@conference {13179,
	title = {Semi-Parametric Model-Based Clustering for DNA Microarray Data},
	booktitle = {Pattern Recognition, 2006. ICPR 2006. 18th International Conference on},
	volume = {3},
	year = {2006},
	month = {2006//00/0},
	pages = {324 - 327},
	abstract = {Various clustering methods have been proposed for the analysis of gene expression data, but conventional clustering algorithms have several critical limitations; how to set parameters such as number of clusters, initial cluster centers, and so on. In this paper, we propose a semi-parametric model-based clustering algorithm in which the underlying model is a mixture of Gaussian. Each gene expression data builds a Gaussian kernel, and the uncertainty of microarray data is naturally integrated in the data representation. Our algorithm provides a principled method to automatically determine parameters - number of components in the mixture, mean, covariance, and weight of each Gaussian - by mean-shift procedure (Comaniciu and Meer, 1999) and curvature fitting. After the initialization, expectation maximization (EM) algorithm is employed for clustering to achieve maximum likelihood (ML). The performance of our algorithm is compared with standard EM algorithm using real data as well as synthetic data},
	keywords = {clustering;, clustering;DNA;Gaussian, computing;genetics;pattern, data;Gaussian, data;maximum, DNA, Expression, fitting;data, kernel;Gaussian, likelihood;mean-shift, maximization;gene, Microarray, mixtures;curvature, model-based, procedure;semiparametric, processes;biology, representation;expectation},
	doi = {10.1109/ICPR.2006.1044},
	author = {Han,Bohyung and Davis, Larry S.}
}
@conference {12697,
	title = {View independent human body pose estimation from a single perspective image},
	booktitle = {Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on},
	volume = {2},
	year = {2004},
	month = {2004/07/02/june},
	pages = {II-16 - II-22 Vol.2 - II-16 - II-22 Vol.2},
	abstract = {Recovering the 3D coordinates of various joints of the human body from an image is a critical first step for several model-based human tracking and optical motion capture systems. Unlike previous approaches that have used a restrictive camera model or assumed a calibrated camera, our work deals with the general case of a perspective uncalibrated camera and is thus well suited for archived video. The input to the system is an image of the human body and correspondences of several body landmarks, while the output is the set of 3D coordinates of the landmarks in a body-centric coordinate system. Using ideas from 3D model based invariants, we set up a polynomial system of equations in the unknown head pitch, yaw and roll angles. If we are able to make the often-valid assumption that the torso twist is small, there are finite numbers of solutions to the head-orientation that can be computed readily. Once the head orientation is computed, the epipolar geometry of the camera is recovered, leading to solutions to the 3D joint positions. Results are presented on synthetic and real images.},
	keywords = {3D, analysis;, biomechanics;, body, body-centric, camera;, capture, coordinate, coordinates;, detection;, epipolar, equation, estimation;, geometry;, human, image, image;, images;, model-based, models;, MOTION, object, optical, perspective, physiological, polynomial, polynomials;, pose, real, single, synthetic, system;, systems;, torso, tracking;, twist;, uncalibrated},
	doi = {10.1109/CVPR.2004.1315139},
	author = {Parameswaran, V. and Chellapa, Rama}
}
