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Proceedings Paper

Multiple instance dictionary learning for subsurface object detection using handheld EMI
Author(s): Alina Zare; Matthew Cook; Brendan Alvey; Dominic K. Ho
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Paper Abstract

A dictionary learning approach for subsurface object detection using handheld electromagnetic induction (EMI) data is presented. A large number of unsupervised and supervised dictionary learning methods have been developed in the literature. However, the majority of these methods require data point-specific labels during training. In the application to subsurface object detection, often the specific training data samples that correspond to target and non-target are not known and difficult to determine manually. In this paper, a dictionary learning method that addresses this issue using the multiple instance learning techniques is presented. Results are shown on real EMI data sets.

Paper Details

Date Published: 14 May 2015
PDF: 8 pages
Proc. SPIE 9454, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XX, 94540G (14 May 2015); doi: 10.1117/12.2179177
Show Author Affiliations
Alina Zare, Univ. of Missouri-Columbia (United States)
Matthew Cook, Univ. of Missouri-Columbia (United States)
Brendan Alvey, Univ. of Missouri-Columbia (United States)
Dominic K. Ho, Univ. of Missouri-Columbia (United States)


Published in SPIE Proceedings Vol. 9454:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XX
Steven S. Bishop; Jason C. Isaacs, Editor(s)

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