Earticle

초록

영어

Static hand gesture recognition is very important for human-computer interaction systems, which is widely used in human-computer interaction systems. Using visible RGB images as the input method would be more stable, but the illumination and skin color are big problems; by contrast the depth images is a better choice. A novel algorithm which recognizes static hand gesture based on depth images using 3d shape context feature and improves the performance by arm major axis correction and contour-center sampling is proposed. Arm major axis correction can solve the rotation problem and the sampling, which increases the recognition rate. Besides, using 3d space information makes the algorithm more stable. Experimental results show that the average recognition rate gets to 95.8%, and the performance and speed are superior to existing algorithms. Recognition results can be widely used in the follow-on real scene human-computer interaction (HCI) operations.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Gesture Recognition Algorithm Based on 3D Shape Context
  3.1 The Input Depth Image Preprocessing: Foreground Segmentation, Arm Axis Correction
  3.2. The 3D Shape Context
  3.3. The Random Forest
 4. Experimental Results and Analysis
  4.1. The Experimental Configuration
  4.2. Analysis and Comparison of Static Hand Gesture Recognition Performance
  4.3. Future Work
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Mei Bie [ Institute of Media and Communications, Changchun Normal University, China ]
• Zhe Wang [ Educational Technology Center of Jilin Province, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605