Design of Computer Vision Interface by Recognizing Hand Motion

손동작 인식에 의한 컴퓨터 비전 인터페이스 설계

  • Yun, Jin-Hyun (Department of Information and communication Engineering, Inha University) ;
  • Lee, Chong-Ho (Department of Information and communication Engineering, Inha University)
  • 윤진현 (인하대학교 정보통신공학과) ;
  • 이종호 (인하대학교 정보통신공학과)
  • Received : 2010.04.05
  • Accepted : 2010.04.30
  • Published : 2010.05.25

Abstract

As various interfacing devices for computational machines are being developed, a new HCI method using hand motion input is introduced. This interface method is a vision-based approach using a single camera for detecting and tracking hand movements. In the previous researches, only a skin color is used for detecting and tracking hand location. However, in our design, skin color and shape information are collectively considered. Consequently, detection ability of a hand increased. we proposed primary orientation edge descriptor for getting an edge information. This method uses only one hand model. Therefore, we do not need training processing time. This system consists of a detecting part and a tracking part for efficient processing. In tracking part, the system is quite robust on the orientation of the hand. The system is applied to recognize a hand written number in script style using DNAC algorithm. Performance of the proposed algorithm reaches 82% recognition ratio in detecting hand region and 90% in recognizing a written number in script style.

Acknowledgement

Supported by : 인하대학교

References

  1. A.Jaimes, N.Sebe, "Multimodal Human Computer Interaction: A Survey," LNCS, vol. 3766, pp.1- 15, 2005.
  2. N.Sebe, I.Cohen, T.Gevers, T.Huang, "Multimodal Approaches for Emotion Recognition: A Survey," Proceedings of the SPIE, Volume 5670, pp. 56-67, 2004.
  3. 배창석, 전병태, 윤호섭, 민병우 "손의 이동 궤적 분석에 의한 제스처 인식," 전자공학회 워크샵, 제 8권, pp.144-148, Jan. 1996.
  4. Pragati Garg, Naveen Aggarwal, Sanjeev Sofat, "Vision Based and Gesture Recognition.," PWASET, volume 37, 2009.
  5. A. Mulder, "Hand gestures for HCI," Technical Report 96-1, vol. Simon Fraster University, 1996
  6. B. Stenger, A. Thayananthan, P.H.S. Torr, R. Cipolla, "Model-based hand tracking using a hierarchical Bayesian filter," IEEE transactions on pattern analysis and machine intelligence, September 2006.
  7. 고민삼, 이광희, 김창우, 안준호, 김인중, "비전 기반 제스처 인식을 이용한 인터페이스 구현," 한국컴퓨터종합학술대회 논문집, vol. 35 No.1, 2008.
  8. Tarek M. Mahmoud, "A New Fast Skin Color Detection Technique," PWASET, vol.33. September 2008.
  9. R., Gonzales, and E., Woods, "Digital Image Processing," Prentice Hall, Inc, New Jersey, 2002.
  10. B.D., Zarit, B.J., Super, and F.K.H. Quek, "Comparison of five color models in skin pixel classification.," In Int. Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems, pages 58-63, Corfu, Greece, Sept. 1999.
  11. D. Chai, and K.N. Ngan, "Face segmentation using skin-color map in videophone applications," IEEE Trans. on Circuits and Systems for Video Technology, 9(4): 551-564, June 1999. https://doi.org/10.1109/76.767122
  12. W. T. Freeman and M. Roth. "Orientation histograms for hand gesture recognition," Intl. Workshop on Automatic Face and Gesture Recognition, IEEE Computer Society, Zurich, Switzerland, pages 296.301, June 1995.
  13. K. Hotta, "Scene Classification Based on multi-resulution Orientation Histogram of Gabor Features," ICVS 2008, LNCS 5008, pp.291~301, 2008.
  14. P. Buehler, M. Everingham, A. Zisserman, "Learning sign language by watching TV (using weakly aligned subtitles)," IEEE CVPR 2009.
  15. N. Dalal and B. Triggs. "Histogram of oriented gradients for human detection." In Proc. CVPR, 2005.
  16. 서승원, 선우명훈, "Template Matching을 위한 새로운 알고리즘 및 ASIC칩 구현," 전자공학회 논문지, 제3권, C편, 제1호,pp.15-24, Jan. 1998.
  17. 권오혁, "DNA 특성을 모방한 디지털 패턴인식 하드웨어 설계," 인하대학교 정보통신공학과 석사학위논문, 2008.
  18. 최선욱, "Multi-class 데이터 분류를 위한 DNA 컴퓨팅 기반의 패턴인식 하드웨어 설계," 인하대학교 정보통신공학과 석사학위논문, 2008.
  19. J.-K. Kim, B.-T. Zhang, "Evolving hypernetworks for pattern classification," IEEE Congress on Evolutionary Computation (CEC 2007), pp.1856-1862, 2007.
  20. University of California Irvine, Machine Learning repository, http://archive.ics.uci.edu/ml/