Adaptive motion-based gesture recognition interface for mobile phones

Adaptive motion-based gesture recognition interface for mobile phones. Hannuksela, J., Barnard, M., Sangi, P., & Heikkilä, J. In Computer Vision Systems. ICVS 2008. Lecture Notes in Computer Science, volume 5008 LNCS, pages 271-280, 2008. Springer, Berlin, Heidelberg.
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In this paper, we introduce a new vision based interaction technique for mobile phones. The user operates the interface by simply moving a finger in front of a camera. During these movements the finger is tracked using a method that embeds the Kalman filter and Expectation Maximization (EM) algorithms. Finger movements are interpreted as gestures using Hidden Markov Models (HMMs). This involves first creating a generic model of the gesture and then utilizing unsupervised Maximum a Posteriori (MAP) adaptation to improve the recognition rate for a specific user. Experiments conducted on a recognition task involving simple control commands clearly demonstrate the performance of our approach. © 2008 Springer-Verlag Berlin Heidelberg.

@inproceedings{
 title = {Adaptive motion-based gesture recognition interface for mobile phones},
 type = {inproceedings},
 year = {2008},
 keywords = {Finger tracking,Handheld devices,Human-computer interaction,MAP adaptation,Motion estimation},
 pages = {271-280},
 volume = {5008 LNCS},
 publisher = {Springer, Berlin, Heidelberg},
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 created = {2019-09-15T16:34:27.252Z},
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 profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},
 last_modified = {2019-09-15T18:07:37.962Z},
 read = {false},
 starred = {false},
 authored = {true},
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 abstract = {In this paper, we introduce a new vision based interaction technique for mobile phones. The user operates the interface by simply moving a finger in front of a camera. During these movements the finger is tracked using a method that embeds the Kalman filter and Expectation Maximization (EM) algorithms. Finger movements are interpreted as gestures using Hidden Markov Models (HMMs). This involves first creating a generic model of the gesture and then utilizing unsupervised Maximum a Posteriori (MAP) adaptation to improve the recognition rate for a specific user. Experiments conducted on a recognition task involving simple control commands clearly demonstrate the performance of our approach. © 2008 Springer-Verlag Berlin Heidelberg.},
 bibtype = {inproceedings},
 author = {Hannuksela, Jari and Barnard, Mark and Sangi, Pekka and Heikkilä, Janne},
 doi = {10.1007/978-3-540-79547-6_26},
 booktitle = {Computer Vision Systems. ICVS 2008. Lecture Notes in Computer Science}
}

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