Abstract
We are researching for real-time hand shape estimation, which we are going to apply to user interface and interactive applications. We have employed a computer vision approach, since unwired sensing provides restriction-free observation, or a natural way of sensing. The problem is that since a human hand has many joints, it has geometrically high degrees of freedom, which makes hand shape estimation difficult. For example, we have to deal with a self-occlusion problem and a large amount of computation. At the same time, a human hand has several physical constraints, i.e., each joint has a movable range and interdependence, which can potentially reduce the search space of hand shape estimation. This paper proposes a novel method to estimate 3D hand shapes in real-time by using shape features acquired from camera images and physical hand constraints heuristically introduced. We have made preliminary experiments using multiple cameras under uncomplicated background. We show experimental results in order to verify the effectiveness of our proposed method.
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References
Shimada, N., Shirai, Y.: Hand Posture Estimation based on 2D Appearance Retrieval Using Monocular Camera. In: Proc. Int, Workshop on RATFG-RTS, pp. 23–30 (2001)
Stenger, B., Thayananthan, A., Torr, P.H.S., Cipolla, R.: Filtering using a tree-based estimater. In: Proc. ICCV, pp. 1063–1070 (2003)
Ueda, E., Matsumoto, Y., Imai, M., Ogasawara, T.: Hand Pose Estimation for Vision-based Human Interface. IEEE Trans. on Industrial Electronics. 50(4), 676–684 (2003)
Lu, S., Metaxas, D., Samaras, D., Oliensis, J.: Using multiple cues for hand tracking and model refinement. In: Proc. CVPR, pp. 443–450 (2003)
Häger-Ross, C., Schieber, M.H.: Quantifying the Independence of Hand Finger Movements: Comparisons of Digit, Hands, nad Movement Frequencies. The Journal of Neuroscience 20(22), 8542–8550 (2000)
Kamper, D.G., Cruz, E.G., Siegel, M.P.: Stereotypical fingertip trajectories during grasp. Journal of Neurophysiology 90(6), 3702–3710 (2003)
ElKoura, G., Singh, K.: Handrix: Animating the Human Hand. In: Proc. SIGGRAPH, pp. 110–119 (2003)
Wang, L.T., Chen, C.C.: A combined optimization method for solving the inverse kinematics problem of mechanical manipulators. IEEE Trans. on Robotics and Automations 17(4), 489–499 (1991)
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© 2005 Springer-Verlag Berlin Heidelberg
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Fujiki, R., Arita, D., Taniguchi, Ri. (2005). Real-Time 3D Hand Shape Estimation Based on Inverse Kinematics and Physical Constraints. In: Roli, F., Vitulano, S. (eds) Image Analysis and Processing – ICIAP 2005. ICIAP 2005. Lecture Notes in Computer Science, vol 3617. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11553595_104
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DOI: https://doi.org/10.1007/11553595_104
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28869-5
Online ISBN: 978-3-540-31866-8
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