@inbook{af92c15b6e964637b52d6cb3a66d4c68,
title = "Estimating 3D human pose from single images using iterative refinement of the prior",
abstract = "This paper proposes a generative method to extract 3D human pose using just a single image. Unlike many existing approaches we assume that accurate foreground background segmentation is not possible and do not use binary silhouettes. A stochastic method is used to search the pose space and the posterior distribution is maximized using Expectation Maximization (EM). It is assumed that some knowledge is known a priori about the position, scale and orientation of the person present and we specifically develop an approach to exploit this. The result is that we can learn a more constrained prior without having to sacrifice its generality to a specific action type. A single prior is learnt using all actions in the Human Eva dataset [9] and we provide quantitative results for images selected across all action categories and subjects, captured from differing viewpoints.",
keywords = "Joints, Three dimensional displays, Image edge detection, Wrist, Image color analysis, humans, Approximation methods, prior refinement, Pose estimation, single image, human eva data set, stochastic processes, expectation-maximisation algorithm ",
author = "Ben Daubney and Xianghua Xie",
year = "2010",
doi = "10.1109/ICPR.2010.840",
language = "English",
isbn = "9780769541099",
series = "Proceedings - International Conference on Pattern Recognition",
pages = "3440--3443",
booktitle = "Proceedings - International Conference on Pattern Recognition",
}