Grasp Gesture Recognition Based on Rough Sets Theory in AR System

Real-time human-computer interaction is one of the three important features of augmented reality technology. H and i s t he par t of bo dy used m ost frequ ently for th e inte ractions b etween u sers an d objects in virtual scene. Because of the complexity and flexibility of hand motions, it is a hot research topic to recognize the user's grasp gesture accurately and to determine whether the grasp is successful. In this pa per, grasp g esture recogni tion a lgorithm based on roug h sets theo ry wa s proposed a fter analyzing the structure of hand and the features of grasp action, and a related system was developed. The results show that by using this method, users can grasp the virtual objects easily. The interaction process is accurate and natural, which enhances the users' immersion feeling.

[1]  Victor B. Zordan,et al.  Physically based grasping control from example , 2005, SCA '05.

[2]  Jerzy W. Grzymala-Busse,et al.  Rough Sets , 1995, Commun. ACM.

[3]  K. Wan,et al.  Dynamic gesture recognition based on the probabilistic distribution of arm trajectory , 2008, 2008 IEEE International Conference on Mechatronics and Automation.

[4]  Katsushi Ikeuchi,et al.  Grasp recognition using a 3D articulated model and infrared images , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[5]  Javier Ruiz-del-Solar,et al.  Dynamic gesture recognition for human robot interaction , 2009, 2009 6th Latin American Robotics Symposium (LARS 2009).

[6]  Thomas S. Huang,et al.  Static Hand Gesture Recognition based on Local Orientation Histogram Feature Distribution Model , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[7]  Zhu Ji,et al.  Hand Gesture Recognition Based on Structure Analysis , 2006 .

[8]  Stefano Caselli,et al.  Grasp recognition in virtual reality for robot pregrasp planning by demonstration , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[9]  Chun-Liang Tung,et al.  Dynamic hand gesture recognition using hierarchical dynamic Bayesian networks through low-level image processing , 2008, 2008 International Conference on Machine Learning and Cybernetics.

[10]  Yutaka Hirano,et al.  Image-based object recognition and dexterous hand/arm motion planning using RRTs for grasping in cluttered scene , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[11]  Fang Yi-kai A Hand Gesture Recognition Method with Fast Scale-space Feature Detection , 2009 .

[12]  S Padam Priyal,et al.  A study on static hand gesture recognition using moments , 2010, 2010 International Conference on Signal Processing and Communications (SPCOM).

[13]  Yang Luo,et al.  Realistic virtual hand modeling with applications for virtual grasping , 2004, VRCAI '04.

[14]  Yu Sun,et al.  Static Hand Gesture Recognition and its Application based on Support Vector Machines , 2008, 2008 Ninth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing.

[15]  Xu De-you Hand Gesture-based Interaction in Virtual Reality Training System , 2006 .

[16]  Ronald Azuma,et al.  A Survey of Augmented Reality , 1997, Presence: Teleoperators & Virtual Environments.