@conference {13071,
	title = {Action recognition using Partial Least Squares and Support Vector Machines},
	booktitle = {Image Processing (ICIP), 2011 18th IEEE International Conference on},
	year = {2011},
	month = {2011/09//},
	pages = {533 - 536},
	abstract = {We introduce an action recognition approach based on Partial Least Squares (PLS) and Support Vector Machines (SVM). We extract very high dimensional feature vectors representing spatio-temporal properties of actions and use multiple PLS regressors to find relevant features that distinguish amongst action classes. Finally, we use a multi-class SVM to learn and classify those relevant features. We applied our approach to INRIA{\textquoteright}s IXMAS dataset. Experimental results show that our method is superior to other methods applied to the IXMAS dataset.},
	keywords = {analysis;support, approach;multiclass, approximations;regression, dataset;action, dimensional, extraction;feature, extraction;image, feature, high, INRIA, IXMAS, least, machines;, machines;very, partial, properties;support, recognition, recognition;least, regressors;spatiotemporal, squares, SVM;multiple, vector, vectors},
	doi = {10.1109/ICIP.2011.6116399},
	author = {Ramadan,S. and Davis, Larry S.}
}
@conference {12435,
	title = {Blurring-invariant Riemannian metrics for comparing signals and images},
	booktitle = {2011 IEEE International Conference on Computer Vision (ICCV)},
	year = {2011},
	month = {2011/11/06/13},
	pages = {1770 - 1775},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We propose a novel Riemannian framework for comparing signals and images in a manner that is invariant to their levels of blur. This framework uses a log-Fourier representation of signals/images in which the set of all possible Gaussian blurs of a signal, i.e. its orbits under semigroup action of Gaussian blur functions, is a straight line. Using a set of Riemannian metrics under which the group actions are by isometries, the orbits are compared via distances between orbits. We demonstrate this framework using a number of experimental results involving 1D signals and 2D images.},
	keywords = {blurring-invariant Riemannian metrics, Estimation, Fourier transforms, Gaussian blur function, Gaussian processes, image representation, log-Fourier representation, measurement, Orbits, Polynomials, signal representation, Space vehicles, vectors},
	isbn = {978-1-4577-1101-5},
	doi = {10.1109/ICCV.2011.6126442},
	author = {Zhengwu Zhang and Klassen, E. and Srivastava, A. and Turaga,P. and Chellapa, Rama}
}
@conference {12437,
	title = {Domain adaptation for object recognition: An unsupervised approach},
	booktitle = {2011 IEEE International Conference on Computer Vision (ICCV)},
	year = {2011},
	month = {2011/11/06/13},
	pages = {999 - 1006},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Adapting the classifier trained on a source domain to recognize instances from a new target domain is an important problem that is receiving recent attention. In this paper, we present one of the first studies on unsupervised domain adaptation in the context of object recognition, where we have labeled data only from the source domain (and therefore do not have correspondences between object categories across domains). Motivated by incremental learning, we create intermediate representations of data between the two domains by viewing the generative subspaces (of same dimension) created from these domains as points on the Grassmann manifold, and sampling points along the geodesic between them to obtain subspaces that provide a meaningful description of the underlying domain shift. We then obtain the projections of labeled source domain data onto these subspaces, from which a discriminative classifier is learnt to classify projected data from the target domain. We discuss extensions of our approach for semi-supervised adaptation, and for cases with multiple source and target domains, and report competitive results on standard datasets.},
	keywords = {Data models, data representations, discriminative classifier, Feature extraction, Grassmann manifold, image sampling, incremental learning, labeled source domain, Manifolds, measurement, object category, Object recognition, Principal component analysis, sampling points, semisupervised adaptation, target domain, underlying domain shift, unsupervised approach, unsupervised domain adaptation, Unsupervised learning, vectors},
	isbn = {978-1-4577-1101-5},
	doi = {10.1109/ICCV.2011.6126344},
	author = {Gopalan,R. and Ruonan Li and Chellapa, Rama}
}
@conference {12446,
	title = {Illumination robust dictionary-based face recognition},
	booktitle = {2011 18th IEEE International Conference on Image Processing (ICIP)},
	year = {2011},
	month = {2011/09/11/14},
	pages = {777 - 780},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {In this paper, we present a face recognition method based on simultaneous sparse approximations under varying illumination. Our method consists of two main stages. In the first stage, a dictionary is learned for each face class based on given training examples which minimizes the representation error with a sparseness constraint. In the second stage, a test image is projected onto the span of the atoms in each learned dictionary. The resulting residual vectors are then used for classification. Furthermore, to handle changes in lighting conditions, we use a relighting approach based on a non-stationary stochastic filter to generate multiple images of the same person with different lighting. As a result, our algorithm has the ability to recognize human faces with good accuracy even when only a single or a very few images are provided for training. The effectiveness of the proposed method is demonstrated on publicly available databases and it is shown that this method is efficient and can perform significantly better than many competitive face recognition algorithms.},
	keywords = {albedo, approximation theory, classification, competitive face recognition algorithms, Databases, Dictionaries, Face, face recognition, face recognition method, filtering theory, human face recognition, illumination robust dictionary-based face recognition, illumination variation, image representation, learned dictionary, learning (artificial intelligence), lighting, lighting conditions, multiple images, nonstationary stochastic filter, publicly available databases, relighting, relighting approach, representation error, residual vectors, Robustness, simultaneous sparse approximations, simultaneous sparse signal representation, sparseness constraint, Training, varying illumination, vectors},
	isbn = {978-1-4577-1304-0},
	doi = {10.1109/ICIP.2011.6116670},
	author = {Patel, Vishal M. and Tao Wu and Biswas,S. and Phillips,P.J. and Chellapa, Rama}
}
@conference {13383,
	title = {Maximizing Expected Utility for Stochastic Combinatorial Optimization Problems},
	booktitle = {2011 IEEE 52nd Annual Symposium on Foundations of Computer Science (FOCS)},
	year = {2011},
	month = {2011/10/22/25},
	pages = {797 - 806},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We study the stochastic versions of a broad class of combinatorial problems where the weights of the elements in the input dataset are uncertain. The class of problems that we study includes shortest paths, minimum weight spanning trees, and minimum weight matchings over probabilistic graphs, and other combinatorial problems like knapsack. We observe that the expected value is inadequate in capturing different types of risk averse or risk-prone behaviors, and instead we consider a more general objective which is to maximize the expected utility of the solution for some given utility function, rather than the expected weight (expected weight becomes a special case). We show that we can obtain a polynomial time approximation algorithm with additive error ϵ for any ϵ >; 0, if there is a pseudopolynomial time algorithm for the exact version of the problem (This is true for the problems mentioned above) and the maximum value of the utility function is bounded by a constant. Our result generalizes several prior results on stochastic shortest path, stochastic spanning tree, and stochastic knapsack. Our algorithm for utility maximization makes use of the separability of exponential utility and a technique to decompose a general utility function into exponential utility functions, which may be useful in other stochastic optimization problems.},
	keywords = {Approximation algorithms, Approximation methods, combinatorial problems, Fourier series, knapsack problems, optimisation, OPTIMIZATION, polynomial approximation, polynomial time approximation algorithm, Polynomials, Random variables, stochastic combinatorial optimization, stochastic knapsack, stochastic shortest path, stochastic spanning tree, vectors},
	isbn = {978-1-4577-1843-4},
	doi = {10.1109/FOCS.2011.33},
	author = {Li,Jian and Deshpande, Amol}
}
@conference {19649,
	title = {A Rao-Blackwellized particle filter for EigenTracking},
	booktitle = {Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004},
	volume = {2},
	year = {2004},
	month = {2004/06//},
	pages = {II - 980-II-986 Vol.2},
	abstract = {Subspace representations have been a popular way to model appearance in computer vision. In Jepson and Black{\textquoteright}s influential paper on EigenTracking, they were successfully applied in tracking. For noisy targets, optimization-based algorithms (including EigenTracking) often fail catastrophically after losing track. Particle filters have recently emerged as a robust method for tracking in the presence of multi-modal distributions. To use subspace representations in a particle filter, the number of samples increases exponentially as the state vector includes the subspace coefficients. We introduce an efficient method for using subspace representations in a particle filter by applying Rao-Blackwellization to integrate out the subspace coefficients in the state vector. Fewer samples are needed since part of the posterior over the state vector is analytically calculated. We use probabilistic principal component analysis to obtain analytically tractable integrals. We show experimental results in a scenario in which we track a target in clutter.},
	keywords = {analytically tractable integrals, Computer vision, EigenTracking, Filters, Gaussian processes, modal analysis, multi-modal distributions, NOISE, noisy targets, optimisation, optimization-based algorithms, Particle filters, Particle measurements, Particle tracking, Principal component analysis, probabilistic principal component analysis, Rao-Blackwellized particle filter, Robustness, SHAPE, State estimation, state vector, subspace coefficients, Subspace representations, target tracking, vectors},
	author = {Zia Khan and Balch, T. and Dellaert, F.}
}
@conference {18289,
	title = {Video transcoding by reducing spatial resolution},
	booktitle = {Image Processing, 2000. Proceedings. 2000 International Conference on},
	volume = {1},
	year = {2000},
	month = {2000///},
	pages = {972 -975 vol.1 - 972 -975 vol.1},
	abstract = {In network delivery of digital video, if the bandwidth required for a video is not available, the video has to be recoded at a reduced bit rate. It is highly desirable that the transcoding is carried out in real time while maintaining reasonable image quality. In this paper, we propose a fast approach to derive from an MPEG stream a new MPEG stream with half the spatial resolution. For the downsized video, we first generate from the original compressed video an improved estimate of the motion vectors. We then propose a compressed domain approach with data hiding to produce DCT residues by an open-loop method. The computational complexity is significantly lower than a number of previous approaches. Simulation suggests that our approach produces reasonable image quality},
	keywords = {approach;computational, coding;, coding;video, complexity;data, compression;data, cosine, DCT, delivery;open-loop, domain, encapsulation;discrete, estimation;network, estimation;transform, hiding;digital, method;spatial, quality;motion, reduction;video, residues;MPEG, resolution, resolution;motion, stream;compressed, transcoding;computational, transforms;image, vectors, video;downsized, video;image},
	doi = {10.1109/ICIP.2000.901123},
	author = {Yin,Peng and M. Wu and Liu,Bede}
}
@conference {14168,
	title = {Global rigidity constraints in image displacement fields},
	booktitle = {, Fifth International Conference on Computer Vision, 1995. Proceedings},
	year = {1995},
	month = {1995/06/20/23},
	pages = {245 - 250},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Image displacement fields-optical flow fields, stereo disparity fields, normal flow fields-due to rigid motion possess a global geometric structure which is independent of the scene in view. Motion vectors of certain lengths and directions are constrained to lie on the imaging surface at particular loci whose location and form depends solely on the 3D motion parameters. If optical flow fields or stereo disparity fields are considered, then equal vectors are shown to lie on conic sections. Similarly, for normal motion fields, equal vectors lie within regions whose boundaries also constitute conics. By studying various properties of these curves and regions and their relationships, a characterization of the structure of rigid motion fields is given. The goal of this paper is to introduce a concept underlying the global structure of image displacement fields. This concept gives rise to various constraints that could form the basis of algorithms for the recovery of visual information from multiple views},
	keywords = {3D motion parameters, algorithms, Automation, Computer science, Computer vision, conic sections, curves, equal vectors, Fluid flow measurement, global geometric structure, global rigidity constraints, image displacement fields, Image motion analysis, Image segmentation, Image sequences, imaging surface, Laboratories, Layout, Motion estimation, Motion measurement, motion vectors, multiple views, normal flow fields, optical flow fields, regions, rigid motion, stereo disparity fields, Stereo vision, vectors, visual information recovery},
	isbn = {0-8186-7042-8},
	doi = {10.1109/ICCV.1995.466779},
	author = {Ferm{\"u}ller, Cornelia and Aloimonos, J.}
}
@conference {11982,
	title = {Global rigidity constraints in image displacement fields},
	booktitle = {Proceedings of Fifth International Conference on Computer Vision, 1995},
	year = {1995},
	month = {1995/06/20/23},
	pages = {245 - 250},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Image displacement fields-optical flow fields, stereo disparity fields, normal flow fields-due to rigid motion possess a global geometric structure which is independent of the scene in view. Motion vectors of certain lengths and directions are constrained to lie on the imaging surface at particular loci whose location and form depends solely on the 3D motion parameters. If optical flow fields or stereo disparity fields are considered, then equal vectors are shown to lie on conic sections. Similarly, for normal motion fields, equal vectors lie within regions whose boundaries also constitute conics. By studying various properties of these curves and regions and their relationships, a characterization of the structure of rigid motion fields is given. The goal of this paper is to introduce a concept underlying the global structure of image displacement fields. This concept gives rise to various constraints that could form the basis of algorithms for the recovery of visual information from multiple views},
	keywords = {3D motion parameters, algorithms, Automation, Computer science, Computer vision, conic sections, curves, equal vectors, Fluid flow measurement, global geometric structure, global rigidity constraints, image displacement fields, Image motion analysis, Image segmentation, Image sequences, imaging surface, Laboratories, Layout, Motion estimation, Motion measurement, motion vectors, multiple views, normal flow fields, optical flow fields, regions, rigid motion, stereo disparity fields, Stereo vision, vectors, visual information recovery},
	isbn = {0-8186-7042-8},
	doi = {10.1109/ICCV.1995.466779},
	author = {Ferm{\"u}ller, Cornelia and Aloimonos, J.}
}
