• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

MultiView-Based Hand Posture Recognition Method Based on Point Cloud

Vol. 9, No.7, July 31, 2015
10.3837/tiis.2015.07.014, Download Paper (Free):

Abstract

Hand posture recognition has played a very important role in Human Computer Interaction (HCI) and Computer Vision (CV) for many years. The challenge arises mainly due to self-occlusions caused by the limited view of the camera. In this paper, a robust hand posture recognition approach based on 3D point cloud from two RGB-D sensors (Kinect) is proposed to make maximum use of 3D information from depth map. Through noise reduction and registering two point sets obtained satisfactory from two views as we designed, a multi-viewed hand posture point cloud with most 3D information can be acquired. Moreover, we utilize the accurate reconstruction and classify each point cloud by directly matching the normalized point set with the templates of different classes from dataset, which can reduce the training time and calculation. Experimental results based on posture dataset captured by Kinect sensors (from digit 1 to 10) demonstrate the effectiveness of the proposed method.


Statistics

Show / Hide Statistics

Statistics (Cumulative Counts from December 1st, 2015)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.


Cite this article

[IEEE Style]
Wenkai Xu, Ick-Soo Lee, Suk-Kwan Lee, Bo Lu and Eung-Joo Lee, "MultiView-Based Hand Posture Recognition Method Based on Point Cloud," KSII Transactions on Internet and Information Systems, vol. 9, no. 7, pp. 2585-2598, 2015. DOI: 10.3837/tiis.2015.07.014

[ACM Style]
Xu, W., Lee, I., Lee, S., Lu, B., and Lee, E. 2015. MultiView-Based Hand Posture Recognition Method Based on Point Cloud. KSII Transactions on Internet and Information Systems, 9, 7, (2015), 2585-2598. DOI: 10.3837/tiis.2015.07.014