Abstract:

Sensors which use electromagnetic induction (EMI) to excite a response in conducting bodies have long been investigated for subsurface explosive hazard detection. In particular, EMI sensors have been used to discriminate between different types of objects, and to detect objects with low metal content. One successful, previously investigated approach is the Multiple Instance Adaptive Cosine Estimator (MI-ACE). In this paper, a number of new initialization techniques for MI-ACE are proposed and evaluated using their respective performance and speed. The cross validated learned signatures, as well as learned background statistics, are used with Adaptive Cosine Estimator (ACE) to generate confidence maps, which are clustered into alarms. Alarms are scored against a ground truth and the initialization approaches are compared.

Links:

Citation:

J. Bocinsky, C. McCurley, D. Shats and A. Zare, “Investigation of Initialization Strategies for the Multiple Instance Adaptive Cosine Estimator,” in Proc. SPIE 11012, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV, 2019.

@Article{James2019Investigation
Title = {Investigation of Initialization Strategies for the Multiple Instance Adaptive Cosine Estimator},
Author = {Bocinsky, James and McCurley, Connor and Shats, Daniel and Zare, Alina},
Booktitle = {Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV},
Series = {Proc. SPIE},
Volume = {11012},
Year = {2019},
Month = {May},
}