Database Indexing Methods for 3D Hand Pose Estimation

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.

[1]  Stan Sclaroff,et al.  An appearance-based framework for 3D hand shape classification and camera viewpoint estimation , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[2]  Robert C. Bolles,et al.  Parametric Correspondence and Chamfer Matching: Two New Techniques for Image Matching , 1977, IJCAI.

[3]  Stan Sclaroff,et al.  Estimating 3D hand pose from a cluttered image , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[4]  Rómer Rosales,et al.  3D Hand Pose Reconstruction Using Specialized Mappings , 2001, ICCV.

[5]  Ying Wu,et al.  View-independent recognition of hand postures , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[6]  Ying Wu,et al.  Capturing natural hand articulation , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[7]  H. Samet.  Contractive Embedding Methods for Similarity Searching in Metric Spaces , 2000 .

[8]  David C. Hogg,et al.  Towards 3D hand tracking using a deformable model , 1996, Proceedings of the Second International Conference on Automatic Face and Gesture Recognition.

[9]  Kazuo Kyuma,et al.  Computer vision for computer games , 1996, Proceedings of the Second International Conference on Automatic Face and Gesture Recognition.

[10]  Alex Pentland,et al.  Probabilistic visual learning for object detection , 1995, Proceedings of IEEE International Conference on Computer Vision.

[11]  H. Gabriela,et al.  Cluster-preserving Embedding of Proteins , 1999 .

[12]  Helge J. Ritter,et al.  Parametrized SOMs for hand posture reconstruction , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[13]  J. Bourgain.  On lipschitz embedding of finite metric spaces in Hilbert space , 1985 .

[14]  Jochen Triesch,et al.  Robotic Gesture Recognition , 1997, Gesture Workshop.

[15]  Christos Faloutsos,et al.  FastMap: a fast algorithm for indexing, data-mining and visualization of traditional and multimedia datasets , 1995, SIGMOD '95.

[16]  Yoshiaki Shirai,et al.  Real-time 3D hand posture estimation based on 2D appearance retrieval using monocular camera , 2001, Proceedings IEEE ICCV Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems.

[17]  Jakub Segen,et al.  Shadow gestures: 3D hand pose estimation using a single camera , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[18]  Paulo R. S. Mendonça,et al.  Model-based 3D tracking of an articulated hand , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[19]  James M. Rehg.  Visual analysis of high DOF articulated objects with application to hand tracking , 1995 .