Abstract.
Recently, we developed a technique that allows semi-automatic estimation of anthropometry and pose from a single image. However, estimation was limited to a class of images for which an adequate number of human body segments were almost parallel to the image plane. In this paper, we present a generalization of that estimation algorithm that exploits pairwise geometric relationships of body segments to allow estimation from a broader class of images. In addition, we refine our search space by constructing a fully populated discrete hyper-ellipsoid of stick human body models in order to capture the variance of the statistical anthropometric information. As a result, a better initial estimate can be computed by our algorithm and thus the number of iterations needed during minimization are reduced tenfold. We present our results over a variety of images to demonstrate the broad coverage of our algorithm.
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Published online: 1 September 2003
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Barrón, C., Kakadiaris, I.A. On the improvement of anthropometry and pose estimation from a single uncalibrated image. Machine Vision and Applications 14, 229–236 (2003). https://doi.org/10.1007/s00138-002-0088-8
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DOI: https://doi.org/10.1007/s00138-002-0088-8