@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 {12720,
	title = {A hidden Markov model based framework for recognition of humans from gait sequences},
	booktitle = {Image Processing, 2003. ICIP 2003. Proceedings. 2003 International Conference on},
	volume = {2},
	year = {2003},
	month = {2003/09//},
	pages = {II - 93-6 vol.3 - II - 93-6 vol.3},
	abstract = {In this paper we propose a generic framework based on hidden Markov models (HMMs) for recognition of individuals from their gait. The HMM framework is suitable, because the gait of an individual can be visualized as his adopting postures from a set, in a sequence which has an underlying structured probabilistic nature. The postures that the individual adopts can be regarded as the states of the HMM and are typical to that individual and provide a means of discrimination. The framework assumes that, during gait, the individual transitions between N discrete postures or states but it is not dependent on the particular feature vector used to represent the gait information contained in the postures. The framework, thus, provides flexibility in the selection of the feature vector. The statistical nature of the HMM lends robustness to the model. In this paper we use the binarized background-subtracted image as the feature vector and use different distance metrics, such as those based on the L<sub>1</sub> and L<sub>2</sub> norms of the vector difference, and the normalized inner product of the vectors, to measure the similarity between feature vectors. The results we obtain are better than the baseline recognition rates reported before.},
	keywords = {analysis;, background-subtracted, binarized, discrete, distance, feature, Gait, hidden, human, image, image;, Markov, metrics;, model;, models;, postures;, recognition;, sequences;, vector;},
	doi = {10.1109/ICIP.2003.1246624},
	author = {Sundaresan,Aravind and RoyChowdhury,Amit and Chellapa, Rama}
}
@article {12752,
	title = {A generic approach to simultaneous tracking and verification in video},
	journal = {Image Processing, IEEE Transactions on},
	volume = {11},
	year = {2002},
	month = {2002/05//},
	pages = {530 - 544},
	abstract = {A generic approach to simultaneous tracking and verification in video data is presented. The approach is based on posterior density estimation using sequential Monte Carlo methods. Visual tracking, which is in essence a temporal correspondence problem, is solved through probability density propagation, with the density being defined over a proper state space characterizing the object configuration. Verification is realized through hypothesis testing using the estimated posterior density. In its most basic form, verification can be performed as follows. Given a measurement vector Z and two hypotheses H<sub>1</sub> and H0, we first estimate posterior probabilities P(H<sub>0</sub>|Z) and P(H<sub>1</sub>|Z), and then choose the one with the larger posterior probability as the true hypothesis. Several applications of the approach are illustrated by experiments devised to evaluate its performance. The idea is first tested on synthetic data, and then experiments with real video sequences are presented, illustrating vehicle tracking and verification, human (face) tracking and verification, facial feature tracking, and image sequence stabilization.},
	keywords = {approach;, Carlo, configuration;, correspondence, data;, density, density;, estimated, estimation;, evaluation;, extraction;, Face, facial, feature, generic, human, hypothesis, image, measurement, methods;, Monte, object, performance, posterior, probability, probability;, problem;, processing;, propagation;, recognition;, road, sequence, sequences;, sequential, signal, space;, stabilization;, state, synthetic, temporal, testing;, tracking;, vector;, vehicle, vehicles;, verification;, video, visual},
	isbn = {1057-7149},
	doi = {10.1109/TIP.2002.1006400},
	author = {Li,Baoxin and Chellapa, Rama}
}
