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10 May 2012 Anomaly detection ensemble fusion for buried explosive material detection in forward looking infrared imaging for addressing diurnal temperature variation
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Abstract
In prior work, we describe multiple image space anomaly detection algorithms for the identification of buried explosive materials in forward looking long wave infrared imagery. That work is extended here and focus is placed on improved detection with respect to diurnal temperature variation. An ensemble of shape and size independent image space anomaly detection algorithms are investigated. Specifically, anomalies are identified according to change and blob detection. This anomaly evidence is aggregated and targets are found using an ensemble of trainable size-contrast filters and weighted mean shift clustering. In addition, the blob detector makes use of contrast-limited adaptive histogram equalization for image enhancement. Experimental results are shown based on field data measurements from a U.S. Army test site.
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Derek T. Anderson, Kevin Stone, James M. Keller, and John Rose "Anomaly detection ensemble fusion for buried explosive material detection in forward looking infrared imaging for addressing diurnal temperature variation", Proc. SPIE 8357, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVII, 83570T (10 May 2012); https://doi.org/10.1117/12.920346
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