@conference {12591,
	title = {Simulation and Analysis of Human Walking Motion},
	booktitle = {Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on},
	volume = {1},
	year = {2007},
	month = {2007/04//},
	pages = {I-797 -I-800 - I-797 -I-800},
	abstract = {Simulation and analysis of human walking motion has applications in surveillance and healthcare. In this paper we discuss an approach for modeling human walking motion using a mechanical model in the form of a kinematic chain consisting of rigid links and revolute joints. Our goal is to discriminate different types of walking motions using information such as joint torque and angle sequences extracted from the model. The angle sequences are initially extracted using 3D geometry. From these angle sequences we extract the torque sequences using a recursive Newton Euler inverse dynamics algorithm. Time series models and dynamic time warping of the torque and angle sequences are used to characterize and discriminate different walking patterns. A forward dynamics algorithm is also presented for synthesizing different walking sequences like limping from a normal walking torque sequence},
	keywords = {3D, algorithm;revolute, analysis;image, chain;mechanical, DYNAMICS, Euler, extraction;image, geometry;angle, inverse, joints;rigid, links;surveillance;time, method;feature, model;recursive, models;torque, MOTION, motion;kinematic, Newton, patterns;Newton, problems;time, sequences;dynamic, sequences;inverse, sequences;walking, series, series;time, simulation;torque;, TIME, walking, warp, warping;healthcare;human},
	doi = {10.1109/ICASSP.2007.366028},
	author = {Nandy, K. and Chellapa, Rama}
}
@conference {13239,
	title = {Mean-shift analysis using quasiNewton methods},
	booktitle = {Image Processing, 2003. ICIP 2003. Proceedings. 2003 International Conference on},
	volume = {2},
	year = {2003},
	month = {2003/09//},
	pages = {II - 447-50 vol.3 - II - 447-50 vol.3},
	abstract = {Mean-shift analysis is a general nonparametric clustering technique based on density estimation for the analysis of complex feature spaces. The algorithm consists of a simple iterative procedure that shifts each of the feature points to the nearest stationary point along the gradient directions of the estimated density function. It has been successfully applied to many applications such as segmentation and tracking. However, despite its promising performance, there are applications for which the algorithm converges too slowly to be practical. We propose and implement an improved version of the mean-shift algorithm using quasiNewton methods to achieve higher convergence rates. Another benefit of our algorithm is its ability to achieve clustering even for very complex and irregular feature-space topography. Experimental results demonstrate the efficiency and effectiveness of our algorithm.},
	keywords = {analysis;, classification;, clustering, clustering;, Convergence, density, estimation;, feature-space, image, irregular, iterative, Mean-shift, method;, methods;, Newton, nonparametric, pattern, procedure;, quasiNewton, rates;, segmentation;, technique;, topography;},
	doi = {10.1109/ICIP.2003.1246713},
	author = {Yang,C. and Duraiswami, Ramani and DeMenthon,D. and Davis, Larry S.}
}
@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}
}
