14 May 2015 Multi-scale HOG prescreening algorithm for detection of buried explosive hazards in FL-IR and FL-GPR data
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Abstract
A sliding window based prescreening algorithm, utilizing multi-scale histogram of oriented gradient (MS-HOG) features and a linear support vector machine (SVM) classifier, for detection of buried explosive hazards in forward-looking infrared (FL-IR) and forward-looking ground penetrating radar (FL-GPR) data is presented. This algorithm is compared to previously published FL-IR and FL-GPR prescreening algorithms. The MS-HOG prescreening approach has higher computational complexity, but improves overall detection rates, especially for low-contrast and obscured target signatures. Results are presented on several data sets collected at US Army test sites. These collections span several days, and the FL-IR collections include imagery from both long-wave and mid-wave infrared cameras at multiple standoff distances captured at different hours of the day and different times of the year.
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K. Stone, K. Stone, J. M. Keller, J. M. Keller, D. Shaw, D. Shaw, } "Multi-scale HOG prescreening algorithm for detection of buried explosive hazards in FL-IR and FL-GPR data", Proc. SPIE 9454, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XX, 94540Q (14 May 2015); doi: 10.1117/12.2177663; https://doi.org/10.1117/12.2177663
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