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Analyzing movement trajectories using a Markov bi-clustering method
Year:
2009
Authors :
Shemesh, Moshe
;
.
Volume :
27
Co-Authors:
Erez, K., The Gonda Brain Research Center, Bar-Ilan University, Ramat-Gan, Israel
Goldberger, J., School of Engineering, Bar-Ilan University, Ramat-Gan 52900, Israel
Sosnik, R., The Gonda Brain Research Center, Bar-Ilan University, Ramat-Gan, Israel
Shemesh, M., The Gonda Brain Research Center, Bar-Ilan University, Ramat-Gan, Israel
Rothstein, S., The Gonda Brain Research Center, Bar-Ilan University, Ramat-Gan, Israel
Abeles, M., The Gonda Brain Research Center, Bar-Ilan University, Ramat-Gan, Israel
Facilitators :
From page:
543
To page:
552
(
Total pages:
10
)
Abstract:
In this study we treat scribbling motion as a compositional system in which a limited set of elementary strokes are capable of concatenating amongst themselves in an endless number of combinations, thus producing an unlimited repertoire of complex constructs. We broke the continuous scribblings into small units and then calculated the Markovian transition matrix between the trajectory clusters. The Markov states are grouped in a way that minimizes the loss of mutual information between adjacent strokes. The grouping algorithm is based on a novel markov-state bi-clustering algorithm derived from the Information-Bottleneck principle. This approach hierarchically decomposes scribblings into increasingly finer elements. We illustrate the usefulness of this approach by applying it to human scribbling. © Springer Science+Business Media, LLC 2009.
Note:
Related Files :
Cluster analysis
hand movement
handwriting
hidden Markov model
Models, Biological
motion analysis system
Motor control
Stroke
Show More
Related Content
More details
DOI :
10.1007/s10827-009-0168-0
Article number:
Affiliations:
Database:
Scopus
Publication Type:
article
;
.
Language:
English
Editors' remarks:
ID:
27368
Last updated date:
02/03/2022 17:27
Creation date:
17/04/2018 00:30
You may also be interested in
Scientific Publication
Analyzing movement trajectories using a Markov bi-clustering method
27
Erez, K., The Gonda Brain Research Center, Bar-Ilan University, Ramat-Gan, Israel
Goldberger, J., School of Engineering, Bar-Ilan University, Ramat-Gan 52900, Israel
Sosnik, R., The Gonda Brain Research Center, Bar-Ilan University, Ramat-Gan, Israel
Shemesh, M., The Gonda Brain Research Center, Bar-Ilan University, Ramat-Gan, Israel
Rothstein, S., The Gonda Brain Research Center, Bar-Ilan University, Ramat-Gan, Israel
Abeles, M., The Gonda Brain Research Center, Bar-Ilan University, Ramat-Gan, Israel
Analyzing movement trajectories using a Markov bi-clustering method
In this study we treat scribbling motion as a compositional system in which a limited set of elementary strokes are capable of concatenating amongst themselves in an endless number of combinations, thus producing an unlimited repertoire of complex constructs. We broke the continuous scribblings into small units and then calculated the Markovian transition matrix between the trajectory clusters. The Markov states are grouped in a way that minimizes the loss of mutual information between adjacent strokes. The grouping algorithm is based on a novel markov-state bi-clustering algorithm derived from the Information-Bottleneck principle. This approach hierarchically decomposes scribblings into increasingly finer elements. We illustrate the usefulness of this approach by applying it to human scribbling. © Springer Science+Business Media, LLC 2009.
Scientific Publication
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