and Inference},
  Year                     = {2007},
  Pages                    = {3177-3192},
  Volume                   = {137, issue 10},

  Owner                    = {zickzack},
  Timestamp                = {2011.01.10}
}

@Article{McCaneBenchmarkOptFlow2001,
  author    = {McCane, B. and Novins, K. and Crannitch, D. and Galvin, B.},
  title     = {On benchmarking optical flow},
  year      = {2001},
  volume    = {84},
  number    = {1},
  pages     = {126--143},
  journal   = {Computer Vision and Image Understanding},
  owner     = {gerald},
  publisher = {Elsevier},
  timestamp = {2013.04.12},
}

@Article{MerkerProlongation,
  Title                    = {Four explicit formulas for the prolongations of an infinitesimal Lie symmetry and multivariate Faa di Bruno formulas},
  Author                   = {Merker, Joel},
  Journal                  = {arXiv preprint math/0411650},
  Year                     = {2004},

  Owner                    = {gerald},
  Timestamp                = {2016.05.16}
}

@Article{mine2012projection,
  Title                    = {Projection-Based Augmented Reality in Disney Theme Parks},
  Author                   = {Mine, M. R. and van Baar, J. and Grundhofer, A. and Rose, D. and Yang, B.},
  Journal                  = {Computer},
  Year                     = {2012},
  Number                   = {7},
  Pages                    = {32--40},
  Volume                   = {45},

  Publisher                = {IEEE}
}

@PhdThesis{MinkaExpectationProp,
  Title                    = {A family of algorithms for approximate Bayesian inference},
  Author                   = {Minka, Thomas P.},
  School                   = {Massachusetts Institute Of Technology},
  Year                     = {2001},

  Owner                    = {zickzack},
  Timestamp                = {2010.09.15}
}

@Article{MumfordShah,
  Title                    = {Optimal Approximation by Piecewise Smooth Functions and Associated Varia- tional Problems},
  Author                   = {Mumford, D. and Shah, J.},
  Journal                  = {Comm. Pure and Applied Mathematics},
  Year                     = {1989},
  Pages                    = {577-685},
  Volume                   = {42},

  Owner                    = {zickzack},
  Timestamp                = {2010.08.02}
}

@Article{MurphyIntroGraphMod,
  Title                    = {An Introduction to Graphical Models},
  Author                   = {Murphy, Kevin P.},
  Year                     = {2001},

  Owner                    = {zickzack},
  Timestamp                = {2010.08.06}
}

@Article{NagelEnkelmannAnisSmoothOptFlow,
  Title                    = {An investigation of smoothness constraints for the estimation of displacement vector fields from image sequences},
  Author                   = {Nagel, Hans-Hellmut and Enkelmann, Wilfried},
  Journal                  = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
  Year                     = {1986},
  Number                   = {5},
  Pages                    = {565--593},

  Owner                    = {gerald},
  Publisher                = {IEEE},
  Timestamp                = {2016.01.29}
}

@InCollection{nair2012high,
  Title                    = {High accuracy TOF and stereo sensor fusion at interactive rates},
  Author                   = {Nair, R. and Lenzen, F. and Meister, S. and Sch{\"a}fer, H. and Garbe, C. and Kondermann, D.},
  Booktitle                = {Computer Vision--ECCV 2012. Workshops and Demonstrations},
  Publisher                = {Springer Berlin Heidelberg},
  Year                     = {2012},
  Pages                    = {1--11}
}

@Article{NoetherTheroemEng,
  Title                    = {Invariant Variation Problems},
  Author                   = {Noether, E.},
  Journal                  = {Transport Theory and Statistical Problem},
  Year                     = {1971},
  Pages                    = {183-207},
  Volume                   = {1 (3)},

  Owner                    = {zickzack},
  Timestamp                = {2011.11.03}
}

@Article{NoetherTheoremDeu,
  Title                    = {Invariante Variationsprobleme},
  Author                   = {Noether, E.},
  Journal                  = {Nachrichten Der Koeniglichen Gesellschaft Der Wissenschaften Zu Goettingen},
  Year                     = {1918},
  Pages                    = {235-257},
  Volume                   = {Mathematische-Physikalische Klasse},

  Owner                    = {zickzack},
  Timestamp                = {2011.11.03}
}

@Article{oliveira2012medical,
  Title                    = {Medical image registration: a review},
  Author                   = {Oliveira, F. and Tavares, J. M.},
  Journal                  = {Computer Methods in Biomechanics and Biomedical Engineering},
  Year                     = {2012},
  Pages                    = {1--21},

  Publisher                = {Taylor \& Francis}
}

@Article{OlverSymmetry,
  Title                    = {Symmetry groups and group invariant solutions of partial differential equations},
  Author                   = {Olver, Peter J and others},
  Journal                  = {J. Diff. Geom},
  Year                     = {1979},
  Pages                    = {497--542},
  Volume                   = {14},

  Owner                    = {gerald},
  Timestamp                = {2016.05.16}
}

@Article{OlverAnisotropicDenoiseEdgeDetec,
  author    = {Olver, Peter J and Sapiro, Guillermo and Tannenbaum, Allen},
  title     = {Affine invariant detection: edge maps, anisotropic diffusion, and active contours},
  year      = {1999},
  volume    = {59},
  number    = {1},
  pages     = {45--77},
  journal   = {Acta Applicandae Mathematica},
  owner     = {gerald},
  publisher = {Springer},
  timestamp = {2016.05.16},
}

@Article{Osher2005,
  author    = {Osher, S. and Burger, M. and Goldfarb, D. and Xu, J. and Yin, W.},
  title     = {An iterative regularization method for total variation-based image restoration},
  year      = {2005},
  volume    = {4},
  number    = {2},
  pages     = {460--489},
  journal   = {Multiscale Modeling \& Simulation},
  owner     = {gerald},
  publisher = {SIAM},
  timestamp = {2013.04.12},
}

@Article{OsherRudinShockFilter,
  Title                    = {Feature-oriented image enhancement using shock filters},
  Author                   = {Osher, Stanley and Rudin, Leonid I},
  Journal                  = {SIAM Journal on Numerical Analysis},
  Year                     = {1990},
  Number                   = {4},
  Pages                    = {919--940},
  Volume                   = {27},

  Owner                    = {gerald},
  Publisher                = {SIAM},
  Timestamp                = {2016.01.29}
}

@InBook{Fleet-OptFlow,
  Title                    = {Mathematical Models in Computer Vision: The Handbook},
  Author                   = {Paragios,Nikos},
  Chapter                  = {15,Optical Flow Estimation},
  Editor                   = {Chen, Faugeras},
  Pages                    = {239-258},
  Publisher                = {Springer},
  Year                     = {2005},

  Owner                    = {zickzack},
  Timestamp                = {2010.07.23}
}

@Article{ParagiosGeodActCont,
  Title                    = {Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects},
  Author                   = {Paragios, Nikos and Deriche, Rachid},
  Journal                  = {IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,},
  Year                     = {2000},
  Volume                   = {22, no 3},

  Owner                    = {zickzack},
  Timestamp                = {2010.08.03}
}

@Article{PaulsenMRFSurfaceConstrWillmoreFlow,
  Title                    = {Markov Random Field Surface Reconstruction},
  Author                   = {Paulsen, R. R. and Baerentzen, J. A. and Larsen, R.},
  Journal                  = {IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS},
  Year                     = {2010},
  Number                   = {4},
  Pages                    = {636--646},
  Volume                   = {16},

  Doi                      = {10.1109/TVCG.2009.208},
  Owner                    = {zickzack},
  Timestamp                = {2011.02.15}
}

@Book{PeskinQFT,
  Title                    = {An Introduction to Quantum Field Theory (Frontiers in Physics)},
  Author                   = {Peskin, Michael E. and Schroeder, Daniel V.},
  Publisher                = {Perseus Books},
  Year                     = {1995},

  Owner                    = {zickzack},
  Timestamp                = {2010.09.03}
}

@Article{Piella2009,
  author    = {Piella, Gemma},
  title     = {Image fusion for enhanced visualization: A variational approach},
  year      = {2009},
  volume    = {83},
  number    = {1},
  pages     = {1--11},
  journal   = {International Journal of Computer Vision},
  owner     = {gerald},
  publisher = {Springer},
  timestamp = {2013.04.12},
}

@Article{PollefeysSelfCalibMetricRecon,
  author    = {Pollefeys, M. and Koch, R.and Gool, L.},
  title     = {Self-Calibration and Metric Reconstruction Inspite of Varying and Unknown Intrinsic Camera Parameters},
  year      = {1999},
  language  = {English},
  volume    = {32},
  number    = {1},
  pages     = {7-25},
  issn      = {0920-5691},
  doi       = {10.1023/A:1008109111715},
  journal   = {International Journal of Computer Vision},
  keywords  = {self-calibration; metric 3D reconstruction; uncalibrated; image sequences; varying camera parameters},
  owner     = {gmwangi},
  publisher = {Kluwer Academic Publishers},
  timestamp = {2015.02.05},
}

@Article{pollefeysVisualModHandCam,
  Title                    = {Visual modeling with a hand-held camera},
  Author                   = {Pollefeys, M. and Van Gool, L. and Vergauwen, M. and Verbiest, F. and Cornelis, K. and Tops, J. and Koch, R.},
  Journal                  = {International Journal of Computer Vision},
  Year                     = {2004},
  Number                   = {3},
  Pages                    = {207--232},
  Volume                   = {59},

  Owner                    = {gmwangi},
  Publisher                = {Springer},
  Timestamp                = {2015.02.05}
}

@Article{PreusserAnisGeomDifLevelSet,
  Title                    = {Viscosity solutions of a level-set method for anisotropic geometric diffusion in image processing},
  Author                   = {Preusser, Tobias},
  Journal                  = {JOURNAL OF MATHEMATICAL IMAGING AND VISION},
  Year                     = {2007},
  Pages                    = {205-217},
  Volume                   = {29 issues 2, 3},

  Owner                    = {zickzack},
  Timestamp                = {2011.02.28}
}

@Article{Preusser-PhaseFieldMeth,
  Title                    = {A PHASE FIELD METHOD FOR JOINT DENOISING, EDGE DETECTION, AND MOTION ESTIMATION IN IMAGE SEQUENCE PROCESSING},
  Author                   = {Preusser, T. and Droske, M. and Garbe, C. S. and Telea, A. and Rumpf, M.},
  Journal                  = {Siam J., Appl Math},
  Year                     = {2007},
  Pages                    = {599-618},
  Volume                   = {68 no 3},

  Owner                    = {zickzack},
  Timestamp                = {2010.07.23}
}

@Article{PreusserLevelSetAnisGeomDiff,
  Title                    = {A Level Set Method for Anisotropic Geometric Diffusion in 3D Image Processing},
  Author                   = {Preusser, Tobias and Rumpf, Martin},
  Journal                  = {SIAM Journal on Applied Mathematics},
  Year                     = {2002},
  Pages                    = {1772-1793},
  Volume                   = {62 no 5},

  Owner                    = {zickzack},
  Timestamp                = {2011.02.23}
}

@Article{Ranchin2000,
  author    = {Ranchin, Thierry and Wald, Lucien and others},
  title     = {Fusion of high spatial and spectral resolution images: the ARSIS concept and its implementation},
  year      = {2000},
  volume    = {66},
  number    = {1},
  pages     = {49--61},
  journal   = {Photogrammetric Engineering \& Remote Sensing},
  owner     = {gerald},
  timestamp = {2013.04.12},
}

@Article{RappJaehneErrorsToF,
  Title                    = {A theoretical and experimental investigation of the systematic errors and statistical uncertainties of Time-Of-Flight-cameras},
  Author                   = {Rapp, Holger and Frank, Mario and Hamprecht, Fred A. and J\"{a}hne, Bernd},
  Journal                  = {Int. J. Intelligent Systems Technologies and Applications, Vol. 5, Nos. 3/4, 2008},
  Year                     = {2008},
  Volume                   = {5, no 3,4},

  Abstract                 = {The following paper presents a model to predict the systematic errors and statistical uncertainties of Time-Of-Flight (TOF) 3Dimaging systems. The experimental data obtained with a custom build test setup show that the SD of the depth signal rises approximately quadratically with the depth. The most significant systematic depth error is periodic with an amplitude of around 50mm. It is provoked by the inharmonic correlation function. The inhomogeneity in each pixel (fixed pattern) accounts for a depth error of about 20mm, while illumination and reflectivity variations cause depth errors of less than 10mm, provided that no overflows occur.},
  Owner                    = {zickzack},
  Timestamp                = {2010.08.30}
}

@InProceedings{PollefeysKeyFrame3DRecons,
  Title                    = {3D models from extended uncalibrated video sequences: Addressing key-frame selection and projective drift},
  Author                   = {Repko, J. and Pollefeys, M.},
  Booktitle                = {3-D Digital Imaging and Modeling, 2005. 3DIM 2005. Fifth International Conference on},
  Year                     = {2005},
  Organization             = {IEEE},
  Pages                    = {150--157},

  Owner                    = {gmwangi},
  Timestamp                = {2015.02.05}
}

@Book{RobertCasellaMCMCBook,
  Title                    = {Monte Carlo Statistical Methods},
  Author                   = {Robert, Christian P. and Casella, George},
  Publisher                = {Springer},
  Year                     = {2004},

  Owner                    = {zickzack},
  Timestamp                = {2010.11.12}
}

@Article{RocheCorrRatio,
  Title                    = {Multimodal image registration by maximization of the correlation ratio},
  Author                   = {Roche, Alexis and Malandain, Gr{\'e}goire and Ayache, Nicholas and Pennec, Xavier},
  Year                     = {1998},

  Owner                    = {gerald},
  Timestamp                = {2015.11.03}
}

@InCollection{Roche98CorrelRatio,
  author    = {Roche, A. and Malandain, G. and Pennec, X. and Ayache, N.},
  title     = {The correlation ratio as a new similarity measure for multimodal image registration},
  booktitle = {Medical Image Computing and Computer-Assisted Interventation},
  year      = {1998},
  publisher = {Springer},
  pages     = {1115--1124},
  owner     = {gerald},
  timestamp = {2013.04.08},
}

@Article{Roth-OnSpacSt,
  Title                    = {On The Spacial Statistics of Optical},
  Author                   = {Roth, Stefan and Black, Michael J.},
  Journal                  = {International Journal of Computer Vision},
  Year                     = {2007},
  Pages                    = {33-50},
  Volume                   = {74},

  Owner                    = {zickzack},
  Timestamp                = {2010.07.23}
}

@InProceedings{Roth-SteerableRandField2007,
  Title                    = {Steerable Random Fields},
  Author                   = {Roth, Stefan and Black, Michael J.},
  Booktitle                = {Proc. IEEE 11th Int. Conf. Computer Vision ICCV 2007},
  Year                     = {2007},
  Pages                    = {1--8},

  Doi                      = {10.1109/ICCV.2007.4408981},
  Owner                    = {zickzack},
  Timestamp                = {2010.07.23}
}

@InProceedings{RothFoE,
  Title                    = {Fields of Experts: a framework for learning image priors},
  Author                   = {Roth, Stefan and Black, Michael J.},
  Booktitle                = {Proc. IEEE Computer Society Conf. Computer Vision and Pattern Recognition CVPR 2005},
  Year                     = {2005},
  Pages                    = {860--867},
  Volume                   = {2},

  Doi                      = {10.1109/CVPR.2005.160},
  Owner                    = {zickzack},
  Timestamp                = {2010.07.23}
}

@Article{RudinShockFilter,
  Title                    = {Images, numerical analysis of singularities and shock filters},
  Author                   = {Rudin, Leonid I},
  Year                     = {1987},

  Owner                    = {gerald},
  Publisher                = {California Institute of Technology},
  Timestamp                = {2016.01.29}
}

@Article{RudinOsherFatemiTotalVariation,
  Title                    = {Nonlinear total variation based noise removal algorithms},
  Author                   = {Rudin, Leonid I and Osher, Stanley and Fatemi, Emad},
  Journal                  = {Physica D: Nonlinear Phenomena},
  Year                     = {1992},
  Number                   = {1},
  Pages                    = {259--268},
  Volume                   = {60},

  Owner                    = {gerald},
  Publisher                = {Elsevier},
  Timestamp                = {2015.11.02}
}

@Electronic{RueMartino-R-INLA-intro,
  author    = {Rue, H. and Martino, S.},
  title     = {Introduction to the R-INLA Package},
  owner     = {zickzack},
  timestamp = {2010.12.21},
}

@Article{HavardGMRFFastSampling,
  Title                    = {Fast Sampling of Gaussian Markov Random Fields},
  Author                   = {Rue, H{\aa}vard},
  Journal                  = {Journal of the Royal Statistical Society, Series B},
  Year                     = {2001},
  Pages                    = {325-338},
  Volume                   = {63},

  Owner                    = {zickzack},
  Timestamp                = {2010.10.15}
}

@Article{RueIntegrNestedLapl,
  Title                    = {Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations},
  Author                   = {Rue, H{\aa}vard and Martino, Sara},
  Journal                  = {Journal of the Royal Statistical Society: Series B},
  Year                     = {2009},
  Pages                    = {319-392},
  Volume                   = {71 Issue 2},

  Owner                    = {zickzack},
  Timestamp                = {2010.11.09}
}

@Article{Scharr-BCCEGeneral,
  Title                    = {Towards A Multi-Camera Generalization Of Brightness Constancy},
  Author                   = {Scharr, Hanno},
  Journal                  = {Lecture Notes in Computer Science},
  Year                     = {2007},
  Pages                    = {78-90},
  Volume                   = {3417/2007},

  Abstract                 = {Standard optical flow methods for motion or disparity estimation use a bright- ness constancy constraint equation (BCCE). This BCCE either handles a moving camera imaging a non-moving scene or a fixed camera imaging a moving scene. In this paper a BCCE is developed that can handle instantaneous motion of the camera on a 2D plane normal to the viewing direction and motion of the imaged scene. From the thus acquired up to 5 dimensional data set 3D object motion, 3D surface element position, and -normals can be estimated simultaneously. Experiments using 1d or 2d camera grids and a weighted to- tal least squares (TLS) estimation scheme demonstrate performance in terms of systematic error and noise stability, and show technical implications.},
  Owner                    = {zickzack},
  Timestamp                = {2010.07.23}
}

@InProceedings{ScharrSimEst3DFlowDepth,
  Title                    = {A linear model for simultaneous estimation of 3D motion and depth},
  Author                   = {Scharr, Hanno and K\"{u}sters, Ralf},
  Booktitle                = {Proc. Workshop Motion and Video Computing},
  Year                     = {2006},
  Pages                    = {220--225},

  Doi                      = {1182240},
  Owner                    = {zickzack},
  Timestamp                = {2010.08.11}
}

@InProceedings{ScharsteinSterDepthStructLight,
  Title                    = {High-accuracy stereo depth maps using structured light},
  Author                   = {Scharstein, Daniel and Szeliski, Richard},
  Booktitle                = {Proc. IEEE Computer Society Conf. Computer Vision and Pattern Recognition},
  Year                     = {2003},
  Volume                   = {1},

  Abstract                 = {Recent progress in stereo algorithm performance is quickly outpacing the ability of existing stereo data sets to discriminate among the best-performing algorithms, moti- vating the need for more challenging scenes with accurate ground truth information. This paper describes a method for acquiring high-complexity stereo image pairs with pixel-accurate correspondence information using struc- tured light. Unlike traditional range-sensing approaches, our method does not require the calibration of the light sources and yields registered disparity maps between all pairs of cameras and illumination projectors. We present new stereo data sets acquired with our method and demon- strate their suitability for stereo algorithm evaluation. Our results are available at http://www.middlebury.edu/stereo/},
  Doi                      = {10.1109/CVPR.2003.1211354},
  Owner                    = {zickzack},
  Timestamp                = {2010.08.12}
}

@Article{SchmidtJaehneModelToF,
  Title                    = {A Physical Model of Time-of-Flight 3D Imaging Systems, Including Suppression of Ambient Light},
  Author                   = {Schmidt, Mirko and J\"{a}hne, Bernd},
  Journal                  = {3rd Workshop on Dynamic 3-D Imaging,Springer},
  Pages                    = {1-15},
  Volume                   = {5742},

  Abstract                 = {We have developed a physical model of continuous-wave Time-of-Flight cameras, which focuses on a realistic reproduction of the sensor data. The derived simulation gives the ability to simulate data acquired by a ToF system with low computational effort. The model is able to use an arbitrary optical excitation and to simulate the sampling of a target response by a two-tap sensor, which can use any switch- ing function. Nonlinear photo response and pixel saturation, as well as spatial variations from pixel to pixel like photo response non-uniformity (PRNU) and dark signal non-uniformity (DSNU) can be modeled. Also the influence of interfering background light and on-sensor suppression of ambient light can be simulated. The model was verified by analyzing two scenarios: The cameras re- sponse to an increasing, homogeneous irradiation as well as the system- atic phase deviation caused by higher harmonics of the optical excitation. In both scenarios the model gave a precise reproduction of the observed data.},
  Owner                    = {zickzack},
  Timestamp                = {2010.08.30}
}

@Article{Schmidt1998,
  Title                    = {{World line path integrals as a calculational tool in quantum field theory}},
  Author                   = {Schmidt, Michael G. and Schubert, Christian},
  Year                     = {1998},

  Archiveprefix            = {arXiv},
  Eprint                   = {hep-th/9810161},
  Owner                    = {zickzack},
  Slaccitation             = {%%CITATION = HEP-TH/9810161;%%},
  Timestamp                = {2010.09.03}
}

@Article{Schmidt1996a,
  Title                    = {{Relativistic QFTH-couplings on the worldline}},
  Author                   = {Schmidt, Michael G. and Schubert, Christian},
  Year                     = {1996},

  Archiveprefix            = {arXiv},
  Eprint                   = {hep-th/9611044},
  Owner                    = {zickzack},
  Slaccitation             = {%%CITATION = HEP-TH/9611044;%%},
  Timestamp                = {2010.09.03}
}

@Article{Schmidt1994a,
  Title                    = {{The Worldline path integral approach to Feynman graphs}},
  Author                   = {Schmidt, Michael G. and Schubert, Christian},
  Year                     = {1994},

  Archiveprefix            = {arXiv},
  Eprint                   = {hep-ph/9412358},
  Owner                    = {zickzack},
  Slaccitation             = {%%CITATION = HEP-PH/9412358;%%},
  Timestamp                = {2010.09.03}
}

@Article{Schmidt2007,
  author    = {Schmidt, Roger},
  title     = {Benefits of IR/visible fusion},
  year      = {2007},
  volume    = {6541},
  pages     = {654105--1},
  journal   = {Proc. SPIE-The International Society for Optical Engineering, Orlando, USA},
  owner     = {gerald},
  timestamp = {2013.04.12},
}

@Article{SchmidtKappesSpinDetecGraphMod,
  Title                    = {Spine Detection and Labeling Using a Parts-Based Graphical Model},
  Author                   = {Schmidt, Stefan and Kappes, J\"{o}rg and Bergtholdt, Martin and Pekar, Vladimir and Dries, Sebastian and Bystrov , Daniel and Schn\"{o}rr, Christoph},
  Journal                  = {Proc. 20th International Conference on Information Processing in Medical Imaging (IPMI 2007)},
  Year                     = {2007},
  Pages                    = {122-133},
  Volume                   = {4584},

  Owner                    = {zickzack},
  Publisher                = {Springer},
  Series                   = {LCNS},
  Timestamp                = {2010.08.30}
}

@Article{SchnoerrAnisoFiltRegOptFlow,
  Title                    = {A Theoretical Framework for Convex Regularizers in PDE-Based Computation of Image Motion},
  Author                   = {Schn\"{o}rr, C. and Weickert, J.},
  Journal                  = {International Journal of Computer Vision},
  Year                     = {2001},
  Pages                    = {245-264},
  Volume                   = {45,no 3},

  Abstract                 = {Many differential methods for the recovery of the optic flow field from an image sequence can be expressed in terms of a variational problem where the optic flow minimizes some energy. Typically, these energy functionals consist of two terms: a data term, which requires e.g. that a brightness constancy assumption holds, and a regularizer that encourages global or piecewise smoothness of the flow field. In this paper we present a systematic classification of rotation invariant convex regularizers by exploring their connection to diffusion filters for multichannel images. This taxonomy provides a unifying framework for data-driven and flow-driven, isotropic and anisotropic, as well as spatial and spatio-temporal regularizers. While some of these techniques are classic methods from the literature, others are derived here for the first time. We prove that all these methods are well- posed: they posses a unique solution that depends in a continuous way on the initial data. An interesting structural relation between isotropic and anisotropic flow-driven regularizers is identified, and a design criterion is proposed for constructing anisotropic flow-driven regularizers in a simple and direct way from isotropic ones. Its use is illustrated by several examples.},
  Owner                    = {zickzack},
  Timestamp                = {2010.08.06}
}

@Article{FieguthSchneiderMultiscSeg,
  Title                    = {Multiscale methods for the segmentation and reconstruction of signals and images},
  Author                   = {Schneider, M. K. and Fieguth, P. W. and Karl, W. C. and Willsky, A. S.},
  Journal                  = {IEEE TRANSACTIONS ON IMAGE PROCESSING},
  Year                     = {2000},
  Number                   = {3},
  Pages                    = {456--468},
  Volume                   = {9},

  Doi                      = {10.1109/83.826782},
  Owner                    = {zickzack},
  Timestamp                = {2011.01.14}
}

@Article{SchroderNoetherTheorem,
  Title                    = {NOETHER'S THEOREM AND THE CONSERVATION LAWS IN CLASSICAL FIELD THEORIES},
  Author                   = {Schr\"{o}der, U.E.},
  Journal                  = {Fortsch.Phys.},
  Year                     = {1968},
  Pages                    = {357-372},
  Volume                   = {16}
}

@Article{Schubert2007,
  Title                    = {{QED in the worldline representation}},
  Author                   = {Schubert, Christian},
  Journal                  = {AIP Conf. Proc.},
  Year                     = {2007},
  Pages                    = {178-194},
  Volume                   = {917},

  Archiveprefix            = {arXiv},
  Doi                      = {10.1063/1.2751955},
  Eprint                   = {hep-th/0703186},
  Owner                    = {zickzack},
  Slaccitation             = {%%CITATION = HEP-TH/0703186;%%},
  Timestamp                = {2010.09.03}
}

@Article{Schubert2000,
  Title                    = {{QED in the worldline formalism}},
  Author                   = {Schubert, Christian},
  Year                     = {2000},

  Archiveprefix            = {arXiv},
  Eprint                   = {hep-ph/0011331},
  Owner                    = {zickzack},
  Slaccitation             = {%%CITATION = HEP-PH/0011331;%%},
  Timestamp                = {2010.09.03}
}

@InProceedings{LidarBoostSchuonTheobalt,
  Title                    = {LidarBoost: Depth superresolution for ToF 3D shape scanning},
  Author                   = {Schuon, S. and Theobalt, C. and Davis, J. and Thrun, S.},
  Booktitle                = {Proc. IEEE Conf. Computer Vision and Pattern Recognition CVPR 2009},
  Year                     = {2009},
  Pages                    = {343--350},

  Doi                      = {10.1109/CVPR.2009.5206804},
  Owner                    = {zickzack},
  Timestamp                = {2010.09.22}
}

@InProceedings{SchuonTheobaltToFSuperRes,
  Title                    = {High-quality scanning using time-of-flight depth superresolution},
  Author                   = {Schuon, S. and Theobalt, C. and Davis, J. and Thrun, S. },
  Booktitle                = {Proc. IEEE Computer Society Conf. Computer Vision and Pattern Recognition Workshops CVPRW '08},
  Year                     = {2008},
  Pages                    = {1--7},

  Doi                      = {10.1109/CVPRW.2008.4563171},
  Owner                    = {zickzack},
  Timestamp                = {2010.09.22}
}

@Article{SethianLevelSetFastMarchMeth,
  Title                    = {Evolution, Implementation, and Application of Level Set and Fast Marching Methods for Advancing Fronts},
  Author                   = {Sethian, James A.},
  Journal                  = {Journal of Computational Physics},
  Year                     = {2001},
  Pages                    = {503-555},
  Volume                   = {169},

  Owner                    = {zickzack},
  Timestamp                = {2011.02.21}
}

@Article{Shams2010,
  author    = {Shams, Ramtin and Sadeghi, Parastoo and Kennedy, Rodney and Hartley, Richard},
  title     = {A survey of medical image registration on multicore and the GPU},
  year      = {2010},
  volume    = {27},
  number    = {2},
  pages     = {50--60},
  journal   = {Signal Processing Magazine, IEEE},
  owner     = {gerald},
  publisher = {IEEE},
  timestamp = {2013.04.12},
}

@InProceedings{shi-tomasi-good-features,
  Title                    = {Good features to track},
  Author                   = {Shi, J. and Tomasi, C.},
  Booktitle                = {Computer Vision and Pattern Recognition, 1994. Proceedings CVPR'94., 1994 IEEE Computer Society Conference on},
  Year                     = {1994},
  Organization             = {IEEE},
  Pages                    = {593--600},

  Owner                    = {gmwangi},
  Timestamp                = {2015.02.05}
}

@Article{SnavelyModellingWorld,
  Title                    = {Modeling the world from internet photo collections},
  Author                   = {Snavely, N. and Seitz, S. M. and Szeliski, R.},
  Journal                  = {International Journal of Computer Vision},
  Year                     = {2008},
  Number                   = {2},
  Pages                    = {189--210},
  Volume                   = {80},

  Owner                    = {gmwangi},
  Publisher                = {Springer},
  Timestamp                = {2015.02.05}
}

@InProceedings{SnavelyPhotoTourism,
  Title                    = {Photo tourism: exploring photo collections in 3D},
  Author                   = {Snavely, N. and Seitz, S. M. and Szeliski, R.},
  Booktitle                = {ACM transactions on graphics (TOG)},
  Year                     = {2006},
  Number                   = {3},
  Organization             = {ACM},
  Pages                    = {835--846},
  Volume                   = {25},

  Owner                    = {gmwangi},
  Timestamp                = {2015.02.05}
}

@Article{SochenImageRiemann,
  Title                    = {A general framework for low level vision},
  Author                   = {Sochen, N. and Kimmel, R. and Malladi, R. },
  Journal                  = ieee_j_ip,
  Year                     = {1998},
  Number                   = {3},
  Pages                    = {310--318},
  Volume                   = {7},

  Doi                      = {10.1109/83.661181},
  Owner                    = {zickzack},
  Timestamp                = {2011.02.14}
}

@Article{SpiesJaehneRangeFlow,
  Title                    = {Range Flow Estimation},
  Author                   = {Spies, Hagen and J\"{a}hne, Bernd},
  Journal                  = {Computer Vision and Image Understanding},
  Year                     = {2002},
  Pages                    = {209-231},
  Volume                   = {85},

  Abstract                 = {We discuss the computation of the instantaneous 3D displacement vector fields of deformable surfaces from sequences of range data. We give a novel version of the basic motion constraint equation that can be evaluated directly on the sensor grid. The various forms of the aperture problem encountered are investigated and the derived constraint solutions are solved in a total least squares (TLS) framework. We propose a regularization scheme to compute dense full flow fields from the sparse TLS solutions. The performance of the algorithm is analyzed quantitatively for both synthetic and real data. Finally we apply the method to compute the 3D motion field of living plant leaves},
  Owner                    = {zickzack},
  Timestamp                = {2010.08.11}
}

@InProceedings{SpiesJaehneDenseRangeFlow,
  Title                    = {Dense range flow from depth and intensity data},
  Author                   = {Spies, Hagen and J\"{a}hne, Bernd and Barron, John L.},
  Booktitle                = {Proc. 15th Int Pattern Recognition Conf},
  Year                     = {2000},
  Pages                    = {131--134},
  Volume                   = {1},

  Abstract                 = {The combined use of intensity and depth information greatly helps in the estimation of the local 3D movements (range flow) of moving surfaces. We demonstrate how the two can be combined in both a local total least squares al- gorithm and in an iterative global variational technique. While the first assumes locally constant flow the second method relies on a smoothly varying flow field. The im- provement achieved through incorporating intensity is illus- trated qualitatively and quantitatively on synthetic and real test data.},
  Doi                      = {10.1109/ICPR.2000.905290},
  Owner                    = {zickzack},
  Timestamp                = {2010.08.11}
}

@Article{SpiessbergerFusionLockin,
  author    = {Spiessberger, C. and Gleiter, A. and Busse G.},
  title     = {Data Fusion of Lockin-Thermography Phase Images},
  year      = {2008},
  journal   = {9th International Conference on Quantitative InfraRed Thermography},
  owner     = {gerald},
  timestamp = {2013.04.07},
}

@InProceedings{Sun2010a,
  Title                    = {Secrets of optical flow estimation and their principles},
  Author                   = {Sun, D. and Roth, S. and Black, M. J.},
  Booktitle                = {Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on},
  Year                     = {2010},
  Organization             = {IEEE},
  Pages                    = {2432--2439}
}

@InProceedings{sun2010secrets,
  Title                    = {Secrets of optical flow estimation and their principles},
  Author                   = {Sun, D. and Roth, S. and Black, M. J.},
  Booktitle                = {Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on},
  Year                     = {2010},
  Organization             = {IEEE},
  Pages                    = {2432--2439}
}

@InProceedings{SunRothOpticalFlowCVPR2010,
  author       = {Sun, D. and Roth, S. and Black, M. J.},
  title        = {Secrets of optical flow estimation and their principles},
  booktitle    = {Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on},
  year         = {2010},
  organization = {IEEE},
  pages        = {2432--2439},
  owner        = {gerald},
  timestamp    = {2013.04.12},
}

@Article{Roth-LearnOptFlow,
  Title                    = {Learning Optical Flow},
  Author                   = {Sun, D. and Roth, S. and Lewis, J. P. and Black, M. J.},
  Journal                  = {Computer Vision - ECCV 2008},
  Year                     = {2008},
  Pages                    = {83-97},
  Volume                   = {5304/2008},

  Owner                    = {zickzack},
  Timestamp                = {2010.07.23}
}

@InProceedings{PollefeysLiveMetric3DReconstructionICCV2013,
  Title                    = {Live metric 3d reconstruction on mobile phones},
  Author                   = {Tanskanen, P. and Kolev, K. and Meier, L. and Camposeco, F. and Saurer, O. and Pollefeys, M.},
  Booktitle                = {Computer Vision (ICCV), 2013 IEEE International Conference on},
  Year                     = {2013},
  Organization             = {IEEE},
  Pages                    = {65--72},

  Owner                    = {gmwangi},
  Timestamp                = {2015.02.05}
}

@Article{TeleaJointEdgeFlowDet,
  Title                    = {A Variational Approach to Joint Denoising, Edge Detection and Motion Estimation},
  Author                   = {Telea, A. and Preusser,T. and Droske, M. and Garbe, C. S. and Rumpf, M.},
  Journal                  = {LNCS},
  Year                     = {2006},
  Pages                    = {525-535},
  Volume                   = {4174},

  Abstract                 = {The estimation of optical flow fields from image sequences is incorporated in a Mumford–Shah approach for image denoising and edge detection. Possibly noisy image sequences are considered as input and a piecewise smooth image intensity, a piecewise smooth motion field, and a joint discontinuity set are obtained as minimizers of the functional. The method simultaneously detects image edges and motion field dis- continuities in a rigorous and robust way. It comes along with a natural multi–scale approximation that is closely related to the phase field ap- proximation for edge detection by Ambrosio and Tortorelli. We present an implementation for 2D image sequences with finite elements in space and time. It leads to three linear systems of equations, which have to be iteratively in the minimization procedure. Numerical results underline the robustness of the presented approach and different applications are shown.},
  Owner                    = {zickzack},
  Timestamp                = {2010.08.02}
}

@Article{TheobaltComb3DOptFlowSilMoCap,
  Title                    = {Combining 3D Flow Fields with Silhouette-based Human Motion Capture for Immersive Video},
  Author                   = {Theobalt, Christian and Carranza, Joel and Magnor, Marcus A.},
  Journal                  = {Graphical Models},
  Year                     = {2003},
  Pages                    = {333-351},
  Volume                   = {66,Issue 6},

  Abstract                 = {In recent years, the convergence of Computer Vision and Computer Graphics has put forth a new field of research that focuses on the reconstruction of real-world scenes from video streams. To make immersive 3D video reality, not only the acquisition but also the real-time high-quality rendering of a recorded scene from an arbitrary novel viewpoint needs to be possible. In this paper, we describe latest advancements of our system to reconstruct and render free-viewpoint videos of human actors. We apply a silhouette-based non-intrusive motion capture algorithm which employs a 3D human body model to estimate the actor’s parameters of motion from multi-view video streams. A renderer plays back the acquired motion sequence in realt-ime from an arbitrary novel perspective. Realistic physical appearance of the moving actor is obtained by generating time-varying multi-view textures from video. In this work it is shown that the motion capture sub-system can be enhanced by incorporating texture information from the input video streams into the tracking process. 3D motion fields from optical flow are reconstructed that are used in combination with silhouette matching to estimate pose parameters. We demonstrate the high visual quality that is achieved with the proposed approach and validate the enhancements caused by the the motion field step.},
  Owner                    = {zickzack},
  Timestamp                = {2010.08.06}
}

@Article{TheobaltSilMotionCapWithOptFlow,
  Title                    = {Enhancing Silhouette-based Human Motion Capture with 3D Motion Fields},
  Author                   = {Theobalt, Christian and Carranza, Joel and Magnor, Marcus A.},
  Journal                  = {11th Pacific Conference on Computer Graphics and Applications},
  Year                     = {2003},
  Pages                    = {185},

  Abstract                 = {High-quality non-intrusive human motion capture is nec- essary for acquistion of model-based free-viewpoint video of human actors. Silhouette-based approaches have demon- strated that they are able to accurately recover a large range of human motion from multi-view video. However, they fail to make use of all available information, specif- ically that of texture information. This paper presents an algorithm that uses motion fields constructed from optical flow in multi-view video sequences. The use of motion fields augments the silhoutte-based method by incorporating texture-information into the track- ing process. The algorithm is a key-component in a larger free-viewpoint video system of human actors. Our results demonstrate that our method accurately estimates pose pa- rameters and allows for realistic texture generation in 3D video sequences.},
  Owner                    = {zickzack},
  Timestamp                = {2010.08.06}
}

@Article{MaTianSuperResImageMCMCSampling,
  Title                    = {Stochastic super-resolution image reconstruction},
  Author                   = {Tian, Jing and Ma, Kai-Kuang},
  Journal                  = {Journal of Visual Communication and Image Representation},
  Year                     = {2010},
  Pages                    = {232-244},
  Volume                   = {21 issue 3},

  Owner                    = {zickzack},
  Timestamp                = {2010.11.04}
}

@Book{TomasiKanadeTracking,
  Title                    = {Detection and tracking of point features},
  Author                   = {Tomasi, C. and Kanade, T.},
  Publisher                = {School of Computer Science, Carnegie Mellon Univ. Pittsburgh},
  Year                     = {1991},

  Owner                    = {gmwangi},
  Timestamp                = {2015.02.05}
}

@InProceedings{TomasiBilatFiltering,
  Title                    = {Bilateral filtering for gray and color images},
  Author                   = {Tomasi, C. and Manduchi, R. },
  Booktitle                = {Proc. Sixth Int Computer Vision Conf},
  Year                     = {1998},
  Pages                    = {839--846},

  Doi                      = {10.1109/ICCV.1998.710815},
  Owner                    = {zickzack},
  Timestamp                = {2010.09.29}
}

@Article{TorrMotionSegModelSel,
  Title                    = {Geometric motion segmentation and model selection},
  Author                   = {Torr, P.},
  Journal                  = {Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences},
  Year                     = {1998},
  Number                   = {1740},
  Pages                    = {1321--1340},
  Volume                   = {356},

  Owner                    = {gmwangi},
  Publisher                = {The Royal Society},
  Timestamp                = {2015.02.05}
}

@InProceedings{torr98aMultipleHypothesisMotionModel,
  Title                    = {Maintaining multiple motion model hypotheses over many views to recover matching and structure},
  Author                   = {Torr, P. and Fitzgibbon, A. W. and Zisserman, A.},
  Booktitle                = {Computer Vision, 1998. Sixth International Conference on},
  Year                     = {1998},
  Organization             = {IEEE},
  Pages                    = {485--491},

  Owner                    = {gmwangi},
  Timestamp                = {2015.02.05}
}

@Article{Town2007,
  author    = {Town, C.},
  title     = {Multi-sensory and multi-modal fusion for sentient computing},
  year      = {2007},
  volume    = {71},
  number    = {2},
  pages     = {235--253},
  journal   = {International Journal of Computer Vision},
  owner     = {gerald},
  publisher = {Springer},
  timestamp = {2013.04.12},
}

@InProceedings{TriggsAutoCalib,
  Title                    = {Autocalibration and the absolute quadric},
  Author                   = {Triggs, B.},
  Booktitle                = {Computer Vision and Pattern Recognition, 1997. Proceedings., 1997 IEEE Computer Society Conference on},
  Year                     = {1997},
  Organization             = {IEEE},
  Pages                    = {609--614},

  Owner                    = {gmwangi},
  Timestamp                = {2015.02.05}
}

@Article{Uras-MotionPercep,
  Title                    = {A computational approach to motion perception},
  Author                   = {Uras, S. and Girosi, F. and Verri, A. and Torre, V.},
  Journal                  = {Biological Cybernetics},
  Year                     = {1988},
  Pages                    = {79-87},
  Volume                   = {60,no 2},

  Owner                    = {zickzack},
  Timestamp                = {2010.07.23}
}

@Article{vadivambal2011applications,
  Title                    = {Applications of thermal imaging in agriculture and food industry - a review},
  Author                   = {Vadivambal, R. and Jayas, D. S},
  Journal                  = {Food and Bioprocess Technology},
  Year                     = {2011},
  Number                   = {2},
  Pages                    = {186--199},
  Volume                   = {4},

  Publisher                = {Springer}
}

@PhdThesis{HermosilloPHDMatching,
  Title                    = {Variational Methods for Multimodal Image Matching},
  Author                   = {Valadez, Gerardo Hermosillo},
  School                   = {\'{E}cole Doctorale Sciences et Technologies de l’Information et de la Communication},
  Year                     = {2002},

  Owner                    = {gerald},
  Timestamp                = {2012.03.12}
}

@Article{VeseMultPhaseLevelset,
  Title                    = {A multiphase level set framework for image segmentation using the Mumford and shah model},
  Author                   = {Vese, L. A. and Chan, T. F.},
  Journal                  = {International Journal of Computer Vision},
  Year                     = {2002},
  Pages                    = {271-293},
  Volume                   = {50 no 3},

  Owner                    = {zickzack},
  Timestamp                = {2011.02.10}
}

@PhdThesis{VigodaAnalogLogicGraphMod,
  Title                    = {Analog Logic: Continuous-Time Analog Circuits for Statistical Signal Processing},
  Author                   = {Vigoda, Benjmin},
  School                   = {Massachusetts Institute Of Technology},
  Year                     = {2003},

  Owner                    = {zickzack},
  Timestamp                = {2010.08.18}
}

@Article{Vitti2012,
  Title                    = {The Mumford--Shah variational model for image segmentation: An overview of the theory, implementation and use},
  Author                   = {Vitti, Alfonso},
  Journal                  = {ISPRS Journal of Photogrammetry and Remote Sensing},
  Year                     = {2012},
  Pages                    = {50--64},
  Volume                   = {69},

  Owner                    = {gmwangi},
  Publisher                = {Elsevier},
  Timestamp                = {2015.01.14}
}

@Article{WainwrightGraphModVar,
  Title                    = {Graphical Models, exponential families and variational inference},
  Author                   = {Wainwright, Martin J. and Jordan, Michael I.},
  Journal                  = {Foundations and Trends R in Machine Learning},
  Year                     = {2008},
  Pages                    = {1-305},
  Volume                   = {Vol. 1, Nos. 1-2},

  Abstract                 = {The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building large-scale multivariate statistical models. Graphical models have become a focus of research in many statisti- cal, computational and mathematical fields, including bioinformatics, communication theory, statistical physics, combinatorial optimiza- tion, signal and image processing, information retrieval and statistical machine learning. Many problems that arise in specific instances — including the key problems of computing marginals and modes of probability distributions — are best studied in the general setting. Working with exponential family representations, and exploiting the conjugate duality between the cumulant function and the entropy for exponential families, we develop general variational representa- tions of the problems of computing likelihoods, marginal probabili- ties and most probable configurations. We describe how a wide variety},
  Owner                    = {zickzack},
  Timestamp                = {2010.08.03}
}

@Article{WalessaDespekImageGMRF,
  Title                    = {Model-based despeckling and information extraction from SAR images},
  Author                   = {Walessa, M. and Datcu, M. },
  Journal                  = ieee_j_grs,
  Year                     = {2000},
  Number                   = {5},
  Pages                    = {2258--2269},
  Volume                   = {38},

  Doi                      = {10.1109/36.868883},
  Owner                    = {zickzack},
  Timestamp                = {2010.11.04}
}

@Article{WangGeodesActContTensorVoting,
  Title                    = {A Variational Approach for Image Segmentation Using Tensor Voting},
  Author                   = {Wang, Wei and Pi, Ling},
  Journal                  = {Chinese Journal of Electronics},
  Year                     = {2010},
  Volume                   = {19 no. 2},

  Owner                    = {zickzack},
  Timestamp                = {2011.02.16}
}

@Article{WangSSIM,
  Title                    = {Image quality assessment: from error visibility to structural similarity},
  Author                   = {Wang, Z. and Bovik, A. C. and Sheikh, H. R. and Simoncelli, E. P. },
  Journal                  = {IEEE Transactions on Image Processing},
  Year                     = {2004},
  Number                   = {4},
  Pages                    = {600--612},
  Volume                   = {13},

  Doi                      = {10.1109/TIP.2003.819861},
  Owner                    = {gerald},
  Timestamp                = {2012.03.04}
}

@InCollection{wedel2009improved,
  Title                    = {An improved algorithm for TV-L 1 optical flow},
  Author                   = {Wedel, A. and Pock, T. and Zach, C. and Bischof, H. and Cremers, D.},
  Booktitle                = {Statistical and Geometrical Approaches to Visual Motion Analysis},
  Publisher                = {Springer},
  Year                     = {2009},
  Pages                    = {23--45}
}

@Article{weickertCoherenceenhancing,
  Title                    = {Coherence-Enhancing Diffusion Filtering},
  Author                   = {Weickert, J.},
  Journal                  = {International Journal of Computer Vision},
  Year                     = {1999},
  Pages                    = {111-127},
  Volume                   = {31},

  Owner                    = {zickzack},
  Timestamp                = {2011.09.15}
}

@Article{weickertAnisDiffTheory,
  Title                    = {Theoretical Foundations Of Anisotropic Diffusion In Image Processing},
  Author                   = {Weickert, J.},
  Journal                  = {Computing, Suppl.},
  Year                     = {1996},
  Pages                    = {221-236},
  Volume                   = {11},

  Owner                    = {zickzack},
  Timestamp                = {2011.09.15}
}

@Article{WongFieguthCPOLRegis,
  Title                    = {CPOL: Complex phase order likelihood as a similarity measure for MR–CT registration},
  Author                   = {Wong, Alexander and Clausi, David A. and Fieguth, Paul},
  Journal                  = {Medical Image Analysis},
  Year                     = {2009},
  Volume                   = {doi:10.1016/j.media.2009.10.002},

  Owner                    = {gerald},
  Timestamp                = {2012.01.09}
}

@Article{Wong2011,
  author    = {Wong, WK and Zhao, Haitao},
  title     = {Eyeglasses removal of thermal image based on visible information},
  year      = {2011},
  journal   = {Information Fusion},
  owner     = {gerald},
  publisher = {Elsevier},
  timestamp = {2013.04.12},
}

@InProceedings{wong2009effective,
  Title                    = {An effective surveillance system using thermal camera},
  Author                   = {Wong, Wai Kit and Tan, Poi Ngee and Loo, Chu Kiong and Lim, Way Soong},
  Booktitle                = {Signal Acquisition and Processing, 2009. ICSAP 2009. International Conference on},
  Year                     = {2009},
  Organization             = {IEEE},
  Pages                    = {13--17}
}

@Article{WoodsEMSuperres,
  Title                    = {Stochastic methods for joint registration, restoration, and interpolation of multiple undersampled images},
  Author                   = {Woods, N. A. and Galatsanos, N. P. and Katsaggelos, A. K.},
  Journal                  = IEEE_J_IP,
  Year                     = {2006},
  Number                   = {1},
  Pages                    = {201--213},
  Volume                   = {15},

  Doi                      = {10.1109/TIP.2005.860355},
  Owner                    = {zickzack},
  Timestamp                = {2011.07.07}
}

@Article{wyseChangepointINLA,
  author        = {Wyse, Jason and Friel, Nial and Rue, H{\aa}vard},
  title         = {Approximate simulation-free Bayesian inference for multiple changepoint models with dependence within segments},
  year          = {2010},
  month         = nov,
  eprint        = {1011.5038},
  abstract      = {This paper proposes approaches for the analysis of multiple changepoint models when dependency in the data is modelled through a hierarchical Gaussian Markov random field. Integrated nested Laplace approximations are used to approximate data quantities, and an approximate filtering recursions approach is proposed for savings in compuational cost when detecting changepoints. All of these methods are simulation free. Analysis of real data demonstrates the usefulness of the approach in general. The new models which allow for data dependence are compared with conventional models where data within segments is assumed independent.},
  adsnote       = {Provided by the SAO/NASA Astrophysics Data System},
  archiveprefix = {arXiv},
  journal       = {ArXiv e-prints},
  keywords      = {Statistics - Computation},
  primaryclass  = {stat.CO},
}

@Article{YezziZoelleiVarJointSegRegGeomCont,
  Title                    = {A Variational Framework for Joint Segmentation and Registration},
  Author                   = {Yezzi, Anthoni and Z\"{o}llei, Lilla and Kapur, Tina},
  Journal                  = {Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis},
  Year                     = {2001},
  Pages                    = {44},

  Abstract                 = {Traditionally, segmentation and registration have been solved as two independent problems, even though it is often the case that the solution to one impacts the solution to the other. In this paper, we introduce a geometric, variational framework that uses active con- tours to simultaneously segment and register features from multi- ple images. The key observation is that multiple images may be segmented by evolving a single contour as well as the mappings of that contour into each image. To the best of our knowledge, this is the first attempt at interleaving segmentation and registration in such a framework.},
  Owner                    = {zickzack},
  Timestamp                = {2010.08.04}
}

@Article{ZhangFieguthStrCorEnRegis,
  Title                    = {Efficient Globally Optimal Registration of Remote Sensing Imagery via Quasi-Random Scale-Space Structural Correlation Energy Functional},
  Author                   = {Zhang, W. and Wong, A. and Mishra, A. and Fieguth, P. and Clausi, D. A.},
  Journal                  = {IEEE GEOSCIENCE AND REMOTE SENSING LETTERS},
  Year                     = {2011},
  Pages                    = {997-1001},
  Volume                   = {8 No. 5},

  Owner                    = {gerald},
  Timestamp                = {2012.01.09}
}

@PhdThesis{XinhuaZhangGraphMod,
  Title                    = {Graphical Models: Modeling, Optimization, and Hilbert Space Embedding},
  Author                   = {Zhang, Xinhua},
  Year                     = {2010},

  Abstract                 = {Over the past two decades graphical models have been widely used as powerful tools for compactly representing distributions. On the other hand, kernel methods have been used extensively to come up with rich representations. This thesis aims to combine graphical models with kernels to produce compact models with rich representational abilities. Graphical models are a powerful underlying formalism in machine learning. Their graph theoretic properties provide both an intuitive modular interface to model the interacting factors, and a data structure facilitating efficient learning and inference. The probabilistic nature ensures the global consistency of the whole framework, and allows convenient interface of models to data. Kernel methods, on the other hand, provide an effective means of representing rich classes of features for general objects, and at the same time allow efficient search for the optimal model. Recently, kernels have been used to characterize distributions by embedding them into high dimensional feature space. Interestingly, graphical models again decompose this characterization and lead to novel and direct ways of comparing distributions based on samples. Among the many uses of graphical models and kernels, this thesis is devoted to the following four areas: Conditional random fields for multi-agent reinforcement learning Condi- tional random fields (CRFs) are graphical models for modeling the probability of la- bels given the observations. They have traditionally been trained with using a set of observation and label pairs. Underlying all CRFs is the assumption that, conditioned on the training data, the label sequences of different training examples are independent and identically distributed (iid ). We extended the use of CRFs to a class of tempo- ral learning algorithms, namely policy gradient reinforcement learning (RL). Now the labels are no longer iid. They are actions that update the environment and affect the next observation. From an RL point of view, CRFs provide a natural way to model joint actions in a decentralized Markov decision process. They define how agents can communicate with each other to choose the optimal joint action. We tested our frame- work on a synthetic network alignment problem, a distributed sensor network, and a road traffic control system. Using tree sampling by Hamze & de Freitas (2004) for inference, the RL methods employing CRFs clearly outperform those which do not vii viii model the proper joint policy. Bayesian online multi-label classification Gaussian density filtering (GDF) pro- vides fast and effective inference for graphical models (Maybeck, 1982). Based on this natural online learner, we propose a Bayesian online multi-label classification (BOMC) framework which learns a probabilistic model of the linear classifier. The training la- bels are incorporated to update the posterior of the classifiers via a graphical model similar to TrueSkill (Herbrich et al., 2007), and inference is based on GDF with ex- pectation propagation. Using samples from the posterior, we label the test data by maximizing the expected F-score. Our experiments on Reuters1-v2 dataset show that BOMC delivers significantly higher macro-averaged F-score than the state-of-the-art online maximum margin learners such as LaSVM (Bordes et al., 2005) and passive- aggressive online learning (Crammer et al., 2006). The online nature of BOMC also allows us to efficiently use a large amount of training data. Hilbert space embedment of distributions Graphical models are also an essen- tial tool in kernel measures of independence for non-iid data. Traditional information theory often requires density estimation, which makes it unideal for statistical esti- mation. Motivated by the fact that distributions often appear in machine learning via expectations, we can characterize the distance between distributions in terms of distances between means, especially means in reproducing kernel Hilbert spaces which are called kernel embedment. Under this framework, the undirected graphical models further allow us to factorize the kernel embedment onto cliques, which yields efficient measures of independence for non-iid data (Zhang et al., 2009). We show the effective- ness of this framework for ICA and sequence segmentation, and a number of further applications and research questions are identified. Optimization in maximum margin models for structured data Maximum margin estimation for structured data, e.g. (Taskar et al., 2004), is an important task in machine learning where graphical models also play a key role. They are special cases of regularized risk minimization, for which bundle methods (BMRM, Teo et al., 2007) and the closely related SVMStruct (Tsochantaridis et al., 2005) are state-of-the-art general purpose solvers. Smola et al. (2007b) proved that BMRM requires O(1/ ) iterations to converge to an accurate solution, and we further show that this rate hits the lower bound. By utilizing the structure of the objective function, we devised an algorithm √ for the structured loss which converges to an accurate solution in O(1/ ) iterations. This algorithm originates from Nesterov’s optimal first order methods (Nesterov, 2003, 2005b).},
  Owner                    = {zickzack},
  Timestamp                = {2010.08.23}
}

@Article{ZhangSpatialResEnhWavelet,
  Title                    = {Spatial Resolution Enhancement for Hyperspectral Image Based on Wavelet Baysian Fusion},
  Author                   = {Zhang, Y.},
  Journal                  = {4th International Congress on Image and Signal Processing},
  Year                     = {2011},

  Owner                    = {gerald},
  Timestamp                = {2013.03.26}
}

@Article{ZhaoMumfShahStructTens,
  Title                    = {Image Restoration Under Significant Additive Noise},
  Author                   = {Zhao, W. and Pope, A.},
  Journal                  = {Signal Processing Letters, IEEE},
  Year                     = {2007},
  Number                   = {6},
  Pages                    = {401--404},
  Volume                   = {14},

  Doi                      = {10.1109/LSP.2006.887843},
  Owner                    = {zickzack},
  Timestamp                = {2011.09.06}
}

@InProceedings{Zhu2008,
  author       = {Zhu, Jiejie and Wang, Liang and Yang, Ruigang and Davis, James},
  title        = {Fusion of time-of-flight depth and stereo for high accuracy depth maps},
  booktitle    = {Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on},
  year         = {2008},
  organization = {IEEE},
  pages        = {1--8},
  owner        = {gerald},
  timestamp    = {2013.04.12},
}

@InProceedings{ZhuFusionTOFandStereoVisForDepth,
  Title                    = {Fusion of time-of-flight depth and stereo for high accuracy depth maps},
  Author                   = {Zhu, Jiejie and Wang, Liang and Yang, Ruigang and Davis, James},
  Booktitle                = {Proc. IEEE Conf. Computer Vision and Pattern Recognition CVPR 2008},
  Year                     = {2008},
  Pages                    = {1--8},

  Abstract                 = {Time-of-flight range sensors have error characteristics which are complementary to passive stereo. They provide real time depth estimates in conditions where passive stereo does not work well, such as on white walls. In contrast, these sensors are noisy and often perform poorly on the textured scenes for which stereo excels. We introduce a method for combining the results from both methods that performs better than either alone. A depth probability distribution function from each method is calculated and then merged. In addition, stereo methods have long used global methods such as belief propagation and graph cuts to improve results, and we apply these methods to this sensor. Since time-of-flight devices have primarily been used as individual sensors, they are typically poorly calibrated. We introduce a method that substantially improves upon the manufacturerpsilas calibration. We show that these techniques lead to improved accuracy and robustness.},
  Doi                      = {4587761},
  Owner                    = {zickzack},
  Timestamp                = {2010.08.11}
}

@Book{ZinnJustinQFT,
  Title                    = {Quantum Field Theory and Critical Phenomena (International Series of Monographs on Physics)},
  Author                   = {Zinn-Justin, Jean},
  Publisher                = {Oxford University Pres},
  Year                     = {2002},

  Owner                    = {zickzack},
  Timestamp                = {2010.09.03}
}

@Article{Zitova2003,
  author    = {Zitova, B. and Flusser, J.},
  title     = {Image registration methods: a survey},
  year      = {2003},
  volume    = {21},
  number    = {11},
  pages     = {977--1000},
  journal   = {Image and vision computing},
  owner     = {gerald},
  publisher = {Elsevier},
  timestamp = {2013.04.12},
}

@InProceedings{Zou2005,
  author       = {Zou, X. and Bhanu, B.},
  title        = {Tracking humans using multi-modal fusion},
  booktitle    = {Computer Vision and Pattern Recognition-Workshops, 2005. CVPR Workshops. IEEE Computer Society Conference on},
  year         = {2005},
  organization = {IEEE},
  pages        = {4--4},
  owner        = {gerald},
  timestamp    = {2013.04.12},
}

@Book{FeynmanLectures,
  author       = {Feynman, Richard Phillips and Leighton, Robert B. and Sands, Matthewp},
  title        = {{The Feynman Lectures on Physics}},
  year         = {1963},
  isbn         = {0201021153, 9780201021158},
  url          = {http://www.feynmanlectures.info/},
  slaccitation = {%%CITATION = INSPIRE-1283666;%%},
}

@Book{KugoGaugeTheory,
  author       = {Kugo, Taichiro},
  title        = {{Eichtheorie}},
  year         = {1997},
  doi          = {10.1007/978-3-642-59128-0},
  slaccitation = {%%CITATION = INSPIRE-1330920;%%},
}

@Article{FadiliTV,
  author   = {J. M. Fadili and G. Peyre},
  title    = {Total Variation Projection With First Order Schemes},
  year     = {2011},
  volume   = {20},
  number   = {3},
  month    = mar,
  pages    = {657--669},
  issn     = {1057-7149},
  doi      = {10.1109/TIP.2010.2072512},
  journal  = {IEEE Transactions on Image Processing},
  keywords = {image texture, iterative methods, optimisation, smoothing methods, television, TV projection algorithm, and tomography problems, dual vector field, first order nonsmooth optimization methods, image deconvolution, image denoising, image inpainting, image projection algorithm, image texture synthesis, inverse problems, iterative algorithm, iterative soft thresholding, total variation constraint, total variation projection, Convergence, Convex functions, Inverse problems, Noise reduction, Optimization, Projection algorithms, TV, Duality, Nesterov scheme, forward-backward splitting, inverse problems, projection, proximal operator, total variation, Algorithms, Image Processing, Computer-Assisted, Models, Theoretical, Phantoms, Imaging, Tomography},
}

@InProceedings{BayramTV,
  author    = {{{\. {I}}} Bayram and M. E. Kamasak},
  title     = {A directional total variation},
  booktitle = {Proc. 20th European Signal Processing Conf. (EUSIPCO)},
  year      = {2012},
  month     = aug,
  pages     = {265--269},
  doi       = {10.1109/LSP.2012.2220349},
  issn      = {2219-5491},
  keywords  = {image denoising, directional TV, directional total variation, dominant direction, image denoising, isotropic TV, isotropic image, Image denoising, Noise, Noise level, Noise measurement, Noise reduction, Signal processing algorithms, TV, directional total variation, image denoising, total variation},
}

@InProceedings{DongTV,
  author    = {F. Dong and Z. Liu and J. Peng},
  title     = {A novel variational model for multiplicative noise removal by combining nonlocal and weberized total variation regularizations},
  booktitle = {Proc. 4th Int Image and Signal Processing (CISP) Congress},
  year      = {2011},
  volume    = {1},
  month     = oct,
  pages     = {42--46},
  doi       = {10.1109/CISP.2011.6099910},
  keywords  = {image denoising, image restoration, image texture, radar imaging, sonar imaging, synthetic aperture radar, ultrasonic imaging, NLTV, PDHG, SAR, Weberized total variation regularizations, laser imaging, multiplicative noise removal, nonlocal total variation regularizations, primal-dual hybrid gradient algorithm, sonar imaging, speckle noise, synthetic aperture radar imaging, ultrasound imaging, variational model, Image restoration, Imaging, Mathematical model, PSNR, TV},
}

@Misc{LenzenVariational,
  author    = {Scherzer, Otmar and Grasmair, Markus and Grossauer, Harald and Haltmeier, Markus and Lenzen, Frank},
  title     = {Variational methods in imaging, volume 167 of Applied Mathematical Sciences},
  year      = {2009},
  publisher = {Springer, New York},
}

@Article{ShewChukconjugateGradients,
  author  = {Shewchuk, Jonathan Richard},
  title   = {An introduction to the conjugate gradient method without the agonizing pain, 1994},
  year    = {1994},
  journal = {URL http://www-2. cs. cmu. edu/jrs/jrspapers. html\# cg},
}

@InCollection{EvansLevelSetMeanCurvature,
  author    = {Evans, LC and Spruck, J},
  title     = {Motion of Level Sets by Mean Curvature. I},
  booktitle = {Fundamental contributions to the continuum theory of evolving phase interfaces in solids},
  year      = {1999},
  publisher = {Springer},
  pages     = {328--374},
}

@Book{WolfOptflowKeyframeSel,
  author    = {Richardson, Iain E},
  title     = {H. 264 and MPEG-4 video compression: video coding for next-generation multimedia},
  year      = {2004},
  publisher = {John Wiley \& Sons},
}

@Article{HanVideoComprOpticalFlow,
  author    = {Han, Soo-Chul and Podilchuk, Christine I},
  title     = {Video compression with dense motion fields},
  year      = {2001},
  volume    = {10},
  number    = {11},
  pages     = {1605--1612},
  journal   = {IEEE Transactions on Image Processing},
  publisher = {IEEE},
}

@InProceedings{HanVideoComprOpticalFlow99,
  author       = {Han, Soo-Chul and Podilchuk, Christine I},
  title        = {Efficient encoding of dense motion fields for motion-compensated video compression},
  booktitle    = {Image Processing, 1999. ICIP 99. Proceedings. 1999 International Conference on},
  year         = {1999},
  volume       = {1},
  organization = {IEEE},
  pages        = {84--88},
}

@Article{BayramDirectionalTV,
  author    = {Bayram, Ilker and Kamasak, Mustafa E},
  title     = {Directional total variation},
  year      = {2012},
  volume    = {19},
  number    = {12},
  pages     = {781--784},
  journal   = {IEEE Signal Processing Letters},
  publisher = {IEEE},
}

@Book{weicketAnisotropicDiffusionBook,
  author    = {Weickert, Joachim},
  title     = {Anisotropic diffusion in image processing},
  year      = {1998},
  volume    = {1},
  publisher = {Teubner Stuttgart},
}

@InProceedings{weickertTextureEnhStructTens,
  author       = {Weickert, Joachim},
  title        = {Multiscale texture enhancement},
  booktitle    = {International Conference on Computer Analysis of Images and Patterns},
  year         = {1995},
  organization = {Springer},
  pages        = {230--237},
}

@Article{NitzbergStructTensDiffusion,
  author    = {Nitzberg, Mark and Shiota, Takahiro},
  title     = {Nonlinear image filtering with edge and corner enhancement},
  year      = {1992},
  volume    = {14},
  number    = {8},
  pages     = {826--833},
  journal   = {IEEE transactions on pattern analysis and machine intelligence},
  publisher = {IEEE},
}

@Article{MaesMutualInformationRegistration,
  author    = {Maes, Frederik and Collignon, Andre and Vandermeulen, Dirk and Marchal, Guy and Suetens, Paul},
  title     = {Multimodality image registration by maximization of mutual information},
  year      = {1997},
  volume    = {16},
  number    = {2},
  pages     = {187--198},
  journal   = {IEEE transactions on Medical Imaging},
  publisher = {IEEE},
}

@PhdThesis{RocheUnifyingMaxLikelihoodRegistration,
  author = {Roche, Alexis and Malandain, Gregoire and Ayache, Nicholas},
  title  = {Unifying maximum likelihood approaches in medical image registration},
  year   = {1999},
  school = {Inria},
}

@InProceedings{NetschCrossCorrRegistr,
  author       = {Netsch, Thomas and Rosch, Peter and van Muiswinkel, Arianne and Weese, J{\"u}rgen},
  title        = {Towards real-time multi-modality 3-D medical image registration},
  booktitle    = {Computer Vision, 2001. ICCV 2001. Proceedings. Eighth IEEE International Conference on},
  year         = {2001},
  volume       = {1},
  organization = {IEEE},
  pages        = {718--725},
}

@InProceedings{CachierCrossCorrRegistration,
  author       = {Cachier, Pascal and Pennec, Xavier},
  title        = {3D non-rigid registration by gradient descent on a gaussian-windowed similarity measure using convolutions},
  booktitle    = {Mathematical Methods in Biomedical Image Analysis, 2000. Proceedings. IEEE Workshop on},
  year         = {2000},
  organization = {IEEE},
  pages        = {182--189},
}

@InProceedings{ZachTVL1OpticalFlow,
  author       = {Zach, Christopher and Pock, Thomas and Bischof, Horst},
  title        = {A duality based approach for realtime TV-L 1 optical flow},
  booktitle    = {Joint Pattern Recognition Symposium},
  year         = {2007},
  organization = {Springer},
  pages        = {214--223},
}

@Article{PapenbergTVOpticalFlow,
  author    = {Papenberg, Nils and Bruhn, Andr{\'e}s and Brox, Thomas and Didas, Stephan and Weickert, Joachim},
  title     = {Highly accurate optic flow computation with theoretically justified warping},
  year      = {2006},
  volume    = {67},
  number    = {2},
  pages     = {141--158},
  journal   = {International Journal of Computer Vision},
  publisher = {Springer},
}

@Book{BishopNeuralNetworkPatRec,
  author    = {Bishop, Christopher M},
  title     = {Neural networks for pattern recognition},
  year      = {1995},
  publisher = {Oxford university press},
}

@Article{ZetscheLimitsOfLinFilters,
  author    = {Zetzsche, Christoph and Barth, Erhardt},
  title     = {Fundamental limits of linear filters in the visual processing of two-dimensional signals},
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@Article{khanCFRP,
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@Article{ferreiraCFRP,
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  abstract = {The quantitative evaluation of optical flow algorithms by Barron et al. (1994) led to significant advances in performance. The challenges for optical flow algorithms today go beyond the datasets and evaluation methods proposed in that paper. Instead, they center on problems associated with complex natural scenes, including nonrigid motion, real sensor noise, and motion discontinuities. We propose a new set of benchmarks and evaluation methods for the next generation of optical flow algorithms. To that end, we contribute four types of data to test different aspects of optical flow algorithms: (1) sequences with nonrigid motion where the ground-truth flow is determined by tracking hidden fluorescent texture, (2) realistic synthetic sequences, (3) high frame-rate video used to study interpolation error, and (4) modified stereo sequences of static scenes. In addition to the average angular error used by Barron et al., we compute the absolute flow endpoint error, measures for frame interpolation error, improved statistics, and results at motion discontinuities and in textureless regions. In October 2007, we published the performance of several well-known methods on a preliminary version of our data to establish the current state of the art. We also made the data freely available on the web at                   http://vision.middlebury.edu/flow/                                  . Subsequently a number of researchers have uploaded their results to our website and published papers using the data. A significant improvement in performance has already been achieved. In this paper we analyze the results obtained to date and draw a large number of conclusions from them.},
  journal  = {International Journal of Computer Vision},
}

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@Article{combettes2007douglas,
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  year      = {2007},
  volume    = {1},
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  pages     = {564--574},
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@Article{levitin1966constrained,
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  volume    = {25},
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@Article{chen1997convergence,
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@Book{mercier1979topics,
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@Article{lions1979splitting,
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  publisher = {SIAM},
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@Article{passty1979ergodic,
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@Article{combettes2004solving,
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  year      = {1992},
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  pages     = {293--318},
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  year      = {1999},
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@InCollection{arrow2014reduction,
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  year      = {2014},
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@Article{esser2010general,
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  title         = {A general framework for a class of first order primal-dual algorithms for convex optimization in imaging science},
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  year          = {2010},
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  keywords      = {primal-dual},
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@Article{andersen2000efficient,
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  year          = {2000},
  volume        = {22},
  number        = {1},
  pages         = {243--262},
  __markedentry = {[gerald:6]},
  keywords      = {primal-dual},
  publisher     = {SIAM},
}

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