Feature Extraction Using Distribution Representation For Colorimetric Sensor Arrays Used As Explosives Detectors

Tommy Sonne Alstrøm, Raviv Raich, Natalie V. Kostesha, Jan Larsen

AbstractWe present a colorimetric sensor array which is able to detect
explosives such as DNT, TNT, HMX, RDX and TATP
and identifying volatile organic compounds in the presence of
water vapor in air. To analyze colorimetric sensors with statistical
methods, a suitable representation of sensory readings
is required. We present a new approach of extracting features
from a colorimetric sensor array based on a color distribution
representation. For each sensor in the array, we construct
a K–nearest neighbor classifier based on the Hellinger distances
between color distribution of a test compound and the
color distribution of all the training compounds. The performance
of this set of classifiers are benchmarked against a set
of K–nearest neighbor classifiers that is based on traditional
feature representation (e.g., mean or global mode). The suggested
approach of using the entire distribution outperforms
the traditional approaches which use a single feature.
Keywordscolorinmetric, sensor array, KNN, Hellinger distance
TypeConference paper [With referee]
ConferenceICASSP 2012
Year2012    Month March
PublisherIEEE Press
Notewww.icassp2012.com
Electronic version(s)[pdf]
BibTeX data [bibtex]
IMM Group(s)Intelligent Signal Processing


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