Abstract
Estimation of 3D hand pose is useful in many gesture recognition applications, ranging from human-computer interaction to recognition of sign languages. In this paper, 3D hand pose estimation is treated as a database indexing problem. Given an input image of a hand, the most similar images in a large database of hand images are retrieved. The hand pose parameters of the retrieved images are used as estimates for the hand pose in the input image. Lipschitz embeddings are used to map edge images of hands into a Euclidean space. Similarity queries are initially performed in this Euclidean space, to quickly select a small set of candidate matches. These candidate matches are finally ranked using the more computationally expensive chamfer distance. Using Lipschitz embeddings to select likely candidate matches greatly reduces retrieval time over applying the chamfer distance to the entire database, without significant losses in accuracy.
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References
Athitsos, V., Sclaroff, S.: An appearance-based framework for 3D hand shape classification and camera viewpoint estimation. Automatic Face and Gesture Recognition, 45–50 (2002)
Athitsos, V., Sclaroff, S.: Estimating hand pose from a cluttered image. In: CVPR, pp. 432–439 (2003)
Barrow, H.G., Tenenbaum, J.M., Bolles, R.C., Wolf, H.C.: Parametric correspondence and chamfer matching: Two new techniques for image matching. In: IJCAI, pp. 659–663 (1977)
Bourgain, J.: On Lipschitz embeddings of finite metric spaces in hilbert space. Israel Journal of Mathematics 52, 46–52 (1985)
Faloutsos, C., Lin, K.I.: FastMap: A fast algorithm for indexing, data-mining and visualization of traditional and multimedia datasets. In: ACM SIGMOD International Conference on Management of Data, pp. 163–174 (1995)
Freeman, W.T., Roth, M.: Computer vision for computer games. Automatic Face and Gesture Recognition, 100–105 (1996)
Heap, T., Hogg, D.: Towards 3D hand tracking using a deformable model. Face and Gesture Recognition, 140–145 (1996)
Hjaltason, G., Samet, H.: Contractive embedding methods for similarity searching in metric spaces. Technical Report TR-4102, Computer Science Department, University of Maryland (2000)
Hristescu, G., Farach-Colton, M.: Cluster-preserving embedding of proteins. Technical Report 99-50, Computer Science Department, Rutgers University (1999)
Linial, N., London, E., Rabinovich, Y.: The geometry of graphs and some of its algorithmic applications. In: IEEE Symposium on Foundations of Computer Science, pp. 577–591 (1994)
Moghaddam, B., Pentland, A.: Probabilistic visual learning for object detection. Technical Report 326, MIT (June 1995)
Nölker, C., Ritter, H.: Parametrized SOMs for hand posture reconstruction. In: IJCNN, pp. 4139–4144 (2000)
Rehg, J.M.: Visual Analysis of High DOF Articulated Objects with Application to Hand Tracking. PhD thesis, Electrical and Computer Eng., Carnegie Mellon University (1995)
Rosales, R., Athitsos, V., Sigal, L., Sclaroff, S.: 3D hand pose reconstruction using specialized mappings. In: ICCV, vol. 1, pp. 378–385 (2001)
Segen, J., Kumar, S.: Shadow gestures: 3D hand pose estimation using a single camera. In: CVPR, pp. 479–485 (1999)
Shimada, N., Kimura, K., Shirai, Y.: Real-time 3-D hand posture estimation based on 2-D appearance retrieval using monocular camera. Recognition, Analysis and Tracking of Faces and Gestures in Realtime Systems, 23–30 (2001)
Stenger, B., Mendonça, P.R.S., Cipolla, R.: Model based 3D tracking of an articulated hand. In: CVPR, vol. 2, pp. 310–315 (2001)
Triesch, J., von der Malsburg, C.: Robotic gesture recognition. In: Gesture Workshop, pp. 233–244 (1997)
Virtual Technologies, Inc., Palo Alto, CA. VirtualHand Software Library Reference Manual (August 1998)
Wu, Y., Huang, T.S.: View-independent recognition of hand postures. In: CVPR, vol. 2, pp. 88–94 (2000)
Wu, Y., Lin, J.Y., Huang, T.S.: Capturing natural hand articulation. In: ICCV, vol. 2, pp. 426–432 (2001)
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Athitsos, V., Sclaroff, S. (2004). Database Indexing Methods for 3D Hand Pose Estimation. In: Camurri, A., Volpe, G. (eds) Gesture-Based Communication in Human-Computer Interaction. GW 2003. Lecture Notes in Computer Science(), vol 2915. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24598-8_27
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DOI: https://doi.org/10.1007/978-3-540-24598-8_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-21072-6
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