A hybrid HMM/DPA adaptive gesture recognition method

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3804 LNCS, Issue , 2005, Pages 227-234

  Rajko, Stjepan ^a,b   Qian, Gang ^a,b  

^a Arizona State University   (United States)

^b ARIZONA STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE SYSTEMS; DATA PROCESSING; DYNAMIC PROGRAMMING; MARKOV PROCESSES; ROBUSTNESS (CONTROL SYSTEMS);

DYNAMIC PROGRAMMING ALIGNMENT (DPA); GESTURE DATA; HIDDEN MARKOV MODELS (HMM);

GESTURE RECOGNITION;

EID: 33744826826     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11595755_28     Document Type: Conference Paper

References (12)

1. Lee, C., Xu, Y.: Online, interactive learning of gestures for human/robot interfaces. In: 1996 IEEE International Conference on Robotics and Automation. Volume 4. (1996) 2982-2987
2. Vogler, C., Metaxas, D.: Handshapes and movements: Multiple-channel american sign language recognition. In: Lecture Notes in Computer Science. Volume 2915. (2004) 247-258
3. Hienz, H., Bauer, B., Kraiss, K.F.: Hmm-based continuous sign language recognition using stochastic grammars. In: GW '99: Proceedings of the International Gesture Workshop on Gesture-Based Communication in Human-Computer Interaction, London, UK, Springer-Verlag (1999) 185-196
4. Corradini, A.: Dynamic time warping for off-line recognition of a small gesture vocabulary. In: Proceedings of the IEEE ICCV Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems (RATFG-RTS'01), Washington, DC, USA, IEEE Computer Society (2001) 82
5. Keogh, E., Pazzani, M.: Derivative dynamic time warping. In: First SIAM International Conference on Data Mining, Chicago, IL, USA (2001)
6. Wu, H., Kido, R., Shioyama, T.: Improvement of continuous dynamic programming for human gesture recognition. In: International Conference on Pattern Recognition. Volume 02. (2000) 945-948
7. Masek, W.J., Paterson, M.S.: A faster algorithm for computing string edit distances. Journal of Computer and System Sciences 20 (1980) 18-31
8. Setubal, J., Meidanis, J.: Introduction to computational molecular biology. PWS Publishing Company, Boston, MA (1997)
9. Smith, T.F., Waterman, M.S.: Identification of common molecular subsequences. Journal of Molecular Biology 147 (1981) 195-197
10. Kruskall, J.B., Liberman, M.: The symmetric time warping algorithm: From continuous to discrete. In: Time Warps, String Edits and Macromolecules: The Theory and Practice of String Comparison, Addison-Wesley (1983)
11. Hirschberg, D.S.: A linear space algorithm for computing maximal common subsequences. In: Communications of the ACM. Volume 18(6). (1975) 341-343
12. Rajko, S., Aluru, S.: Space and time optimal parallel sequence alignments. IEEE Transactions on Parallel and Distributed Systems 15 (2004) 1070-1081