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Investigation of initialization strategies for the Multiple Instance Adaptive Cosine Estimator
Author(s): James Bocinsky; Connor McCurley; Daniel Shats; Alina Zare
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Paper 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.

Paper Details

Date Published: 10 May 2019
PDF: 13 pages
Proc. SPIE 11012, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV, 110120N (10 May 2019); doi: 10.1117/12.2519463
Show Author Affiliations
James Bocinsky, Univ. of Florida (United States)
Connor McCurley, Univ. of Florida (United States)
Daniel Shats, Univ. of Florida (United States)
Alina Zare, Univ. of Florida (United States)


Published in SPIE Proceedings Vol. 11012:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV
Steven S. Bishop; Jason C. Isaacs, Editor(s)

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