TY - JOUR
T1 - Wavelet-based feature extraction using probabilistic finite state automata for pattern classification
AU - Jin, Xin
AU - Gupta, Shalabh
AU - Mukherjee, Kushal
AU - Ray, Asok
N1 - Funding Information:
This work has been supported by the U.S. Office of Naval Research under Grant No. N00014-09-1-0688, and the U.S. Army Research Laboratory and the U.S. Army Research Office under Grant No. W911NF-07-1-0376. Any opinions, findings and conclusions expressed in this publication are those of the authors and do not necessarily reflect the views of the sponsoring agencies.
Copyright:
Copyright 2011 Elsevier B.V., All rights reserved.
PY - 2011/7
Y1 - 2011/7
N2 - Real-time data-driven pattern classification requires extraction of relevant features from the observed time series as low-dimensional and yet information-rich representations of the underlying dynamics. These low-dimensional features facilitate in situ decision-making in diverse applications, such as computer vision, structural health monitoring, and robotics. Wavelet transforms of time series have been widely used for feature extraction owing to their timefrequency localization properties. In this regard, this paper presents a symbolic dynamics-based method to model surface images, generated by wavelet coefficients in the scale-shift space. These symbolic dynamics-based models (e.g., probabilistic finite state automata (PFSA)) capture the relevant information, embedded in the sensor data, from the associated Perron-Frobenius operators (i.e., the state-transition probability matrices). The proposed method of pattern classification has been experimentally validated on laboratory apparatuses for two different applications: (i) early detection of evolving damage in polycrystalline alloy structures, and (ii) classification of mobile robots and their motion profiles.
AB - Real-time data-driven pattern classification requires extraction of relevant features from the observed time series as low-dimensional and yet information-rich representations of the underlying dynamics. These low-dimensional features facilitate in situ decision-making in diverse applications, such as computer vision, structural health monitoring, and robotics. Wavelet transforms of time series have been widely used for feature extraction owing to their timefrequency localization properties. In this regard, this paper presents a symbolic dynamics-based method to model surface images, generated by wavelet coefficients in the scale-shift space. These symbolic dynamics-based models (e.g., probabilistic finite state automata (PFSA)) capture the relevant information, embedded in the sensor data, from the associated Perron-Frobenius operators (i.e., the state-transition probability matrices). The proposed method of pattern classification has been experimentally validated on laboratory apparatuses for two different applications: (i) early detection of evolving damage in polycrystalline alloy structures, and (ii) classification of mobile robots and their motion profiles.
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U2 - 10.1016/j.patcog.2010.12.003
DO - 10.1016/j.patcog.2010.12.003
M3 - Article
AN - SCOPUS:79952184225
SN - 0031-3203
VL - 44
SP - 1343
EP - 1356
JO - Pattern Recognition
JF - Pattern Recognition
IS - 7
ER -