@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 {12693,
	title = {Classification probability analysis of principal component space analysis},
	booktitle = {Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on},
	volume = {1},
	year = {2004},
	month = {2004/08//},
	pages = {240 - 243 Vol.1 - 240 - 243 Vol.1},
	abstract = {In a previous paper, we have presented a new linear classification algorithm, principal component space analysis (PCNSA) which is designed for problems like object recognition where different classes have unequal and non-white noise covariance matrices. PCNSA first obtains a principal components space (PCA space) for the entire data and in this PCA space, it finds for each class "i", an M<sub>i</sub> dimensional subspace along which the class{\textquoteright}s intra-class variance is the smallest. We call this subspace an approximate space (ANS) since the lowest variance is usually "much smaller" than the highest. A query is classified into class "i" if its distance from the class{\textquoteright}s mean in the class{\textquoteright}s ANS is a minimum. In this paper, we discuss the PCNSA algorithm more precisely and derive tight upper bounds on its classification error probability. We use these expressions to compare classification performance of PCNSA with that of subspace linear discriminant analysis (SLDA).},
	keywords = {algorithm;, analysis;, approximate, classification, classification;, component, covariance, discriminant, error, intraclass, linear, matrices;, matrix;, noise;, nonwhite, object, pattern, PCA;, principal, probability;, recognition;, space, space;, statistics;, subspace, variance;},
	doi = {10.1109/ICPR.2004.1334068},
	author = {Vaswani, N. and Chellapa, Rama}
}
@article {18437,
	title = {Susceptibility matrix: a new aid to software auditing},
	journal = {Security Privacy, IEEE},
	volume = {2},
	year = {2004},
	month = {2004/04//mar},
	pages = {16 - 21},
	abstract = {Testing for security is lengthy, complex, and costly, so focusing test efforts in areas that have the greatest number of security vulnerabilities is essential. This article describes a taxonomy-based approach that gives an insight into the distribution of vulnerabilities in a system.},
	keywords = {approach;, auditing;, data;, matrix;, of, program, Security, software, susceptibility, taxonomy-based, testing;, vulnerabilities;},
	isbn = {1540-7993},
	doi = {10.1109/MSECP.2004.1281240},
	author = {Jiwnani,K. and Zelkowitz, Marvin V}
}
@article {12699,
	title = {Wide baseline image registration with application to 3-D face modeling},
	journal = {Multimedia, IEEE Transactions on},
	volume = {6},
	year = {2004},
	month = {2004/06//},
	pages = {423 - 434},
	abstract = {Establishing correspondence between features in two images of the same scene taken from different viewing angles is a challenging problem in image processing and computer vision. However, its solution is an important step in many applications like wide baseline stereo, three-dimensional (3-D) model alignment, creation of panoramic views, etc. In this paper, we propose a technique for registration of two images of a face obtained from different viewing angles. We show that prior information about the general characteristics of a face obtained from video sequences of different faces can be used to design a robust correspondence algorithm. The method works by matching two-dimensional (2-D) shapes of the different features of the face (e.g., eyes, nose etc.). A doubly stochastic matrix, representing the probability of match between the features, is derived using the Sinkhorn normalization procedure. The final correspondence is obtained by minimizing the probability of error of a match between the entire constellation of features in the two sets, thus taking into account the global spatial configuration of the features. The method is applied for creating holistic 3-D models of a face from partial representations. Although this paper focuses primarily on faces, the algorithm can also be used for other objects with small modifications.},
	keywords = {2D, 3D, algorithm;, baseline, biometrics;, Computer, configuration;, correspondence, doubly, error, extraction;, Face, feature, holistic, image, matching;, matrix;, minimization;, modeling;, models;, normalization, probability, probability;, procedure;, processes;, processing;, recognition;, registration;, representation;, sequences;, shapes;, Sinkhorn, spatial, statistics;, Stochastic, video, vision;, wide},
	isbn = {1520-9210},
	doi = {10.1109/TMM.2004.827511},
	author = {Roy-Chowdhury, A.K. and Chellapa, Rama and Keaton, T.}
}
@conference {14050,
	title = {A simplified Newton method for the inverse orthotropic problem},
	booktitle = {Antennas and Propagation Society International Symposium, 2003. IEEE},
	volume = {1},
	year = {2003},
	month = {2003/06//},
	pages = {535 - 538 vol.1 - 535 - 538 vol.1},
	abstract = {We consider one of the most challenging inverse problems in electromagnetic scattering for inhomogeneous medium. We try to reconstruct the elements of a matrix from scattering data in an orthotropic medium. To solve the inverse problem, we implement a simplified Newton method and present some numerical results.},
	keywords = {algebra;, electromagnetic, inhomogeneous, inverse, matrix, matrix;, media;, medium;, method;, Newton, orthotropic, problem;, problems;, scattering;, wave},
	doi = {10.1109/APS.2003.1217514},
	author = {Seydou,F. and Seppanen,T. and Duraiswami, Ramani}
}
@conference {12747,
	title = {Wide baseline image registration using prior information},
	booktitle = {Multimedia Signal Processing, 2002 IEEE Workshop on},
	year = {2002},
	month = {2002/12//},
	pages = {37 - 40},
	abstract = {Establishing correspondence between features in two images of the same scene taken from different viewing angles in a challenging problem in image processing and computer vision. However, its solution is an important step in many applications like wide baseline stereo, 3D model alignment, creation of panoramic views etc. In this paper, we propose a technique for registration of two images of a face obtained from different viewing angles. We show that prior information about the general characteristics of a face obtained from video sequences of different faces can be used to design a robust correspondence algorithm. The method works by matching 2D shapes of the different features of the face. A doubly stochastic matrix, representing the probability of match between the features, is derived using the Sinkhorn normalization procedure. The final correspondence is obtained by minimizing the probability of error of a match between the entire constellations of features in the two sets, thus taking into account the global spatial configuration of the features. The method is applied for creating holistic 3D models of a face from partial representations. Although this paper focuses primarily on faces, the algorithm can also be used for other objects with small modifications.},
	keywords = {2D, 3D, algorithm;, alignment;, angles;, baseline, Computer, configuration;, constellation;, correspondence, creation;, doubly, error, extraction;, Face, feature, global, holistic, image, images;, matching;, matrix;, model, models;, normalization, panoramic, probability;, procedure;, processes;, processing;, registration;, robust, sequences;, SHAPE, signal, Sinkhorn, spatial, statistics;, stereo;, Stochastic, video, view, viewing, vision;, wide},
	doi = {10.1109/MMSP.2002.1203242},
	author = {Chowdhury, AM and Chellapa, Rama and Keaton, T.}
}
