@conference {14024,
	title = {Approximate expressions for the mean and the covariance of the maximum likelihood estimator for acoustic source localization},
	booktitle = {Acoustics, Speech, and Signal Processing, 2005. Proceedings. (ICASSP {\textquoteright}05). IEEE International Conference on},
	volume = {3},
	year = {2005},
	month = {2005/03//},
	pages = {iii/73 - iii/76 Vol. 3 - iii/73 - iii/76 Vol. 3},
	abstract = {Acoustic source localization using multiple microphones can be formulated as a maximum likelihood estimation problem. The estimator is implicitly defined as the minimum of a certain objective function. As a result, we cannot get explicit expressions for the mean and the covariance of the estimator. We derive approximate expressions for the mean vector and covariance matrix of the estimator using Taylor{\textquoteright}s series expansion of the implicitly defined estimator. The validity of our expressions is verified by Monte-Carlo simulations. We also study the performance of the estimator for different microphone array configurations.},
	keywords = {(mathematics);, acoustic, approximate, approximation, array, array;, covariance, estimation;, expansion;, expressions;, function;, likelihood, localization;, matrices;, matrix;, maximum, mean, microphone, objective, processing;, series, signal, source, Taylor, theory;, vector;, vectors;},
	doi = {10.1109/ICASSP.2005.1415649},
	author = {Raykar,V.C. and Duraiswami, Ramani}
}
@conference {14034,
	title = {Computation of Green{\textquoteright}s function for finite-size photonic crystals by boundary element method},
	booktitle = {Antennas and Propagation Society International Symposium, 2004. IEEE},
	volume = {4},
	year = {2004},
	month = {2004/06//},
	pages = {4320 - 4323 Vol.4 - 4320 - 4323 Vol.4},
	abstract = {We have derived a new integral equation method for computing the Green{\textquoteright}s function for 2D electromagnetic wave propagation in a finite size photonic crystal. The method is also used for deriving the multipole expansions for the case of circular cylinders. This derivation is different from the ones in the literature. Our numerical results show an excellent agreement of the two algorithms. In the future we would like to expand the method to the numerical solution of the three-dimensional electromagnetic problem.},
	keywords = {2D, boundary, boundary-elements, circular, crystals;, cylinders;, electromagnetic, element, equation, equations;, expansions;, finite-size, function, function;, Green, Green{\textquoteright}s, integral, method;, methods;, multipole, photonic, propagation;, wave},
	doi = {10.1109/APS.2004.1330307},
	author = {Seydou,F. and Ramahi,O. and Duraiswami, Ramani and Seppdnen,T.}
}
@conference {18225,
	title = {Image hashing resilient to geometric and filtering operations},
	booktitle = {Multimedia Signal Processing, 2004 IEEE 6th Workshop on},
	year = {2004},
	month = {2004/10/01/sept},
	pages = {355 - 358},
	abstract = {Image hash functions provide compact representations of images, which is useful for search and authentication applications. In this work, we have identified a general three step framework and proposed a new image hashing scheme that achieves a better overall performance than the existing approaches under various kinds of image processing distortions. By exploiting the properties of discrete polar Fourier transform and incorporating cryptographic keys, the proposed image hash is resilient to geometric and filtering operations, and is secure against guessing and forgery attacks.},
	keywords = {compact, cryptographic, cryptography;, discrete, distortion;, Filtering, Fourier, function;, geometric, hash, image, key, key;, operation;, polar, PROCESSING, public, representation;, theory;, transform;, transforms;},
	doi = {10.1109/MMSP.2004.1436566},
	author = {Swaminathan,A. and Mao,Yinian and M. Wu}
}
@article {15372,
	title = {A novel approach to removing intersymbol interference from spin-stand images},
	journal = {Magnetics, IEEE Transactions on},
	volume = {40},
	year = {2004},
	month = {2004/07//},
	pages = {2197 - 2199},
	abstract = {A novel intersymbol interference (ISI) removal technique based on the "response function" characterization of giant-magnetoresistive heads is presented. It is demonstrated that the ISI-free readback image that corresponds to the actual underlying magnetization patterns can be extracted from the ISI-distorted readback signal through deconvolution. A new image deconvolution algorithm has been implemented, and it has been shown that it effectively removes the ISI distortions.},
	keywords = {algorithm;, characterization;, Deconvolution, function, function;, giant, giant-magnetoresistive, Head, heads;, image, image;, imaging;, Interference, interference;, intersymbol, ISI-distorted, ISI-free, magnetic, magnetization, magnetoresistance;, patterns;, readback, removal;, response, signal;, spin-stand, suppression;},
	isbn = {0018-9464},
	doi = {10.1109/TMAG.2004.830153},
	author = {Mayergoyz, Issak D and Tse,C. and Krafft,C. and McAvoy,P.}
}
@article {17798,
	title = {Optimal models of disjunctive logic programs: semantics, complexity, and computation},
	journal = {Knowledge and Data Engineering, IEEE Transactions on},
	volume = {16},
	year = {2004},
	month = {2004/04//},
	pages = {487 - 503},
	abstract = {Almost all semantics for logic programs with negation identify a set, SEM(P), of models of program P, as the intended semantics of P, and any model M in this class is considered a possible meaning of P with regard to the semantics the user has in mind. Thus, for example, in the case of stable models [M. Gelfond et al., (1988)], choice models [D. Sacca et al., (1990)], answer sets [M. Gelfond et al., (1991)], etc., different possible models correspond to different ways of "completing" the incomplete information in the logic program. However, different end-users may have different ideas on which of these different models in SEM(P) is a reasonable one from their point of view. For instance, given SEM(P), user U<sub>1</sub> may prefer model M<sub>1</sub> isin;SEM(P) to model M<sub>2</sub> isin;SEM(P) based on some evaluation criterion that she has. We develop a logic program semantics based on optimal models. This semantics does not add yet another semantics to the logic programming arena - it takes as input an existing semantics SEM(P) and a user-specified objective function Obj, and yields a new semantics Opt(P)_ sube; SEM(P) that realizes the objective function within the framework of preferred models identified already by SEM(P). Thus, the user who may or may not know anything about logic programming has considerable flexibility in making the system reflect her own objectives by building "on top" of existing semantics known to the system. In addition to the declarative semantics, we provide a complete complexity analysis and algorithms to compute optimal models under varied conditions when SEM(P) is the stable model semantics, the minimal models semantics, and the all-models semantics.},
	keywords = {complexity;, computational, disjunctive, function;, knowledge, LANGUAGE, Logic, minimal, model, nonmonotonic, objective, optimisation;, OPTIMIZATION, problems;, program, Programming, programming;, reasoning;, representation;, semantics;, stable, user-specified},
	isbn = {1041-4347},
	doi = {10.1109/TKDE.2004.1269672},
	author = {Leone,N. and Scarcello,F. and V.S. Subrahmanian}
}
@conference {12686,
	title = {Robust Bayesian cameras motion estimation using random sampling},
	booktitle = {Image Processing, 2004. ICIP {\textquoteright}04. 2004 International Conference on},
	volume = {2},
	year = {2004},
	month = {2004/10//},
	pages = {1361 - 1364 Vol.2 - 1361 - 1364 Vol.2},
	abstract = {In this paper, we propose an algorithm for robust 3D motion estimation of wide baseline cameras from noisy feature correspondences. The posterior probability density function of the camera motion parameters is represented by weighted samples. The algorithm employs a hierarchy coarse-to-fine strategy. First, a coarse prior distribution of camera motion parameters is estimated using the random sample consensus scheme (RANSAC). Based on this estimate, a refined posterior distribution of camera motion parameters can then be obtained through importance sampling. Experimental results using both synthetic and real image sequences indicate the efficacy of the proposed algorithm.},
	keywords = {3D, baseline, Bayesian, CAMERAS, cameras;, coarse-to-fine, consensus, density, estimation;, feature, function;, hierarchy, image, images;, importance, matching;, MOTION, posterior, probability, probability;, processing;, random, RANSAC;, real, realistic, sample, sampling;, scheme;, sequences;, stereo, strategy;, synthetic, wide},
	doi = {10.1109/ICIP.2004.1419754},
	author = {Qian, G. and Chellapa, Rama and Qinfen Zheng}
}
@article {13235,
	title = {Efficient kernel density estimation using the fast gauss transform with applications to color modeling and tracking},
	journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
	volume = {25},
	year = {2003},
	month = {2003/11//},
	pages = {1499 - 1504},
	abstract = {Many vision algorithms depend on the estimation of a probability density function from observations. Kernel density estimation techniques are quite general and powerful methods for this problem, but have a significant disadvantage in that they are computationally intensive. In this paper, we explore the use of kernel density estimation with the fast Gauss transform (FGT) for problems in vision. The FGT allows the summation of a mixture of ill Gaussians at N evaluation points in O(M+N) time, as opposed to O(MN) time for a naive evaluation and can be used to considerably speed up kernel density estimation. We present applications of the technique to problems from image segmentation and tracking and show that the algorithm allows application of advanced statistical techniques to solve practical vision problems in real-time with today{\textquoteright}s computers.},
	keywords = {algorithms;, Color, Computer, density, estimation;, fast, function;, Gauss, image, Kernel, modeling;, segmentation;, tracking;, transform;, transforms;, VISION, vision;},
	isbn = {0162-8828},
	doi = {10.1109/TPAMI.2003.1240123},
	author = {Elgammal,A. and Duraiswami, Ramani and Davis, Larry S.}
}
@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 {12765,
	title = {3D face reconstruction from video using a generic model},
	booktitle = {Multimedia and Expo, 2002. ICME {\textquoteright}02. Proceedings. 2002 IEEE International Conference on},
	volume = {1},
	year = {2002},
	month = {2002///},
	pages = {449 - 452 vol.1 - 449 - 452 vol.1},
	abstract = {Reconstructing a 3D model of a human face from a video sequence is an important problem in computer vision, with applications to recognition, surveillance, multimedia etc. However, the quality of 3D reconstructions using structure from motion (SfM) algorithms is often not satisfactory. One common method of overcoming this problem is to use a generic model of a face. Existing work using this approach initializes the reconstruction algorithm with this generic model. The problem with this approach is that the algorithm can converge to a solution very close to this initial value, resulting in a reconstruction which resembles the generic model rather than the particular face in the video which needs to be modeled. We propose a method of 3D reconstruction of a human face from video in which the 3D reconstruction algorithm and the generic model are handled separately. A 3D estimate is obtained purely from the video sequence using SfM algorithms without use of the generic model. The final 3D model is obtained after combining the SfM estimate and the generic model using an energy function that corrects for the errors in the estimate by comparing local regions in the two models. The optimization is done using a Markov chain Monte Carlo (MCMC) sampling strategy. The main advantage of our algorithm over others is that it is able to retain the specific features of the face in the video sequence even when these features are different from those of the generic model. The evolution of the 3D model through the various stages of the algorithm is presented.},
	keywords = {3D, algorithm;, algorithms;, analysis;, Carlo, chain, Computer, Face, from, function;, generic, human, image, Markov, MCMC, methods;, model;, Monte, MOTION, optimisation;, OPTIMIZATION, processes;, processing;, recognition;, reconstruction, reconstruction;, sampling;, sequence;, sequences;, SfM, signal, structure, surveillance;, video, vision;},
	doi = {10.1109/ICME.2002.1035815},
	author = {Chowdhury, A.R. and Chellapa, Rama and Krishnamurthy, S. and Vo, T.}
}
@article {12748,
	title = {Optimal edge-based shape detection},
	journal = {Image Processing, IEEE Transactions on},
	volume = {11},
	year = {2002},
	month = {2002/11//},
	pages = {1209 - 1227},
	abstract = {We propose an approach to accurately detecting two-dimensional (2-D) shapes. The cross section of the shape boundary is modeled as a step function. We first derive a one-dimensional (1-D) optimal step edge operator, which minimizes both the noise power and the mean squared error between the input and the filter output. This operator is found to be the derivative of the double exponential (DODE) function, originally derived by Ben-Arie and Rao (1994). We define an operator for shape detection by extending the DODE filter along the shape{\textquoteright}s boundary contour. The responses are accumulated at the centroid of the operator to estimate the likelihood of the presence of the given shape. This method of detecting a shape is in fact a natural extension of the task of edge detection at the pixel level to the problem of global contour detection. This simple filtering scheme also provides a tool for a systematic analysis of edge-based shape detection. We investigate how the error is propagated by the shape geometry. We have found that, under general assumptions, the operator is locally linear at the peak of the response. We compute the expected shape of the response and derive some of its statistical properties. This enables us to predict both its localization and detection performance and adjust its parameters according to imaging conditions and given performance specifications. Applications to the problem of vehicle detection in aerial images, human facial feature detection, and contour tracking in video are presented.},
	keywords = {1D, 2D, aerial, analysis;, boundary, conditions;, contour, cross, detection;, DODE, double, edge, edge-based, error, error;, exponential, extraction;, facial, feature, filter, filter;, Filtering, function;, geometry;, global, human, images;, imaging, localization, mean, methods;, NOISE, operator;, optimal, optimisation;, output;, performance;, pixel;, power;, propagation;, properties;, section;, SHAPE, square, squared, statistical, step, theory;, tracking;, two-dimensional, vehicle, video;},
	isbn = {1057-7149},
	doi = {10.1109/TIP.2002.800896},
	author = {Moon, H. and Chellapa, Rama and Rosenfeld, A.}
}
@conference {15207,
	title = {Efficient minimum cost matching using quadrangle inequality},
	booktitle = {Foundations of Computer Science, 1992. Proceedings., 33rd Annual Symposium on},
	year = {1992},
	month = {1992/10//},
	pages = {583 - 592},
	abstract = {The authors present efficient algorithms for finding a minimum cost perfect matching, and for solving the transportation problem in bipartite graphs, G = (Red cup; Blue, Red times; Blue), where |Red| = n, |Blue| = m, n les; m, and the cost function obeys the quadrangle inequality. The first results assume that all the red points and all the blue points lie on a curve that is homeomorphic to either a line or a circle and the cost function is given by the Euclidean distance along the curve. They present a linear time algorithm for the matching problem. They generalize the method to solve the corresponding transportation problem in O((m+n)log(m+n)) time. The next result is an O(n log m) algorithm for minimum cost matching when the cost array is a bitonic Monge array. An example of this is when the red points lie on one straight line and the blue points lie on another straight line (that is not necessarily parallel to the first one). Finally, they provide a weakly polynomial algorithm for the transportation problem in which the associated cost array is a bitonic Monge array},
	keywords = {algorithm;, array;, bipartite, bitonic, blue, complexity;, computational, cost, distance;, Euclidean, function;, geometry;, graph, graphs;, inequality;, linear, MATCHING, matching;, minimisation;, minimum, Monge, perfect, points;, polynomial, problem;, quadrangle, red, theory;, TIME, transportation, transportation;, weakly},
	doi = {10.1109/SFCS.1992.267793},
	author = {Aggarwal,A. and Bar-Noy,A. and Khuller, Samir and Kravets,D. and Schieber,B.}
}
