JSTS:Journal of Semiconductor Technology and Science

  • 제16권5호
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  • Pages.557-563
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  • 2016
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  • 1598-1657(pISSN)
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  • 2233-4866(eISSN)
대한전자공학회 (The Institute of Electronics and Information Engineers)
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Accelerating Distance Transform Image based Hand Detection using CPU-GPU Heterogeneous Computing

  • Yi, Zhaohua (Department of Computer and Information Science, The University of Mississippi) ;
  • Hu, Xiaoqi (Department of Computer and Information Science, The University of Mississippi) ;
  • Kim, Eung Kyeu (Department of Information and Communication Engineering, Hanbat National University) ;
  • Kim, Kyung Ki (School of Electronic and Electrical Engineering, Daegu University) ;
  • Jang, Byunghyun (Department of Computer and Information Science, The University of Mississippi)
  • 투고 : 2016.09.22
  • 심사 : 2016.01.28
  • 발행 : 2016.10.30
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초록

Most of the existing hand detection methods rely on the contour shape of hand after skin color segmentation. Such contour shape based computations, however, are not only susceptible to noise and other skin color segments but also inherently sequential and difficult to efficiently parallelize. In this paper, we implement and accelerate our in-house distance image based approach using CPU-GPU heterogeneous computing. Using emerging CPU-GPU heterogeneous computing technology, we achieved 5.0 times speed-up for $320{\times}240$ images, and 17.5 times for $640{\times}480$ images and our experiment demonstrates that our proposed distance image based hand detection is robust and fast, reaching up to 97.32% palm detection rate, 80.4% of which have more than 3 fingers detected on commodity processors.

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참고문헌

  1. R. Rajesh, Nagarjunan, Arunachalam, Aarthi, "Distance Transform Based Hand Gesture Recognition for PowerPoint Presentation Navigation," Advanced Computing: An International Journal, vol. 3, no. 3, pp.41-48, 2013. https://doi.org/10.5121/acij.2012.3304
  2. D. R. Jadhav and L. Lobo, "Navigation of PowerPoint Using Hand Gestures," International Journal of Science and Research(IJSR), vol. 4, no. 1, pp. 833-837, 2015.
  3. ByungGook Lee, HuiShyong Yeo and Hyotaek Lim, "Hand tracking and gesture recognition system for human-computer interaction using lowcost hardware," Multimedia Tools and Applications, 2013.
  4. Ankit Chaudhary, J. L. Raheja, Karen Das, and Sonia Raheja, "Intelligent approaches to interact with machines using hand gesture recognition in natural way:a survey," International Journal of Computer Science and Engineering Survey (IJCSES), vol. 2, no. 1, 2011.
  5. Paul Viola and Michael Jones, "Rapid object detectionusing a boosted cascade of simple features," Conferene of Computer Vision and Pattern Recognition, 2001.
  6. A.R.Patil and S.S.Subbaraman, "A review on vision based hand gesture recognition approach using support vector machines," Journal of Electronicsl and Communication Engineering, 2012.
  7. Qi Wang Hanjie Wang and Xilin Chen, "Hand posture recognition from disparity cost map," Asian Conference on Computer Vision, 2012.
  8. Nicolas D. Georganas Qing Chen and Emil M. Petriu, "Real-time vision-based hand gesture recognition using haar-like features," Instrumentation and Measurement Technology Conference, 2007.
  9. Artur Wilkowski and Wodzimierz Kasprzak, "Hand gesture modeling using dynamic bayesian networks and deformable templates," Seventh International Conference on Signal Image Technology and Internet-Based Systems, 2011.
  10. Douglas Chai and King N. Ngan, "Face segmentation using skin-color map in videophone applications," IEEE Transactions on Circuits and Systems for Video Technology, vol. 9, no. 4, 1999.
  11. Gunilla Borgefors, "Distance transformations in digital images," Computer Vision, Graphics and Image Processing, no. 34, pp. 344-371, 1986. https://doi.org/10.1016/S0734-189X(86)80047-0