Improving hand gesture recognition using 3D combined features

2009 2nd International Conference on Machine Vision, ICMV 2009

Volumn , Issue , 2009, Pages 128-132

  Elmezain, Mahmoud ^a   Al Hamadi, Ayoub ^a   Michaelis, Bernd ^a  

^a OTTO VON GUERICKE UNIVERSITY   (Germany)

Author keywords

Computer vision and image analysis; Gesture recognition; Statistical pattern recognition

Indexed keywords

CALIBRATION DATA; CARTESIAN SYSTEMS; CODEWORD; COLOR IMAGE SEQUENCES; COMBINED FEATURES; COMPLEX ENVIRONMENTS; CROSS CORRELATIONS; DEPTH INFORMATION; DEPTH MAP; HAND GESTURE; HAND TRACKING; HAND-GESTURE RECOGNITION; K-MEANS CLUSTERING; MEAN SHIFT; PASSIVE STEREO; RECOGNITION RATES; ROBUST METHODS; STATISTICAL PATTERN RECOGNITION; VITERBI PATH;

COMPUTER VISION; HIDDEN MARKOV MODELS; IMAGE ANALYSIS; PARTIAL DISCHARGES; THREE DIMENSIONAL;

GESTURE RECOGNITION;

EID: 77949829990     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICMV.2009.28     Document Type: Conference Paper

References (17)

1. X. Deyou, A Network Approach for Hand Gesture Recognition in Virtual Reality Driving Training System of SPG, International Conference ICPR, pp. 519-522, 2006.
2. E. Holden, R. Owens and G. Roy, Hand Movement Classification Using Adaptive Fuzzy Expert System, Journal of Expert Systems, Vol. 9(4), pp. 465-480, 1996.
3. M. Elmezain, A. Al-Hamadi and B. Michaelis, Real-Time Capable System for Hand Gesture Recognition Using Hidden Markov Models in Stereo Color Image Sequences, Journal of WSCG, Vol. 16, pp. 65-72, 2008.
4. M. Elmezain, A. Al-Hamadi and B. Michaelis, A Novel System for Automatic Hand Gesture Spotting and Recognition in Stereo Color Image Sequences. Journal of WSCG, Vol.17, No. 1, pp. 89-96, 2009.
5. M. Elmezain, A. Al-Hamadi, J. Appenrodt and B. Michaelis, A Hidden Markov Model-Based Continuous Gesture Recognition System for Hand Motion Trajectory. International Conference on Pattern Recognition (ICPR), pp. 519-522, 2008.
6. M. Elmezain, A. Al-Hamadi and B. Michaelis, Spatio-Temporal Feature Extraction-Based Hand Gesture Recognition for Isolated American Sign Language and Arabic Numbers. IEEE Symposium on ISPA, pp. 254-259, 2009.
7. N. Liu and B. C. Lovell, MMX-accelerated Real-Time Hand Tracking System, In IVCNZ, pp. 381-385, 2001.
8. T. Nobuhiko, S. Nobutaka, and S. Yoshiaki, Extraction of Hand Features for Recognition of Sign Language Words, In International Conf. VI, pp. 391-398, 2002.
9. N. P. Vassilia and G. M. Konstantinos, On Feature Extraction and Sign Recognition for Greek Sign Language, International Conference on Artificial Intelligence and Soft Computer, pp. 93-98, 2003.
10. Y. Ho-Sub, S. Jung, J. B. Young and S. Y. Hyun, Hand Gesture Recognition using Combined Features of Location, Angle and Velocity, Journal of Pattern Recognition, Vol. 34(7), pp. 1491-1501, 2001.
11. D. Comaniciu, V. Ramesh and P. Meer, Real-Time Tracking of Non-Rigid Objects Using Mean Shift, In Conference CVPR, Vol. 2, pp. 1-8, 2000.
12. R. Klette, K. Schlüns and A. Koschan, Computer Vision: Three-Dimensional Data from Images, Springer, Singapore, ISBN 981-3083-71-9, 1998.
13. R. Niese, A. Al-Hamadi and B. Michaelis, A Novel Method for 3D Face Detection and Normalization, Journal of Multimedia, Vol. 2, pp. 1-12, 2007.
14. S. Khalid, U. Ilyas, S. Sarfaraz and A. Ajaz, ABhattacharyya Coefficient in Correlation of Gary-Scale Objects, Journal of Multimedia, Vol. 1, pp. 56-61, 2006.
15. D. Comaniciu and P. Meer, Mean Shift: A Robust Approach Toward Feature Space Analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, pp. 603-619, 2002.
16. C. Ding and X. He, K-means Clustering via Principal Component Analysis, International Conference on Machine Learning, pp. 225-232, 2004.
17. T. Kanungo, D. M. Mount, N. Netanyahu, C. Piatko, R. Silverman and A. Y. Wu, An Efficient k-means Clustering Algorithm: Analysis and Implementation, IEEE Transaction on PAMI, Vol. 24, pp. 881-892, 2002.