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access icon openaccess Assistive technology for relieving communication lumber between hearing/speech impaired and hearing people

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References

    1. 1)
      • 1. MFD, Malaysian Sign Language. 2012; Available at http://www.mfd.org.my/public/edu_eSign.asp.
    2. 2)
      • 2. MacKenzie, I.S.: ‘Input devices and interaction techniques for advanced computing, in virtual environments and advanced interface design’ (Oxford University Press, Oxford, UK, 1995), pp. 437470.
    3. 3)
    4. 4)
      • 4. Yang, M.H., Ahuja, N.: ‘Recognizing hand gesture using motion trajectories’. IEEE Conf. Computer Vision and Pattern Recognition, IEEE Computer Society, Fort Collins, CO, USA, 1999, vol. 1, pp. 466483.
    5. 5)
      • 5. Grzeszczuk, R., Bradski, G., Chu, M.H., Bouguet, J.: ‘Stereo based gesture recognition invariant to 3D pose and lighting’. IEEE Conf. Computer Vision and Pattern Recognition, IEEE Computer Society, Head Island, SC, USA, 2000, vol. 1, pp. 826833.
    6. 6)
    7. 7)
      • 7. Bilal, S., Akmeliawati, R., Momoh, J., Shafie, A.A.: ‘Dynamic approach for real-time skin detection’, J. Real-Time Image Process., 2012, p. 6.
    8. 8)
    9. 9)
      • 9. Imagawa, K., Lu, S., Igi, S.: ‘Color-based hands tracking system for sign language recognition’. Third IEEE Int. Conf. Automatic Face and Gesture Recognition, Nara, Japan, 1998, pp. 462467.
    10. 10)
      • 10. Bilal, S., Akmeliawati, R., Salami, M.J.E., Shafie, A.A., Bouhabba, E.M., et al: ‘A hybrid method using Haar-like and skin-color algorithm for hand posture detection, recognition and tracking’. Int. Conf. Mechatronics and Automation (ICMA), Xi'an, China, 2010, pp. 934939.
    11. 11)
      • 11. Kilian, J.: Simple Image Analysis by Moments, 2001, 8 pp. Available at http://www.scribd.com/doc/39759766/Simple-Image-Analysis-by-Moments.
    12. 12)
      • 12. Jusko, D.: Full Real Color Wheel Course, 2011. Available at http://www.realcolorwheel.com/human.htm.
    13. 13)
      • 13. Bradski, G.R.: ‘Computer vision face tracking for use in a perceptual user interface’, Intel Technol. J., 1998, 2, (3), pp. 115.
    14. 14)
      • 14. Bilal, S., Akmeliawati, R., Shafie, A.A., Salami, M.J.E.: ‘Modelling of human upper body for sign language recognition’. Fifth Int. Conf. Automation, Robotics and Applications (ICARA), Wellington, New Zealand, 2011, pp. 104108.
    15. 15)
      • 15. Starner, T.E., Pentland, A.: ‘Real-time American sign language recognition from video using hidden Markov models’. IEEE Int. Symp. Computer Vision, Coral Gables, FL, USA, 1995, pp. 265270.
    16. 16)
      • 16. Segouat, J., Braffort, A.: ‘Toward modeling sign language coarticulation’, in Kopp, S., Wachsmuth, I. (Eds.): ‘Gesture in embodied communication and human–computer interaction’ (Springer Berlin Heidelberg, 2010), pp. 325336.
    17. 17)
    18. 18)
    19. 19)
    20. 20)
    21. 21)
    22. 22)
    23. 23)
      • 23. Kahol, K., Tripathi, P., Panchanathan, S., Rikakis, T.: ‘Gesture segmentation in complex motion sequences’. IEEE Int. Conf. Automatic Face and Gesture Recognition, Seoul, Korea, 2004, vol. 3, pp. II-1058.
    24. 24)
      • 24. Ong, S.C.W., Ranganath, S.: ‘A new probabilistic model for recognizing signs with systematic modulations’, Third International Workshop on Analysis and Modelling of Faces and Gestures, Rio de Janeiro, Brazil, 2007 (LNCS, 4778/2007), pp. 1630.
    25. 25)
      • 25. Ruiduo, Y., Sarkar, S.: ‘Detecting coarticulation in sign language using conditional random fields’. 18th Int. Conf. Pattern Recognition, 2006, ICPR 2006, 2006, vol. 2, pp. 108112.
    26. 26)
    27. 27)
      • 27. Li, H., Greenspan, M.: ‘Segmentation and recognition of continuous gestures’. IEEE Int. Conf. Image Processing, 2007, ICIP 2007, 2007, vol. 1, pp. 365368.
    28. 28)
      • 28. Li, H., Greenspan, M.: ‘Continuous time-varying gesture segmentation by dynamic time warping of compound gesture models’. Int. Workshop on Human Activity Recognition and Modelling (HARAM2005), 2005, p. 8.
    29. 29)
      • 29. Starner, T., Pentland, A.: ‘Real time American sign language recognition from video using hidden Markov model’. Int. Symp. Computer Vision, Florida, USA, 1995, pp. 265270.
    30. 30)
      • 30. Vogler, C.P.: ‘American sign language recognition: reducing the complexity of the task with phoneme-based modeling and parallel hidden Markov models’. PhD dissertation, University of Pennsylvania, USA, p. 172.
    31. 31)
    32. 32)
    33. 33)
      • 33. Liang, R.-H., Ming, O.: ‘A real-time continuous gesture recognition system for sign language’. IEEE Int. Conf. Automatic Face and Gesture Recognition, Japan, 1998, pp. 558567.
    34. 34)
      • 34. Li, H., Greenspan, M.: ‘Multi-scale gesture recognition from time-varying contours’. 10th IEEE Int. Conf. Computer Vision, ICCV 2005, 2005, vol. 1, pp. 236243.
    35. 35)
    36. 36)
      • 36. Khan, S., Bailey, D.G., Sen Gupta, G.: ‘Delayed absolute difference (DAD) signatures of dynamic features for sign language segmentation’. Fifth Int. Conf. Automation, Robotics and Applications (ICARA2011), Wellington, New Zealand, 2011, pp. 109114.
    37. 37)
      • 37. Qt Project. Available at http://www.qt-project.org/.
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