Articulated hand tracking by PCA-ICA approach

This paper introduces a new representation of hand motions for tracking and recognizing hand-finger gestures in an image sequence. A human hand has 15 joints and its high dimensionality makes it difficult to model hand motions. To make things easier, it is important to represent a hand motion in a low dimensional space. Principle component analysis (PCA) has been proposed to reduce the dimensionality. However, the PCA basis vectors only represent global features, which are not optimal to represent intrinsic features. This paper proposes an efficient representation of hand motions by independent component analysis (ICA). The ICA basis vectors represent local features, each of which corresponds to the motion of a particular finger. This representation is more efficient in modeling hand motions for tracking and recognizing hand-finger gestures in an image sequence. This paper demonstrates the effectiveness of our method by tracking hands in real image sequences

[1]  Björn Stenger,et al.  Filtering using a tree-based estimator , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[2]  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..

[3]  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).

[4]  Michael Isard,et al.  Contour Tracking by Stochastic Propagation of Conditional Density , 1996, ECCV.

[5]  Michael Isard,et al.  CONDENSATION—Conditional Density Propagation for Visual Tracking , 1998, International Journal of Computer Vision.

[6]  David Windridge,et al.  Hidden Markov chain estimation and parameterisation via ICA-based feature-selection , 2005, Pattern Analysis and Applications.

[7]  Yen-Wei Chen,et al.  Independent Component Analysis for Color Indexing , 2004, IEICE Trans. Inf. Syst..

[8]  Marian Stewart Bartlett,et al.  Face recognition by independent component analysis , 2002, IEEE Trans. Neural Networks.

[9]  Terrence J. Sejnowski,et al.  The “independent components” of natural scenes are edge filters , 1997, Vision Research.

[10]  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.

[11]  Yen-Wei Chen,et al.  Classification of Remotely Sensed Images Using Independent Component Analysis and Spatial Consistency , 2004, J. Adv. Comput. Intell. Intell. Informatics.

[12]  Terrence J. Sejnowski,et al.  Blind separation and blind deconvolution: an information-theoretic approach , 1995, 1995 International Conference on Acoustics, Speech, and Signal Processing.

[13]  Thomas S. Huang,et al.  Tracking articulated hand motion with eigen dynamics analysis , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[14]  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.

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

[16]  Yen-Wei Chen,et al.  Robust Edge Detection by Independent Component Analysis in Noisy Images , 2004, IEICE Trans. Inf. Syst..

[17]  Tosiyasu L. Kunii,et al.  Model-based analysis of hand posture , 1995, IEEE Computer Graphics and Applications.

[18]  Stan Sclaroff,et al.  3D hand pose reconstruction using specialized mappings , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.