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17 May 2016 Improved landmine detection through context-dependent score calibration
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Algorithms developed for the detection of landmines are tasked with discriminating a wide variety of targets in a diverse array of environmental conditions. However, the potential performance of a detection algorithm may be underestimated by evaluating it in batch on a large, diverse dataset. This is because environmental, or in general, contextual, factors may contribute significant variance to the output of a detection algorithm across different contexts. One way to view this is as a problem of miscalibration: within each context, the output scores of a detection algorithm can be seen as miscalibrated relative to the scores produced in the other contexts. As a result of this miscalibration, the observed receiver operating characteristic (ROC) curve for a detector can have a sub-optimal area-under-the-curve (AUC). One solution, then, is to re-calibrate the detector within each context. In this work, we identify multiple sets of contexts in which different landmine detection algorithms exhibit significant output variance and, consequently, miscalibration. We then apply a monotonic calibration strategy that maximizes AUC and demonstrate the gain in observed performance that results when a landmine detection algorithm is properly calibrated within each context.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Brandon Smock, Joseph Wilson, and Martin Milner "Improved landmine detection through context-dependent score calibration", Proc. SPIE 9842, Signal Processing, Sensor/Information Fusion, and Target Recognition XXV, 98420K (17 May 2016);

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