Feature extraction using distribution representation for colorimetric sensor arrays used as explosives detectors
Publication: Research - peer-review › Article in proceedings – Annual report year: 2012
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.
|Title of host publication||2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)|
|Conference||IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012)|
|Period||25/03/12 → 30/03/12|
|Name||I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings|
|Citations||Web of Science® Times Cited: No match on DOI|
- Hellinger distance, Chemo–selective compounds, Explosives detection, Feature extraction, K– nearest neighbor classification
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