3D Posture Representation Using Meshless Parameterization with Cylindrical Virtual Boundary

3D data is getting popular which offers more details and accurate information for posture recognition. However, it leads to computational hurdles and is not suitable for real time application. Therefore, we introduce a dimension reduction method using meshless parameterization with cylindrical virtual boundary for 3D posture representation. The meshless parameterization is based on convex combination approach which has good properties, such as fast computation and one-to-one mapping characteristic. This method depends on the number of boundary points. However, 3D posture reconstruction using silhouettes extraction from multiple cameras had resulted various number of boundary points. Therefore, a cylindrical virtual boundary points is introduced to overcome the inconsistency of 3D reconstruction boundary points. The proposed method generates five slices of 2D parametric appearance to represent a 3D posture for recognition purpose.

[1]  Zhanyi Hu,et al.  Gesture Recognition Using Quadratic Curves , 2006, ACCV.

[2]  Martin Reimers,et al.  Meshless parameterization and surface reconstruction , 2001, Comput. Aided Geom. Des..

[3]  Shree K. Nayar,et al.  Computer Vision - ACCV 2006, 7th Asian Conference on Computer Vision, Hyderabad, India, January 13-16, 2006, Proceedings, Part I , 2006, ACCV.

[4]  Ying Wu,et al.  Vision-Based Gesture Recognition: A Review , 1999, Gesture Workshop.

[5]  Eunjung Han,et al.  Dimension Reduction in 3D Gesture Recognition Using Meshless Parameterization , 2006, PSIVT.

[6]  Jitendra Malik,et al.  Recovering human body configurations: combining segmentation and recognition , 2004, CVPR 2004.

[7]  Antonio Camurri,et al.  Gesture-Based Communication in Human-Computer Interaction , 2003, Lecture Notes in Computer Science.

[8]  Sang-Woong Lee,et al.  Real-Time Gesture Recognition Using 3D Motion History Model , 2005, ICIC.

[9]  Yoichi Sato,et al.  Real-time input of 3D pose and gestures of a user's hand and its applications for HCI , 2001, Proceedings IEEE Virtual Reality 2001.

[10]  Kai Hormann,et al.  Surface Parameterization: a Tutorial and Survey , 2005, Advances in Multiresolution for Geometric Modelling.

[11]  Luis Rueda,et al.  Advances in Image and Video Technology, Second Pacific Rim Symposium, PSIVT 2007, Santiago, Chile, December 17-19, 2007, Proceedings , 2007, PSIVT.

[12]  J. Oden,et al.  The Mathematics of Surfaces II , 1988 .

[13]  Stephen J. McKenna,et al.  An Experimental Comparison of Trajectory-Based and History-Based Representation for Gesture Recognition , 2003, Gesture Workshop.

[14]  Isaac Cohen,et al.  Posture and Gesture Recognition using 3D Body Shapes Decomposition , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[15]  Michael S. Floater,et al.  Meshless Parameterization and B-Spline Surface Approximation , 2000, IMA Conference on the Mathematics of Surfaces.

[16]  Gregory D. Hager,et al.  Gesture Recognition Using 3D Appearance and Motion Features , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[17]  Joshua B. Tenenbaum,et al.  Global Versus Local Methods in Nonlinear Dimensionality Reduction , 2002, NIPS.

[18]  Rémi Ronfard,et al.  Motion History Volumes for Free Viewpoint Action Recognition , 2005 .

[19]  Michael G. Strintzis,et al.  A gesture recognition system using 3D data , 2002, Proceedings. First International Symposium on 3D Data Processing Visualization and Transmission.

[20]  Xiao-Ping Zhang,et al.  Advances in Intelligent Computing, International Conference on Intelligent Computing, ICIC 2005, Hefei, China, August 23-26, 2005, Proceedings, Part I , 2005, ICIC.

[21]  Bian Wu,et al.  A hand gesture recognition system based on local linear embedding , 2005, J. Vis. Lang. Comput..