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Feature extraction using distribution representation for colorimetric sensor arrays used as explosives detectors

TitleFeature extraction using distribution representation for colorimetric sensor arrays used as explosives detectors
Publication TypeConference Paper
Year of Publication2012
AuthorsAlstrom, T. S., R. Raich, N. V. Kostesha, and J. Larsen
Conference NameIEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Pagination2125 - 2128
Date Published03/2012
PublisherIEEE
Conference LocationKyoto, Japan
ISBN Number978-1-4673-0044-5
Keywordschemo-selective compounds, explosives detection, feature extraction, Hellinger distance, k-nearest neighbor classification
Abstract

We 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.

DOI10.1109/ICASSP.2012.6288331