DocumentCode
2085342
Title
Invariant features for 3-D gesture recognition
Author
Campbell, Lee W. ; Becker, David A. ; Azarbayejani, Ali ; Bobick, Aaron E. ; Pentland, Alex
Author_Institution
Media Lab., MIT, Cambridge, MA, USA
fYear
1996
fDate
14-16 Oct 1996
Firstpage
157
Lastpage
162
Abstract
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
Keywords
computer vision; feature extraction; image recognition; 3D data; HMMs; feature vectors; gesture recognition; learning; partial rotational invariance; recognition; stereo video cameras; velocity features; Arm; Birds; Brushes; Cameras; Clouds; Hidden Markov models; Horses; Knee; Tail; Testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Automatic Face and Gesture Recognition, 1996., Proceedings of the Second International Conference on
Conference_Location
Killington, VT
Print_ISBN
0-8186-7713-9
Type
conf
DOI
10.1109/AFGR.1996.557258
Filename
557258
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