TY - GEN
T1 - Transition movement models for large vocabulary continuous sign language recognition
AU - Gao, Wen
AU - Fang, Gaolin
AU - Zhao, Debin
AU - Chen, Yiqiang
PY - 2004
Y1 - 2004
N2 - The major challenges that sign language recognition (SLR) now faces are developing methods that solve large vocabulary continuous sign problems. In this paper, large vocabulary continuous SLR based on transition movement models is proposed. The proposed method employs the temporal clustering algorithm to cluster a large amount of transition movements, and then the corresponding training algorithm is also presented for automatically segmenting and training these transition movement models. The clustered models can improve the generalization of transition movement models, and are very suitable for large vocabulary continuous SLR. At last, the estimated transition movement models, together with sign models, are viewed as candidate models of the Viterbi search algorithm for recognizing continuous sign language. Experiments show that continuous SLR based on transition movement models has good performance over a large vocabulary of 5113 signs.
AB - The major challenges that sign language recognition (SLR) now faces are developing methods that solve large vocabulary continuous sign problems. In this paper, large vocabulary continuous SLR based on transition movement models is proposed. The proposed method employs the temporal clustering algorithm to cluster a large amount of transition movements, and then the corresponding training algorithm is also presented for automatically segmenting and training these transition movement models. The clustered models can improve the generalization of transition movement models, and are very suitable for large vocabulary continuous SLR. At last, the estimated transition movement models, together with sign models, are viewed as candidate models of the Viterbi search algorithm for recognizing continuous sign language. Experiments show that continuous SLR based on transition movement models has good performance over a large vocabulary of 5113 signs.
UR - https://www.scopus.com/pages/publications/4544293052
U2 - 10.1109/AFGR.2004.1301591
DO - 10.1109/AFGR.2004.1301591
M3 - 会议稿件
AN - SCOPUS:4544293052
SN - 0769521223
SN - 9780769521220
T3 - Proceedings - Sixth IEEE International Conference on Automatic Face and Gesture Recognition
SP - 553
EP - 558
BT - Proceedings - Sixth IEEE International Conference on Automatic Face and Gesture Recognition FGR 2004
PB - IEEE Computer Society
T2 - 6th IEEE International Conference on Automatic Face and Gesture Recognition, FGR 2004
Y2 - 17 May 2004 through 19 May 2004
ER -