@conference {12554,
	title = {Factorized variational approximations for acoustic multi source localization},
	booktitle = {Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on},
	year = {2008},
	month = {2008/04/31/4},
	pages = {2409 - 2412},
	abstract = {Estimation based on received signal strength (RSS) is crucial in sensor networks for sensor localization, target tracking, etc. In this paper, we present a Gaussian approximation of the Chi distribution that is applicable to general RSS source localization problems in sensor networks. Using our Gaussian approximation, we provide a factorized variational Bayes (VB) approximation to the location and power posterior of multiple sources using a sensor network. When the source signal and the sensor noise have uncorrelated Gaussian distributions, we demonstrate that the envelope of the sensor output can be accurately modeled with a multiplicative Gaussian noise model. In turn, our factorized VB approximations decrease the computational complexity and provide computational robustness as the number of targets increases. Simulations are provided to demonstrate the effectiveness of the proposed approximations.},
	keywords = {approximation;acoustic, approximation;Bayes, approximation;multisensor, approximation;variational, Bayes, Chi, detection;wireless, distribution;Gaussian, localization;factorized, localization;sensor, methods;Gaussian, multisource, networks;, networks;stochastic, processes;acoustic, sensor, signal, strength;sensor, systems;object, tracking;received, variational},
	doi = {10.1109/ICASSP.2008.4518133},
	author = {Cevher, V. and Sankaranarayanan,A. C and Chellapa, Rama}
}
@conference {12592,
	title = {Markerless Monocular Tracking of Articulated Human Motion},
	booktitle = {Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on},
	volume = {1},
	year = {2007},
	month = {2007/04//},
	pages = {I-693 -I-696 - I-693 -I-696},
	abstract = {This paper presents a method for tracking general 3D general articulated human motion using a single camera with unknown calibration data. No markers, special clothes, or devices are assumed to be attached to the subject. In addition, both the camera and the subject are allowed to move freely, so that long-term view-independent human motion tracking and recognition are possible. We exploit the fact that the anatomical structure of the human body can be approximated by an articulated blob model. The optical flow under scaled orthographic projection is used to relate the spatial-temporal intensity change of the image sequence to the human motion parameters. These motion parameters are obtained by solving a set of linear equations to achieve global optimization. The correctness and robustness of the proposed method are demonstrated using Tai Chi sequences},
	keywords = {analysis;image, blob, camera;spatial-temporal, Chi, equations;markerless, flow;scaled, human, intensity;cameras;gait, model;articulated, monocular, MOTION, motion;global, optimization;human, orthographic, projection;single, recognition;image, sequence;linear, sequences;anatomical, sequences;optimisation;, structure;articulated, Tai, tracking;optical},
	doi = {10.1109/ICASSP.2007.366002},
	author = {Liu,Haiying and Chellapa, Rama}
}
