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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/20266

Title: Articulated pose identification with sparse point features
Authors: Li, Baihua
Meng, Qinggang
Holstein, Horst
Issue Date: 2004
Publisher: © IEEE
Citation: LI, B., MENG, Q. and HOLSTEIN, H., 2004. Articulated pose identification with sparse point features. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 34 (3), pp.1412-1422
Abstract: We propose a general algorithm for identifying an arbitrary pose of an articulated subject with sparse point features. The algorithm aims to identify a one-to-one correspondence between a model point-set and an observed point-set taken from freeform motion of the articulated subject. We avoid common assumptions such as pose similarity or small motions with respect to the model, and assume no prior knowledge from which to infer an initial or partial correspondence between the two point-sets. The algorithm integrates local segment-based correspondences under a set of affine transformations, and a global hierarchical search strategy. Experimental results, based on synthetic pose and real-world human motion data demonstrate the ability of the algorithm to perform the identification task. Reliability is increasingly compromised with increasing data noise and segmental distortion, but the algorithm can tolerate moderate levels. This work contributes to establishing a crucial self-initializing identification in model-based point-feature tracking for articulated motion.
Description: This paper is closed access.
Version: Closed access
DOI: 10.1109/TSMCB.2004.825914
URI: https://dspace.lboro.ac.uk/2134/20266
Publisher Link: http://dx.doi.org/10.1109/TSMCB.2004.825914
ISSN: 1083-4419
Appears in Collections:Closed Access (Computer Science)

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