Abstract
In this work we propose, for the first time, to improve the performance of a Hand Pose Reconstruction (HPR) technique from RGBD camera data, which is affected by self-occlusions, leveraging upon postural synergy information, i.e., a priori information on how human most commonly use and shape their hands in everyday life tasks. More specifically, in our approach, we ignore joint angle values estimated with low confidence through a vision-based HPR technique and fuse synergistic information with such incomplete measures. Preliminary experiments are reported showing the effectiveness of the proposed integration.
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Notes
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Images and depth maps are captured at \(640\times 480@24\) bit and \(640\times 480@16\) bit, respectively.
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Implementation available online at: http://cvrlcode.ics.forth.gr/handtracking/.
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Acknowledgment
This work is supported in part by the European Research Council under the Advanced Grant SoftHands (No. ERC-291166), by the EU H2020 projects SoftPro (No. 688857) and SOMA (No. 645599), and by the EU FP7 project WEARHAP (No. 601165).
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Ciotti, S. et al. (2017). Synergy-Driven Performance Enhancement of Vision-Based 3D Hand Pose Reconstruction. In: Perego, P., Andreoni, G., Rizzo, G. (eds) Wireless Mobile Communication and Healthcare. MobiHealth 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 192. Springer, Cham. https://doi.org/10.1007/978-3-319-58877-3_42
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