Target depth and metal type discrimination with cost based probability updating
Date
2012
Authors
Tran, M.D.J.
Abeynayake, C.
Jain, L.
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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012; 7237:396-405
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Abstract
This paper presents a cost based probability updating technique based on Bayesian decision theory for modifying decision confidences. The technique allows for the incorporation of prior knowledge via the use of cost matrices supplied by another source of intelligence. Data signals acquired by a metal detector array corresponding to UXO based targets were used to evaluate the technique with the assistance of a previously developed automated decision system. The classification classes utilised were based on target depth level and metal type. The results showed that the probability updating technique was able to produce an increase in the classification performance and also reduce the classification errors below approximately 5 to 10%.
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Copyright 2012 Springer