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Research Publication and Presentation |
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Evolution Map: Modeling State Transition of Typhoon Image Sequences by Spatio-temporal Clustering
The purpose of this paper is to analyze the evolution of typhoon cloud
patterns in the spatio-temporal domain using statistical learning
models. The basic approach is clustering procedures for extracting
hidden states of the typhoon, and we also analyze the temporal
dynamics of the typhoon in terms of transitions between hidden
states. The clustering procedures include both spatial and
spatio-temporal clustering procedures, including K-means clustering,
Self-Organizing Maps (SOM), Mixture of Gaussians (MoG) and Generative
Topographic Mapping (GTM) combined with Hidden Markov Model (HMM).
The result of clustering is visualized on the "Evolution Map" on which
we analyze and visualize the temporal structure of the typhoon cloud
patterns. The results show that spatio-temporal clustering procedures
outperform spatial clustering procedures in capturing the temporal
structures of the evolution of the typhoon.
Citation
Asanobu KITAMOTO,
"Evolution Map: Modeling State Transition of Typhoon Image Sequences by Spatio-temporal Clustering",
Discovery Science (DS2002), Lecture Notes in Computer Science (LNCS) 2534,
Lange, S., Satoh, K., and Smith, C.H. (Eds.),
pp. 283-290,
Springer,
doi:10.1007/3-540-36182-0_26,
2002-11
BibTeX Format
@InProceedings{ k:ds02,
author = {Asanobu KITAMOTO},
title = {Evolution Map: Modeling State Transition of Typhoon Image Sequences by Spatio-temporal Clustering},
booktitle = {Discovery Science (DS2002), Lecture Notes in Computer Science (LNCS) 2534},
pages = {283-290},
publisher = {Springer},
editor = {Lange, S., Satoh, K., and Smith, C.H.},
year = 2002,
month = 11,
doi = {10.1007/3-540-36182-0_26},
note = {},
}
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