@article {12440,
	title = {Example-Driven Manifold Priors for Image Deconvolution},
	journal = {Image Processing, IEEE Transactions on},
	volume = {20},
	year = {2011},
	month = {2011/11//},
	pages = {3086 - 3096},
	abstract = {Image restoration methods that exploit prior information about images to be estimated have been extensively studied, typically using the Bayesian framework. In this paper, we consider the role of prior knowledge of the object class in the form of a patch manifold to address the deconvolution problem. Specifically, we incorporate unlabeled image data of the object class, say natural images, in the form of a patch-manifold prior for the object class. The manifold prior is implicitly estimated from the given unlabeled data. We show how the patch-manifold prior effectively exploits the available sample class data for regularizing the deblurring problem. Furthermore, we derive a generalized cross-validation (GCV) function to automatically determine the regularization parameter at each iteration without explicitly knowing the noise variance. Extensive experiments show that this method performs better than many competitive image deconvolution methods.},
	keywords = {Bayesian, cross-validation, data;Bayes, deconvolution;image, determination;deblurring, estimation;, framework;GCV, function;automatic, function;image, image, image;patch-manifold, manifold, method;iteration, method;natural, methods;deconvolution;image, methods;natural, parameter, prior;generalized, prior;unlabeled, problem, regularization, regularization;example-driven, restoration, restoration;iterative, scenes;parameter},
	isbn = {1057-7149},
	doi = {10.1109/TIP.2011.2145386},
	author = {Ni,Jie and Turaga,P. and Patel, Vishal M. and Chellapa, Rama}
}
@article {15383,
	title = {Electromagnetic Modeling of Ethernet Transformers},
	journal = {Magnetics, IEEE Transactions on},
	volume = {46},
	year = {2010},
	month = {2010/02//},
	pages = {563 - 569},
	abstract = {The unique features of Ethernet transformers are reviewed, and novel testing techniques for identification of lumped parameters of equivalent circuits are presented. To account for the distributed nature of the cross-winding and intrawinding capacitances, novel distributed models for electromagnetic analysis of Ethernet transformers are presented.},
	keywords = {analysis;electromagnetic, area, capacitances;lumped, circuits;ethernet, circuits;local, cross-winding;distributed, modeling;equivalent, models;electromagnetic, networks;lumped, networks;transformer, parameter, parameters;novel, techniques;capacitance;equivalent, Testing, transformers;intrawinding, windings;transformers;},
	isbn = {0018-9464},
	doi = {10.1109/TMAG.2009.2033203},
	author = {Bowen,D. and Mayergoyz, Issak D and Krafft,C.}
}
@conference {18155,
	title = {A pattern classification framework for theoretical analysis of component forensics},
	booktitle = {Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on},
	year = {2008},
	month = {2008/04/31/4},
	pages = {1665 - 1668},
	abstract = {Component forensics is an emerging methodology for forensic analysis that aims at estimating the algorithms and parameters in each component of a digital device. This paper proposes a theoretical foundation to examine the performance limits of component forensics. Using ideas from pattern classification theory, we define formal notions of identifiability of components in the information processing chain. We show that the parameters of certain device components can be accurately identified only in controlled settings through semi non-intrusive forensics, while the parameters of some others can be computed directly from the available sample data via complete non-intrusive analysis. We then extend the proposed theoretical framework to quantify and improve the accuracies and confidence in component parameter identification for several forensic applications.},
	keywords = {analysis;information, classification;, classification;parameter, component, estimation;pattern, forensics;component, forensics;pattern, identification;forensic, parameter, processing;nonintrusive},
	doi = {10.1109/ICASSP.2008.4517947},
	author = {Swaminathan,A. and M. Wu and Liu,K. J.R}
}
@conference {12541,
	title = {A Scalable Projective Bundle Adjustment Algorithm using the L infinity Norm},
	booktitle = {Computer Vision, Graphics Image Processing, 2008. ICVGIP {\textquoteright}08. Sixth Indian Conference on},
	year = {2008},
	month = {2008/12//},
	pages = {79 - 86},
	abstract = {The traditional bundle adjustment algorithm for structure from motion problem has a computational complexity of O((m+n)<sup>3</sup>) per iteration and memory requirement of O(mn(m+n)), where m is the number of cameras and n is the number of structure points. The sparse version of bundle adjustment has a computational complexity of O(m<sup>3</sup>+mn) per iteration and memory requirement of O(mn). Here we propose an algorithm that has a computational complexity of O(mn(radicm+radicn)) per iteration and memory requirement of O(max(m,n)). The proposed algorithm is based on minimizing the L<sub>infin</sub> norm of reprojection error. It alternately estimates the camera and structure parameters, thus reducing the potentially large scale optimization problem to many small scale subproblems each of which is a quasi-convex optimization problem and hence can be solved globally. Experiments using synthetic and real data show that the proposed algorithm gives good performance in terms of minimizing the reprojection error and also has a good convergence rate.},
	keywords = {adjustment, algorithm;structure, bundle, complexity;convex, complexity;iteration, error;scalable, estimation;, estimation;cameras;computational, L<sub>infin</sub>, method;large, methods;parameter, norm;camera;computational, OPTIMIZATION, parameter, problem;memory, problem;projection, problem;reprojection, programming;image, projective, reconstruction;iterative, reconstruction;quasiconvex, requirement;motion, scale},
	doi = {10.1109/ICVGIP.2008.51},
	author = {Mitra, K. and Chellapa, Rama}
}
@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.}
}
@article {12719,
	title = {Accurate dense optical flow estimation using adaptive structure tensors and a parametric model},
	journal = {Image Processing, IEEE Transactions on},
	volume = {12},
	year = {2003},
	month = {2003/10//},
	pages = {1170 - 1180},
	abstract = {An accurate optical flow estimation algorithm is proposed in this paper. By combining the three-dimensional (3D) structure tensor with a parametric flow model, the optical flow estimation problem is converted to a generalized eigenvalue problem. The optical flow can be accurately estimated from the generalized eigenvectors. The confidence measure derived from the generalized eigenvalues is used to adaptively adjust the coherent motion region to further improve the accuracy. Experiments using both synthetic sequences with ground truth and real sequences illustrate our method. Comparisons with classical and recently published methods are also given to demonstrate the accuracy of our algorithm.},
	keywords = {3D, accuracy;, adaptive, and, coherent, confidence, dense, eigenfunctions;, eigenvalue, eigenvalues, eigenvectors;, estimation;, flow, generalized, ground, image, measure;, model;, MOTION, optical, parameter, parametric, problem;, real, region;, sequences;, structure, synthetic, tensor;, tensors;, three-dimensional, truth;},
	isbn = {1057-7149},
	doi = {10.1109/TIP.2003.815296},
	author = {Liu,Haiying and Chellapa, Rama and Rosenfeld, A.}
}
@conference {16894,
	title = {Depth-first k-nearest neighbor finding using the MaxNearestDist estimator},
	booktitle = {Image Analysis and Processing, 2003.Proceedings. 12th International Conference on},
	year = {2003},
	month = {2003/09//},
	pages = {486 - 491},
	abstract = {Similarity searching is an important task when trying to find patterns in applications which involve mining different types of data such as images, video, time series, text documents, DNA sequences, etc. Similarity searching often reduces to finding the k nearest neighbors to a query object. A description is given of how to use an estimate of the maximum possible distance at which a nearest neighbor can be found to prune the search process in a depth-first branch-and-bound k-nearest neighbor finding algorithm. Using the MaxNearestDist estimator (Larsen, S. and Kanal, L.N., 1986) in the depth-first k-nearest neighbor algorithm provides a middle ground between a pure depth-first and a best-first k-nearest neighbor algorithm.},
	keywords = {branch-and-bound, data, depth-first, distance;, DNA, documents;, estimation;, estimator;, finding;, images;, k-nearest, matching;, maximum, MaxNearestDist, mining;, neighbor, parameter, pattern, possible, process;, processing;, query, search, searching;, sequences;, series;, similarity, text, TIME, tree, video;},
	doi = {10.1109/ICIAP.2003.1234097},
	author = {Samet, Hanan}
}
@conference {13243,
	title = {Human body pose estimation using silhouette shape analysis},
	booktitle = {Proceedings. IEEE Conference on Advanced Video and Signal Based Surveillance, 2003.},
	year = {2003},
	month = {2003/07//},
	pages = {263 - 270},
	abstract = {We describe a system for human body pose estimation from multiple views that is fast and completely automatic. The algorithm works in the presence of multiple people by decoupling the problems of pose estimation of different people. The pose is estimated based on a likelihood function that integrates information from multiple views and thus obtains a globally optimal solution. Other characteristics that make our method more general than previous work include: (1) no manual initialization; (2) no specification of the dimensions of the 3D structure; (3) no reliance on some learned poses or patterns of activity; (4) insensitivity to edges and clutter in the background and within the foreground. The algorithm has applications in surveillance and promising results have been obtained.},
	keywords = {3D, analysis;, body, classification;, clutter;, detection;, estimation;, extraction;, feature, function;, human, image, likelihood, multiple, object, parameter, parameters;, Pixel, pose, probability;, segmentation;, segmentations;, SHAPE, silhouette, structure;, surveillance;, views;},
	doi = {10.1109/AVSS.2003.1217930},
	author = {Mittal,A. and Liang Zhao and Davis, Larry S.}
}
@conference {12758,
	title = {Bayesian structure from motion using inertial information},
	booktitle = {Image Processing. 2002. Proceedings. 2002 International Conference on},
	volume = {3},
	year = {2002},
	month = {2002///},
	pages = {III-425 - III-428 vol.3 - III-425 - III-428 vol.3},
	abstract = {A novel approach to Bayesian structure from motion (SfM) using inertial information and sequential importance sampling (SIS) is presented. The inertial information is obtained from camera-mounted inertial sensors and is used in the Bayesian SfM approach as prior knowledge of the camera motion in the sampling algorithm. Experimental results using both synthetic and real images show that, when inertial information is used, more accurate results can be obtained or the same estimation accuracy can be obtained at a lower cost.},
	keywords = {3D, analysis;, Bayes, Bayesian, camera, estimation;, image, images;, importance, inertial, information;, methods;, MOTION, motion;, parameter, processing;, real, reconstruction;, sampling;, scene, sensors;, sequence;, sequences;, sequential, signal, structure-from-motion;, synthetic, systems;, video},
	doi = {10.1109/ICIP.2002.1038996},
	author = {Qian,Gang and Chellapa, Rama and Qinfen Zheng}
}
@conference {12746,
	title = {A hierarchical approach for obtaining structure from two-frame optical flow},
	booktitle = {Motion and Video Computing, 2002. Proceedings. Workshop on},
	year = {2002},
	month = {2002/12//},
	pages = {214 - 219},
	abstract = {A hierarchical iterative algorithm is proposed for extracting structure from two-frame optical flow. The algorithm exploits two facts: one is that in many applications, such as face and gesture recognition, the depth variation of the visible surface of an object in a scene is small compared to the distance between the optical center and the object; the other is that the time aliasing problem is alleviated at the coarse level for any two-frame optical flow estimate so that the estimate tends to be more accurate. A hierarchical representation for the relationship between the optical flow, depth, and the motion parameters is derived, and the resulting non-linear system is iteratively solved through two linear subsystems. At the coarsest level, the surface of the object tends to be flat, so that the inverse depth tends to be a constant, which is used as the initial depth map. Inverse depth and motion parameters are estimated by the two linear subsystems at each level and the results are propagated to finer levels. Error analysis and experiments using both computer-rendered images and real images demonstrate the correctness and effectiveness of our algorithm.},
	keywords = {algorithm;, aliasing;, analysis;, computer-rendered, depth, depth;, error, estimation;, extraction;, Face, feature, flow;, gesture, hierarchical, image, images;, inverse, iterative, methods;, MOTION, nonlinear, optical, parameter, processing;, real, recognition;, sequences;, signal, structure-from-motion;, system;, systems;, TIME, two-frame, variation;, video},
	doi = {10.1109/MOTION.2002.1182239},
	author = {Liu,Haiying and Chellapa, Rama and Rosenfeld, A.}
}
@conference {13616,
	title = {On musical score recognition using probabilistic reasoning},
	booktitle = {Document Analysis and Recognition, 1999. ICDAR {\textquoteright}99. Proceedings of the Fifth International Conference on},
	year = {1999},
	month = {1999/09//},
	pages = {115 - 118},
	abstract = {We present a probabilistic framework for document analysis and recognition and illustrate it on the problem of musical score recognition. Our system uses an explicit descriptive model of the document class to find the most likely interpretation of a scanned document image. In contrast to the traditional pipeline architecture, we carry out all stages of the analysis with a single inference engine, allowing for an end-to-end propagation of the uncertainty. The global modeling structure is similar to a stochastic attribute grammar, and local parameters are estimated using hidden Markov models},
	keywords = {analysis;document, attribute, class;document, descriptive, document, engine;local, estimation;musical, grammar;attribute, grammars;character, handling;, image, interpretation;stochastic, Markov, mechanisms;music;uncertainty, model;global, modeling, models;inference, parameter, processing;image, propagation;explicit, reasoning;scanned, recognition;document, recognition;end-to-end, recognition;inference, recognition;probabilistic, score, structure;hidden, Uncertainty},
	doi = {10.1109/ICDAR.1999.791738},
	author = {Stuckelberg,M.V. and David Doermann}
}
@article {15022,
	title = {Scalable data parallel algorithms for texture synthesis using Gibbs random fields},
	journal = {Image Processing, IEEE Transactions on},
	volume = {4},
	year = {1995},
	month = {1995/10//},
	pages = {1456 - 1460},
	abstract = {This article introduces scalable data parallel algorithms for image processing. Focusing on Gibbs and Markov random field model representation for textures, we present parallel algorithms for texture synthesis, compression, and maximum likelihood parameter estimation, currently implemented on Thinking Machines CM-2 and CM-5. The use of fine-grained, data parallel processing techniques yields real-time algorithms for texture synthesis and compression that are substantially faster than the previously known sequential implementations. Although current implementations are on Connection Machines, the methodology presented enables machine-independent scalable algorithms for a number of problems in image processing and analysis},
	keywords = {algorithms;maximum, algorithms;parallel, algorithms;scalable, algorithms;texture, analysis;image, CM-2;Thinking, CM-5;fine-grained, compression;image, compression;texture, Connection, data, estimation;model, estimation;parallel, field;Thinking, fields;Markov, likelihood, machine, Machines;Gibbs, machines;random, Parallel, parameter, processes;, processes;data, processing;image, processing;machine-independent, random, representation;real-time, scalable, synthesis;Markov, texture;maximum},
	isbn = {1057-7149},
	doi = {10.1109/83.465111},
	author = {Bader, D.A. and JaJa, Joseph F. and Chellapa, Rama}
}
