Advertisement

Frontiers of Computer Science

, Volume 12, Issue 6, pp 1258–1260 | Cite as

A real-time hand-signs segmentation and classification system using fuzzy rule based RGB model and grid-pattern analysis

  • Muhammad Aminur RahamanEmail author
  • Mahmood Jasim
  • Md. Haider Ali
  • Tao Zhang
  • Md. Hasanuzzaman
Letter
  • 25 Downloads

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgement

This research is partially supported and funded by the Information and Communication Technology (ICT) Division, Ministry of Posts, Telecommunications and IT, Government of the People’s Republic of Bangladesh.

Supplementary material

11704_2018_7082_MOESM1_ESM.ppt (330 kb)
Supplementary material, approximately 330 KB.
11704_2018_7082_MOESM2_ESM.pdf (19.3 mb)
A Real-Time Hand-Signs Segmentation And Classification System Using Fuzzy Rule Based RGB Model And Grid-Pattern Analysis

References

  1. 1.
    Rahaman M A, Jasim M, Zhang T, Ali M H, Hasanuzzaman M. Realtime bengali and chinese numeral signs recognition using contour matching. In: Proceedings of the IEEE International Conference on Robotics and Biomimetics. 2015, 1215–1220Google Scholar
  2. 2.
    Lü W, Huang J. Skin detection method based on cascaded adaboost classifier. Journal of Shanghai Jiaotong University (Science), 2012, 17(2): 197–202CrossRefGoogle Scholar
  3. 3.
    Xu J, Zhang X. A real-time hand detection system during hand over face occlusion. International Journal of Multimedia and Ubiquitous Engineering, 2015, 10(8): 287–302CrossRefGoogle Scholar
  4. 4.
    Qiu-yu Z, Jun-chi L, Mo-yi Z, Hong-xiang D, Lu L. Hand gesture segmentation method based on YCBCR color space and K-means clustering. International Journal of Signal Processing, Image Processing and Pattern Recognition, 2015, 8(5): 105–116CrossRefGoogle Scholar
  5. 5.
    Iraji M S, Tosinia A. Skin color segmentation in YCBCR color space with adaptive fuzzy neural network (anfis). International Journal of Image, Graphics and Signal Processing, 2012, 4(4): 35–41CrossRefGoogle Scholar
  6. 6.
    Rahaman M A, Jasim M, Ali M H, Hasanuzzaman M. Real-time computer vision based bengali sign language recognition. In: Proceedings of the 17th International Conference on Computer and Information Technology. 2014, 192–197Google Scholar
  7. 7.
    Yasir F, Prasad P WC, Alsadoon A, Elchouemi A. Sift based approach on bangla sign language recognition. In: Proceedings of the 8th IEEE International Workshop on Computational Intelligence and Applications. 2015, 35–39Google Scholar
  8. 8.
    Yasir R, Khan R A. Two-handed hand gesture recognition for bangla sign language using LDA and ANN. In: Proceedings of the 8th International Conference on Software, Knowledge, Information Management and Applications. 2014, 1–5Google Scholar
  9. 9.
    Jasim M, Zhang T, Hasanuzzaman M. A real-time computer vision-based static and dynamic hand gesture recognition system. International Journal on Image and Graphics, 2014, 14(01n02): 1–19Google Scholar
  10. 10.
    Ayshee T F, Raka S A, Hasib Q R, Hossain M, Rahman R M. Fuzzy rule-based hand gesture recognition for bengali characters. In: Proceedings of the IEEE International Advance Computing Conference. 2014, 484–489Google Scholar

Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Muhammad Aminur Rahaman
    • 1
    Email author
  • Mahmood Jasim
    • 1
  • Md. Haider Ali
    • 1
  • Tao Zhang
    • 2
  • Md. Hasanuzzaman
    • 1
  1. 1.Department of Computer Science and EngineeringUniversity of DhakaDhakaBangladesh
  2. 2.Department of Automation, School of Information Science & EngineeringTsinghua UniversityBeijingChina

Personalised recommendations