Name
Abstract | ||
|---|---|---|
We present a hybrid classification method applicable to gesture recognition. The method combines elements of Hidden Markov Models (HMM) and various Dynamic Programming Alignment (DPA) methods, such as edit distance, sequence alignment, and dynamic time warping. As opposed to existing approaches which treat HMM and DPA as either competing or complementing methods, we provide a common framework which allows us to combine ideas from both HMM and DPA research. The combined approach takes on the robustness and effectiveness of HMMs and the simplicity of DPA approaches. We have implemented and successfully tested the proposed algorithm on various gesture data. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11595755_28 | ISVC |
Keywords | Field | DocType |
hybrid hmm,hidden markov models,dpa adaptive gesture recognition,dpa research,common framework,various dynamic programming,combined approach,complementing method,various gesture data,gesture recognition,dpa approach,hybrid classification method,edit distance,dynamic time warping,hidden markov model,sequence alignment | Edit distance,Dynamic programming,Dynamic time warping,Pattern recognition,Markov model,Computer science,Gesture recognition,Speech recognition,Robustness (computer science),Artificial intelligence,Hidden Markov model,Viterbi algorithm | Conference |
Volume | ISSN | ISBN |
3804 | 0302-9743 | 3-540-30750-8 |
Citations | PageRank | References |
4 | 0.92 | 7 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stjepan Rajko | 1 | 154 | 14.22 |
| Gang Qian | 2 | 784 | 63.77 |