Visual sign language recognition based on HMMs and auto-regressive HMMs

Xiaolin Yang; Feng Jiang; Han Liu; Hongxun Yao; Wen Gao; Chunli Wang

doi:10.1007/11678816_9

Visual sign language recognition based on HMMs and auto-regressive HMMs

Xiaolin Yang^*, Feng Jiang, Han Liu, Hongxun Yao, Wen Gao, Chunli Wang

^*Corresponding author for this work

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

Abstract

A sign language recognition system based on Hidden Markov Models(HMMs) and Auto-regressive Hidden Markov Models(ARHMMs) has been proposed in this paper. ARHMMs fully consider the observation relationship and are helpful to discriminate signs which don't have obvious state transitions while similar in motion trajectory. ARHMM which models the observation by mixture conditional linear Gaussian is proposed for sign language recognition. The corresponding training and recognition algorithms for ARHMM are also developed. A hybrid structure to combine ARHMMs with HMMs based on the trick of using an ambiguous word set is presented and the advantages of both models are revealed in such a frame work.

Original language	English (US)
Title of host publication	Gesture in Human-Computer Interaction and Simulation
Subtitle of host publication	6th International Gesture Workshop, GW 2005, Revised Selected Papers
Publisher	Springer Verlag
Pages	80-83
Number of pages	4
ISBN (Print)	3540326243, 9783540326243
DOIs	https://doi.org/10.1007/11678816_9
State	Published - 2006
Event	6th International Gesture Workshop, GW 2005 - Berder Island, France Duration: May 18 2005 → May 20 2005

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3881 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	6th International Gesture Workshop, GW 2005
Country/Territory	France
City	Berder Island
Period	5/18/05 → 5/20/05

Keywords

Autoregressive HMM
Computer Vision
HMM
Sign Language Recognition

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/11678816_9

Cite this

Yang, X., Jiang, F., Liu, H., Yao, H., Gao, W., & Wang, C. (2006). Visual sign language recognition based on HMMs and auto-regressive HMMs. In Gesture in Human-Computer Interaction and Simulation: 6th International Gesture Workshop, GW 2005, Revised Selected Papers (pp. 80-83). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3881 LNAI). Springer Verlag. https://doi.org/10.1007/11678816_9

Yang, Xiaolin ; Jiang, Feng ; Liu, Han et al. / Visual sign language recognition based on HMMs and auto-regressive HMMs. Gesture in Human-Computer Interaction and Simulation: 6th International Gesture Workshop, GW 2005, Revised Selected Papers. Springer Verlag, 2006. pp. 80-83 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{037f4a53fb9d4df6a43337074353c38c,

title = "Visual sign language recognition based on HMMs and auto-regressive HMMs",

abstract = "A sign language recognition system based on Hidden Markov Models(HMMs) and Auto-regressive Hidden Markov Models(ARHMMs) has been proposed in this paper. ARHMMs fully consider the observation relationship and are helpful to discriminate signs which don't have obvious state transitions while similar in motion trajectory. ARHMM which models the observation by mixture conditional linear Gaussian is proposed for sign language recognition. The corresponding training and recognition algorithms for ARHMM are also developed. A hybrid structure to combine ARHMMs with HMMs based on the trick of using an ambiguous word set is presented and the advantages of both models are revealed in such a frame work.",

keywords = "Autoregressive HMM, Computer Vision, HMM, Sign Language Recognition",

author = "Xiaolin Yang and Feng Jiang and Han Liu and Hongxun Yao and Wen Gao and Chunli Wang",

year = "2006",

doi = "10.1007/11678816_9",

language = "English (US)",

isbn = "3540326243",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "80--83",

booktitle = "Gesture in Human-Computer Interaction and Simulation",

note = "6th International Gesture Workshop, GW 2005 ; Conference date: 18-05-2005 Through 20-05-2005",

}

Yang, X, Jiang, F, Liu, H, Yao, H, Gao, W & Wang, C 2006, Visual sign language recognition based on HMMs and auto-regressive HMMs. in Gesture in Human-Computer Interaction and Simulation: 6th International Gesture Workshop, GW 2005, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3881 LNAI, Springer Verlag, pp. 80-83, 6th International Gesture Workshop, GW 2005, Berder Island, France, 5/18/05. https://doi.org/10.1007/11678816_9

Visual sign language recognition based on HMMs and auto-regressive HMMs. / Yang, Xiaolin; Jiang, Feng; Liu, Han et al.

Gesture in Human-Computer Interaction and Simulation: 6th International Gesture Workshop, GW 2005, Revised Selected Papers. Springer Verlag, 2006. p. 80-83 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3881 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Visual sign language recognition based on HMMs and auto-regressive HMMs

AU - Yang, Xiaolin

AU - Jiang, Feng

AU - Liu, Han

AU - Yao, Hongxun

AU - Gao, Wen

AU - Wang, Chunli

PY - 2006

Y1 - 2006

N2 - A sign language recognition system based on Hidden Markov Models(HMMs) and Auto-regressive Hidden Markov Models(ARHMMs) has been proposed in this paper. ARHMMs fully consider the observation relationship and are helpful to discriminate signs which don't have obvious state transitions while similar in motion trajectory. ARHMM which models the observation by mixture conditional linear Gaussian is proposed for sign language recognition. The corresponding training and recognition algorithms for ARHMM are also developed. A hybrid structure to combine ARHMMs with HMMs based on the trick of using an ambiguous word set is presented and the advantages of both models are revealed in such a frame work.

AB - A sign language recognition system based on Hidden Markov Models(HMMs) and Auto-regressive Hidden Markov Models(ARHMMs) has been proposed in this paper. ARHMMs fully consider the observation relationship and are helpful to discriminate signs which don't have obvious state transitions while similar in motion trajectory. ARHMM which models the observation by mixture conditional linear Gaussian is proposed for sign language recognition. The corresponding training and recognition algorithms for ARHMM are also developed. A hybrid structure to combine ARHMMs with HMMs based on the trick of using an ambiguous word set is presented and the advantages of both models are revealed in such a frame work.

KW - Autoregressive HMM

KW - Computer Vision

KW - HMM

KW - Sign Language Recognition

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DO - 10.1007/11678816_9

M3 - Conference contribution

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SN - 9783540326243

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 80

EP - 83

BT - Gesture in Human-Computer Interaction and Simulation

PB - Springer Verlag

T2 - 6th International Gesture Workshop, GW 2005

Y2 - 18 May 2005 through 20 May 2005

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Yang X, Jiang F, Liu H, Yao H, Gao W, Wang C. Visual sign language recognition based on HMMs and auto-regressive HMMs. In Gesture in Human-Computer Interaction and Simulation: 6th International Gesture Workshop, GW 2005, Revised Selected Papers. Springer Verlag. 2006. p. 80-83. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11678816_9

Visual sign language recognition based on HMMs and auto-regressive HMMs

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