@conference {13123,
	title = {Context and observation driven latent variable model for human pose estimation},
	booktitle = {Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on},
	year = {2008},
	month = {2008/06//},
	pages = {1 - 8},
	abstract = {Current approaches to pose estimation and tracking can be classified into two categories: generative and discriminative. While generative approaches can accurately determine human pose from image observations, they are computationally expensive due to search in the high dimensional human pose space. On the other hand, discriminative approaches do not generalize well, but are computationally efficient. We present a hybrid model that combines the strengths of the two in an integrated learning and inference framework. We extend the Gaussian process latent variable model (GPLVM) to include an embedding from observation space (the space of image features) to the latent space. GPLVM is a generative model, but the inclusion of this mapping provides a discriminative component, making the model observation driven. Observation Driven GPLVM (OD-GPLVM) not only provides a faster inference approach, but also more accurate estimates (compared to GPLVM) in cases where dynamics are not sufficient for the initialization of search in the latent space. We also extend OD-GPLVM to learn and estimate poses from parameterized actions/gestures. Parameterized gestures are actions which exhibit large systematic variation in joint angle space for different instances due to difference in contextual variables. For example, the joint angles in a forehand tennis shot are function of the height of the ball (Figure 2). We learn these systematic variations as a function of the contextual variables. We then present an approach to use information from scene/objects to provide context for human pose estimation for such parameterized actions.},
	keywords = {estimation;, estimation;image, Gaussian, gestures;pose, latent, learning;parameterized, model;human, observations;integrated, pose, process, processes;gesture, processing;pose, recognition;image, tracking;Gaussian, variable},
	doi = {10.1109/CVPR.2008.4587511},
	author = {Gupta,A. and Chen,T. and Chen,F. and Kimber,D. and Davis, Larry S.}
}
@conference {18150,
	title = {A Component Estimation Framework for Information Forensics},
	booktitle = {Multimedia Signal Processing, 2007. MMSP 2007. IEEE 9th Workshop on},
	year = {2007},
	month = {2007/10//},
	pages = {397 - 400},
	abstract = {With a rapid growth of imaging technologies and an increasingly widespread usage of digital images and videos for a large number of high security and forensic applications, there is a strong need for techniques to verify the source and integrity of digital data. Component forensics is new approach for forensic analysis that aims to estimate the algorithms and parameters in each component of the digital device. In this paper, we develop a novel theoretical foundation to understand the fundamental performance limits of component forensics. We define formal notions of identifiability of components in the information processing chain, and present methods to quantify the accuracies at which the component parameters can be estimated. Building upon the proposed theoretical framework, we devise methods to improve the accuracies of component parameter estimation for a wide range of forensic applications.},
	keywords = {component, data, data;, Estimation, estimation;image, forensics;parameter, framework;digital, integrity;imaging, of, processing;security, technologies;information},
	doi = {10.1109/MMSP.2007.4412900},
	author = {Swaminathan,A. and M. Wu and Liu,K. J.R}
}
