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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/14605

Title: Feature-based detection using Bayesian data fusion
Authors: Akiwowo, Ayodeji
Eftekhari, Mahroo
Keywords: Bayesian data fusion
Feature selection
Fibre\-optic sensor
Classification
Issue Date: 2013
Publisher: © Taylor and Francis
Citation: AKIWOWO, A. and EFTEKHARI, M., 2013. Feature-based detection using Bayesian data fusion. International Journal of Image and Data Fusion, 4 (4), pp. 308-323
Abstract: Current cocaine detection techniques used at borders have their challenges, which include cost of training specialised operators, the high chance of operator error and the dangers involved in exposure of both operators and container contents to radioactive material. This paper describes a technique which utilises the benefits of data fusion to develop a non-invasive system which relies less on the expertise of the operator, whilst improving false positive rates. To improve the capabilities of the cocaine-detecting fibre-optic sensor, the raw data was pre-processed and features were identified and extracted. The output of each feature is a decision on the classification and the conditional probability that it belongs to the chosen class based on the observed data, which serve as input into a Bayesian data fusion module and outputs the probability that a sample belongs to a class based on the observed features and makes a decision based on the class with the higher probability. The results show that the Bayesian fusion module greatly improves the detection rates of individual feature.
Description: This article was published in the journal, International Journal of Image and Data Fusion [© Taylor and Francis] and the definitive version is available at: http://dx.doi.org/10.1080/19479832.2013.824029
Version: Accepted for publication
DOI: 10.1080/19479832.2013.824029
URI: https://dspace.lboro.ac.uk/2134/14605
Publisher Link: http://dx.doi.org/10.1080/19479832.2013.824029
ISSN: 1947-9832
Appears in Collections:Published Articles (Civil and Building Engineering)

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