@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 {12453,
	title = {Recent advances in age and height estimation from still images and video},
	booktitle = {2011 IEEE International Conference on Automatic Face \& Gesture Recognition and Workshops (FG 2011)},
	year = {2011},
	month = {2011/03/21/25},
	pages = {91 - 96},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Soft-biometrics such as gender, age, race, etc have been found to be useful characterizations that enable fast pre-filtering and organization of data for biometric applications. In this paper, we focus on two useful soft-biometrics - age and height. We discuss their utility and the factors involved in their estimation from images and videos. In this context, we highlight the role that geometric constraints such as multiview-geometry, and shape-space geometry play. Then, we present methods based on these geometric constraints for age and height-estimation. These methods provide a principled means by fusing image-formation models, multi-view geometric constraints, and robust statistical methods for inference.},
	keywords = {age estimation, biometrics (access control), Calibration, Estimation, Geometry, height estimation, HUMANS, image fusion, image-formation model fusion, Legged locomotion, multiview-geometry, Robustness, SHAPE, shape-space geometry, soft-biometrics, statistical analysis, statistical methods, video signal processing},
	isbn = {978-1-4244-9140-7},
	doi = {10.1109/FG.2011.5771367},
	author = {Chellapa, Rama and Turaga,P.}
}
@conference {12445,
	title = {Variable remapping of images from very different sources},
	booktitle = {2011 18th IEEE International Conference on Image Processing (ICIP)},
	year = {2011},
	month = {2011/09/11/14},
	pages = {1501 - 1504},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We present a system which registers image sequences acquired by very different sources, so that multiple views could be transformed to the same coordinates system. This enables the functionality of automatic object identification and confirmation across views and platforms. The capability of the system comes from three ingredients: 1) image context enlargement through temporal integration; 2) robust motion estimation using the G-RANSAC framework with a relaxed correspondence criteria; 3) constrained motion estimation within the G-RANSAC framework. The proposed system has worked successfully on thousands of frames from multiple collections with significant variations in scale and resolution.},
	keywords = {automatic object identification, Buildings, CAMERAS, Conferences, constrained motion estimation, coordinates system, Estimation, G-RANSAC framework, image context enlargement, Image Enhancement, image registration, image sequence registration, Image sequences, Motion estimation, Robustness, temporal integration, variable image remapping},
	isbn = {978-1-4577-1304-0},
	doi = {10.1109/ICIP.2011.6115729},
	author = {Wei Zhang and Yanlin Guo and Meth, R. and Sokoloff, H. and Pope, A. and Strat, T. and Chellapa, Rama}
}
@conference {12492,
	title = {Robust regression using sparse learning for high dimensional parameter estimation problems},
	booktitle = {Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on},
	year = {2010},
	month = {2010/03//},
	pages = {3846 - 3849},
	abstract = {Algorithms such as Least Median of Squares (LMedS) and Random Sample Consensus (RANSAC) have been very successful for low-dimensional robust regression problems. However, the combinatorial nature of these algorithms makes them practically unusable for high-dimensional applications. In this paper, we introduce algorithms that have cubic time complexity in the dimension of the problem, which make them computationally efficient for high-dimensional problems. We formulate the robust regression problem by projecting the dependent variable onto the null space of the independent variables which receives significant contributions only from the outliers. We then identify the outliers using sparse representation/learning based algorithms. Under certain conditions, that follow from the theory of sparse representation, these polynomial algorithms can accurately solve the robust regression problem which is, in general, a combinatorial problem. We present experimental results that demonstrate the efficacy of the proposed algorithms. We also analyze the intrinsic parameter space of robust regression and identify an efficient and accurate class of algorithms for different operating conditions. An application to facial age estimation is presented.},
	keywords = {algorithm;random, analysis;, combinatorial, complexity;least, complexity;parameter, consensus;robust, Estimation, estimation;polynomials;regression, learning;sparse, median, of, problem;cubic, problem;polynomial, problem;sparse, regression, representation;computational, sample, squares;parameter, TIME},
	doi = {10.1109/ICASSP.2010.5495830},
	author = {Mitra, K. and Veeraraghavan,A. and Chellapa, Rama}
}
@conference {12516,
	title = {Locally time-invariant models of human activities using trajectories on the grassmannian},
	booktitle = {Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on},
	year = {2009},
	month = {2009/06//},
	pages = {2435 - 2441},
	abstract = {Human activity analysis is an important problem in computer vision with applications in surveillance and summarization and indexing of consumer content. Complex human activities are characterized by non-linear dynamics that make learning, inference and recognition hard. In this paper, we consider the problem of modeling and recognizing complex activities which exhibit time-varying dynamics. To this end, we describe activities as outputs of linear dynamic systems (LDS) whose parameters vary with time, or a time-varying linear dynamic system (TV-LDS). We discuss parameter estimation methods for this class of models by assuming that the parameters are locally time-invariant. Then, we represent the space of LDS models as a Grassmann manifold. Then, the TV-LDS model is defined as a trajectory on the Grassmann manifold. We show how trajectories on the Grassmannian can be characterized using appropriate distance metrics and statistical methods that reflect the underlying geometry of the manifold. This results in more expressive and powerful models for complex human activities. We demonstrate the strength of the framework for activity-based summarization of long videos and recognition of complex human actions on two datasets.},
	keywords = {action, activity, analysis;local, analysis;video, content, dynamic, Estimation, estimation;statistical, Grassmann, indexing;distance, linear, manifold;activity-based, method;statistical, method;surveillance;time-varying, metrics;human, model;parameter, recognition;human, recognition;indexing;parameter, summarization;computer, surveillance;, system;computer, time-invariant, vision;consumer, vision;image},
	doi = {10.1109/CVPR.2009.5206710},
	author = {Turaga,P. and Chellapa, Rama}
}
@conference {12547,
	title = {Compressed sensing for multi-view tracking and 3-D voxel reconstruction},
	booktitle = {Image Processing, 2008. ICIP 2008. 15th IEEE International Conference on},
	year = {2008},
	month = {2008/10//},
	pages = {221 - 224},
	abstract = {Compressed sensing (CS) suggests that a signal, sparse in some basis, can be recovered from a small number of random projections. In this paper, we apply the CS theory on sparse background-subtracted silhouettes and show the usefulness of such an approach in various multi-view estimation problems. The sparsity of the silhouette images corresponds to sparsity of object parameters (location, volume etc.) in the scene. We use random projections (compressed measurements) of the silhouette images for directly recovering object parameters in the scene coordinates. To keep the computational requirements of this recovery procedure reasonable, we tessellate the scene into a bunch of non-overlapping lines and perform estimation on each of these lines. Our method is scalable in the number of cameras and utilizes very few measurements for transmission among cameras. We illustrate the usefulness of our approach for multi-view tracking and 3-D voxel reconstruction problems.},
	keywords = {3D, background-subtracted, coding;, Estimation, image, problems;multi-view, projections;silhouette, reconstruction;CS, reconstruction;video, sensing;multi-view, silhouettes;image, sparsity;sparse, theory;compressed, tracking;random, voxel},
	doi = {10.1109/ICIP.2008.4711731},
	author = {Reddy, D. and Sankaranarayanan,A. C and Cevher, V. and Chellapa, Rama}
}
@conference {12555,
	title = {Compressive wireless arrays for bearing estimation},
	booktitle = {Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on},
	year = {2008},
	month = {2008/04/31/4},
	pages = {2497 - 2500},
	abstract = {Joint processing of sensor array outputs improves the performance of parameter estimation and hypothesis testing problems beyond the sum of the individual sensor processing results. When the sensors have high data sampling rates, arrays are tethered, creating a disadvantage for their deployment and also limiting their aperture size. In this paper, we develop the signal processing algorithms for randomly deployable wireless sensor arrays that are severely constrained in communication bandwidth. We focus on the acoustic bearing estimation problem and show that when the target bearings are modeled as a sparse vector in the angle space, low dimensional random projections of the microphone signals can be used to determine multiple source bearings by solving an <i>l</i> <sub>1</sub>-norm minimization problem. Field data results are shown where only 10 bits of information is passed from each microphone to estimate multiple target bearings.},
	keywords = {acoustic, algorithms;sparse, arrays;acoustic, arrays;hypothesis, bandwidth;compressive, bearing, Estimation, estimation;signal, estimation;sparse, matrices;vectors;wireless, minimization, networks;, problem;communication, problem;microphone, problems;joint, PROCESSING, processing;array, processing;direction-of-arrival, processing;l<sub>1</sub>-norm, sensor, signal, signals;parameter, Testing, vector;wireless, wireless},
	doi = {10.1109/ICASSP.2008.4518155},
	author = {Cevher, V. and Gurbuz, A.C. and McClellan, J.H. and Chellapa, Rama}
}
@conference {18150,
	title = {A Component Estimation Framework for Information Forensics},
	booktitle = {Multimedia Signal Processing, 2007. MMSP 2007. IEEE 9th Workshop on},
	year = {2007},
	month = {2007/10//},
	pages = {397 - 400},
	abstract = {With a rapid growth of imaging technologies and an increasingly widespread usage of digital images and videos for a large number of high security and forensic applications, there is a strong need for techniques to verify the source and integrity of digital data. Component forensics is new approach for forensic analysis that aims to estimate the algorithms and parameters in each component of the digital device. In this paper, we develop a novel theoretical foundation to understand the fundamental performance limits of component forensics. We define formal notions of identifiability of components in the information processing chain, and present methods to quantify the accuracies at which the component parameters can be estimated. Building upon the proposed theoretical framework, we devise methods to improve the accuracies of component parameter estimation for a wide range of forensic applications.},
	keywords = {component, data, data;, Estimation, estimation;image, forensics;parameter, framework;digital, integrity;imaging, of, processing;security, technologies;information},
	doi = {10.1109/MMSP.2007.4412900},
	author = {Swaminathan,A. and M. Wu and Liu,K. J.R}
}
@conference {12576,
	title = {Fast Bilinear SfM with Side Information},
	booktitle = {Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on},
	year = {2007},
	month = {2007/10//},
	pages = {1 - 8},
	abstract = {We study the beneficial effect of side information on the Structure from Motion (SfM) estimation problem. The side information that we consider is measurement of a {\textquoteright}reference vector{\textquoteright} and distance from fixed plane perpendicular to that reference vector. Firstly, we show that in the presence of this information, the SfM equations can be rewritten similar to a bilinear form in its unknowns. Secondly, we describe a fast iterative estimation procedure to recover the structure of both stationary scenes and moving objects that capitalizes on this information. We also provide a refinement procedure in order to tackle incomplete or noisy side information. We characterize the algorithm with respect to its reconstruction accuracy, memory requirements and stability. Finally, we describe two classes of commonly occurring real-world scenarios in which this algorithm will be effective: (a) presence of a dominant ground plane in the scene and (b) presence of an inertial measurement unit on board. Experiments using both real data and rigorous simulations show the efficacy of the algorithm.},
	keywords = {bilinear, Estimation, estimation;, estimation;reference, information;image, methods;motion, procedure;motion, procedure;side, reconstruction;iterative, recovery;image, structure, vector;refinement},
	doi = {10.1109/ICCV.2007.4408874},
	author = {Ramachandran, M. and Veeraraghavan,A. and Chellapa, Rama}
}
@conference {13138,
	title = {An Interactive Approach to Pose-Assisted and Appearance-based Segmentation of Humans},
	booktitle = {Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on},
	year = {2007},
	month = {2007/10//},
	pages = {1 - 8},
	abstract = {An interactive human segmentation approach is described. Given regions of interest provided by users, the approach iteratively estimates segmentation via a generalized EM algorithm. Specifically, it encodes both spatial and color information in a nonparametric kernel density estimator, and incorporates local MRF constraints and global pose inferences to propagate beliefs over image space iteratively to determine a coherent segmentation. This ensures the segmented humans resemble the shapes of human poses. Additionally, a layered occlusion model and a probabilistic occlusion reasoning method are proposed to handle segmentation of multiple humans in occlusion. The approach is tested on a wide variety of images containing single or multiple occluded humans, and the segmentation performance is evaluated quantitatively.},
	keywords = {algorithm;appearance-based, algorithm;hidden, approach;layered, density, EM, Estimation, estimation;, estimator;pose-assisted, feature, human, Kernel, mechanisms;pose, method;expectation-maximisation, model;nonparametric, occlusion, reasoning, removal;image, segmentation;inference, segmentation;interactive, segmentation;probabilistic},
	doi = {10.1109/ICCV.2007.4409123},
	author = {Zhe Lin and Davis, Larry S. and David Doermann and DeMenthon,D.}
}
@conference {13216,
	title = {Iterative figure-ground discrimination},
	booktitle = {Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on},
	volume = {1},
	year = {2004},
	month = {2004/08//},
	pages = {67 - 70 Vol.1 - 67 - 70 Vol.1},
	abstract = {Figure-ground discrimination is an important problem in computer vision. Previous work usually assumes that the color distribution of the figure can be described by a low dimensional parametric model such as a mixture of Gaussians. However, such approach has difficulty selecting the number of mixture components and is sensitive to the initialization of the model parameters. In this paper, we employ non-parametric kernel estimation for color distributions of both the figure and background. We derive an iterative sampling-expectation (SE) algorithm for estimating the color, distribution and segmentation. There are several advantages of kernel-density estimation. First, it enables automatic selection of weights of different cues based on the bandwidth calculation from the image itself. Second, it does not require model parameter initialization and estimation. The experimental results on images of cluttered scenes demonstrate the effectiveness of the proposed algorithm.},
	keywords = {algorithm;, analysis;, Bandwidth, calculation;, Color, colour, Computer, density, dimensional, discrimination;, distribution;, distributions;, Estimation, estimation;, expectation, figure, Gaussian, ground, image, initialization;, iterative, Kernel, low, methods;, mixture;, model, model;, nonparametric, parameter, parametric, processes;, sampling, sampling;, segmentation, segmentation;, statistics;, theory;, vision;},
	doi = {10.1109/ICPR.2004.1334006},
	author = {Zhao, L. and Davis, Larry S.}
}
@conference {18471,
	title = {Creation of virtual auditory spaces},
	booktitle = {2002 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
	volume = {2},
	year = {2002},
	month = {2002/05//},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {High-quality virtual audio scene rendering is a must for emerging virtual/augmented reality applications and for perceptual user interfaces. We describe algorithms for creation of virtual auditory spaces using measured and non-individualized HRTFs and head tracking. Details of algorithms for HRTF interpolation, room impulse response creation, and audio scene presentation are presented. Tests show that individuals externalize well, and find our interface natural. The system runs in real time with latency of less than 30 ms on an office PC without specialized DSP.},
	keywords = {accuracy, Estimation, Finite impulse response filter, Heating, Program processors, Rendering (computer graphics), System-on-a-chip},
	isbn = {0-7803-7402-9},
	doi = {10.1109/ICASSP.2002.5745052},
	author = {Zotkin,Dmitry N and Duraiswami, Ramani and Davis, Larry S.}
}
@conference {18145,
	title = {Towards a theory of nearly constant time parallel algorithms},
	booktitle = {Foundations of Computer Science, 1991. Proceedings., 32nd Annual Symposium on},
	year = {1991},
	month = {1991/10//},
	pages = {698 - 710},
	abstract = {It is demonstrated that randomization is an extremely powerful tool for designing very fast and efficient parallel algorithms. Specifically, a running time of <e1>O</e1>(lg* <e1>n</e1>) (nearly-constant), with high probability, is achieved using <e1>n</e1>/lg* <e1>n</e1> (optimal speedup) processors for a wide range of fundamental problems. Also given is a constant time algorithm which, using <e1>n</e1> processors, approximates the sum of <e1>n</e1> positive numbers to within an error which is smaller than the sum by an order of magnitude. A variety of known and new techniques are used. New techniques, which are of independent interest, include estimation of the size of a set in constant time for several settings, and ways for deriving superfast optimal algorithms from superfast nonoptimal ones},
	keywords = {computational complexity, Estimation, nearly constant time parallel algorithms, Parallel algorithms, positive numbers, randomization, running time, superfast optimal algorithms},
	doi = {10.1109/SFCS.1991.185438},
	author = {Gil,J. and Matias,Y. and Vishkin, Uzi}
}
