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Cited by 671

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

An HMM-based threshold model approach for gesture recognition

Hyeon-Kyu Lee, Kim, J.H.·1999·IEEE Transactions on Pattern Analysis and Machine Intelligence
DOI 10.1109/34.799904
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Abstract

A new method is developed using the hidden Markov model (HMM) based technique. To handle nongesture patterns, we introduce the concept of a threshold model that calculates the likelihood threshold of an input pattern and provides a confirmation mechanism for the provisionally matched gesture patterns. The threshold model is a weak model for all trained gestures in the sense that its likelihood is smaller than that of the dedicated gesture model for a given gesture. Consequently, the likelihood can be used as an adaptive threshold for selecting proper gesture model. It has, however, a large number of states and needs to be reduced because the threshold model is constructed by collecting the states of all gesture models in the system. To overcome this problem, the states with similar probability distributions are merged, utilizing the relative entropy measure. Experimental results show that the proposed method can successfully extract trained gestures from continuous hand motion with 93.14% reliability.

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Research works printed but not linked: 19

  1. 10.1142/9789814350938_0003 · DOI
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Cited by

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Metadata

Authors
Lee, H.-K., & Kim, J. H.
Venue
IEEE Transactions on Pattern Analysis and Machine Intelligence
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
1999
Volume
21
Issue
10
Pages
961-973
ISSN
0162-8828 (ISSN-L)
DOI
10.1109/34.799904
OpenAlex
W2120701504
Cited by
671
References
2
FWCI
9.88
Type
journal article
Language
en
Open access
closed
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)

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Lee, H.-K., & Kim, J. H. (1999). An HMM-based threshold model approach for gesture recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/34.799904

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