Modelling and segmenting subunits for sign language recognition based on hand motion analysis

Pattern Recognition Letters

Volumn 30, Issue 6, 2009, Pages 623-633

  Han, Junwei ^a   Awad, George ^a   Sutherland, Alistair ^a  

^a DUBLIN CITY UNIVERSITY   (Ireland)

Author keywords

Dynamic time warping; Hand motion; Phoneme; Sign language recognition; Subunit

Indexed keywords

COMPUTER VISION; QUERY LANGUAGES; TIME SERIES; WEAVING;

DYNAMIC TIME WARPING; HAND MOTION; PHONEME; SIGN LANGUAGE RECOGNITION; SUBUNIT;

LINGUISTICS;

EID: 61849122379     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2008.12.010     Document Type: Article

References (20)

1. Awad, G., Han, J., Sutherland, A., 2006. A unified system for segmentation and tracking of face and hands in sign language recognition. In: Proc. Internat. Conf. on Pattern Recognition, pp. 239-242.
2. Bauer, B., Kraiss, K., 2001. Towards an automatic sign language recognition system using subunits. In: Proc. Internat. Gesture Workshop, pp. 64-75.
3. Brentari D. A Prosodic Model of Sign Language Phonology (1998), A Bradford Book, Cambridge, MA & London
4. Fang, G., Gao, X., Gao, W., 2004. A novel approach to automatically extracting basic units from Chinese Sign Language. In: Proc. Internat. Conf. on Pattern Recognition, pp. 454-457.
5. Han, J., Awad, G., Sutherland, A., Wu, H., 2006. Automatic skin segmentation for gesture recognition combining region and support vector machine active learning. In: Proc. Internat. Conf. on Automatic Face and Gesture Recognition, pp. 237-242.
6. Jain A.K., Murty M.N., and Flynn P.J. Data clustering: A review. ACM Comput. Surveys (1999) 264-323
7. Klima E., and Bellugi U. The Signs of Language (1979), Harvard University Press, Cambridge, MA
8. Lee, C., Xu, Y., 2000. Trajectory fitting with smoothing splines using velocity information. In: Proc. IEEE Conf. on Robotics and Automation, pp. 2796-2801.
9. Liang, R.-H, Ouhyoung, M., 1998. A real-time continuous gesture recognition system for Sign Language. In: Proc. Third Internat. Conf. on Automatic Face and Gesture Recognition, pp. 558-565.
10. Liddell S., and Johnson R. American Sign Language: The phonological base. Sign Lang. Stud. 64 (1989) 195-277
11. Otsu N. A thresholding selection method from gray-level histogram. IEEE Trans. Systems Man Cybernet. 9 (1979) 62-66
12. Poizner H., Klima E.S., and Bellugi U. What The Hands Reveal About The Brain (1987), MIT Press, Cambridge, MA
13. Sandler, W., 2008. The syllable in sign language: Considering the other natural modality. In: Ontogeny and Phylogeny of Syllable Organization.
14. Starner T., Weaver J., and Pentland A. Real-time American Sign Language recognition using desk and wearable computer based video. IEEE Trans. Pattern Anal. Machine Intell. 20 (1998) 1371-1375
15. Stokoe, W., 1978. Sign language structure: An outline of the visual communication system of the American deaf. Studies in Linguistics: Occasional papers 8, Linstok Press, MD.
16. Viola, P., Jones, M., 2001. Rapid object detection using a boosted cascade of simple features. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pp. 511-518.
17. Vogler, C., Metaxas, D., 1999. Toward scalability in ASL recognition: Breaking down signs into phonemes. In: Proc. Internat. Gesture Workshop, pp. 211-224.
18. Wilbur R.B. Why syllables? What the notion means for ASL research?. In: Fischer S., and Patricia S. (Eds). Theoretical Issues in Sign Language Research. Vol I: Linguistics (1990), University of Chicago Press, Chicago
19. Yang M., Ahuja N., and Tabb M. Extracting of 2D trajectories and its application to hand gesture recognition. IEEE Trans. Pattern Anal. Machine Intell. 24 (2002) 1061-1074
20. Yeasin M., and Chaudhuri S. Visual understanding of dynamic hand gestures. Pattern Recognit. 33 (2000) 1805-1817