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Second IEEE International Conference on Automatic Face and Gesture Recognition (FG '96)
Invariant features for 3-D gesture recognition
Killington, Vermont
October 14-October 16
ISBN: 0-8186-7713-9
ASCII Text
x 
 
L.W. Campbell, D.A. Becker, A. Azarbayejani, A.F. Bobick, A. Pentland, "Invariant features for 3-D gesture recognition," Automatic Face and Gesture Recognition, IEEE International Conference on, pp. 157, Second IEEE International Conference on Automatic Face and Gesture Recognition (FG '96), 1996.
 
 
BibTex
x
 
@article{ 10.1109/AFGR.1996.557258,
author = {L.W. Campbell and D.A. Becker and A. Azarbayejani and A.F. Bobick and A. Pentland},
title = {Invariant features for 3-D gesture recognition},
journal ={Automatic Face and Gesture Recognition, IEEE International Conference on},
volume = {0},
year = {1996},
isbn = {0-8186-7713-9},
pages = {157},
doi = {http://doi.ieeecomputersociety.org/10.1109/AFGR.1996.557258},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Automatic Face and Gesture Recognition, IEEE International Conference on
TI - Invariant features for 3-D gesture recognition
SN - 0-8186-7713-9
SP
EP
A1 - L.W. Campbell,
A1 - D.A. Becker,
A1 - A. Azarbayejani,
A1 - A.F. Bobick,
A1 - A. Pentland,
PY - 1996
KW - feature extraction; gesture recognition; feature vectors; 3D data; stereo video cameras; HMMs; learning; recognition; partial rotational invariance; velocity features
VL - 0
JA - Automatic Face and Gesture Recognition, IEEE International Conference on
ER -
 
L.W. Campbell, Media Lab., MIT, Cambridge, MA, USA
D.A. Becker, Media Lab., MIT, Cambridge, MA, USA
A. Azarbayejani, Media Lab., MIT, Cambridge, MA, USA
A.F. Bobick, Media Lab., MIT, Cambridge, MA, USA
A. Pentland, Media Lab., MIT, Cambridge, MA, USA
Ten different feature vectors are tested in a gesture recognition task which utilizes 3D data gathered in real-time from stereo video cameras, and HMMs for learning and recognition of gestures. Results indicate velocity features are superior to positional features, and partial rotational invariance is sufficient for good performance.
Index Terms:
feature extraction; gesture recognition; feature vectors; 3D data; stereo video cameras; HMMs; learning; recognition; partial rotational invariance; velocity features
Citation:
L.W. Campbell, D.A. Becker, A. Azarbayejani, A.F. Bobick, A. Pentland, "Invariant features for 3-D gesture recognition," fg, pp.157, Second IEEE International Conference on Automatic Face and Gesture Recognition (FG '96), 1996
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