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Proceedings Paper

Buried object detection using handheld WEMI with task-driven extended functions of multiple instances
Author(s): Matthew Cook; Alina Zare; Dominic K. C. Ho
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Paper Abstract

Many effective supervised discriminative dictionary learning methods have been developed in the literature. However, when training these algorithms, precise ground-truth of the training data is required to provide very accurate point-wise labels. Yet, in many applications, accurate labels are not always feasible. This is especially true in the case of buried object detection in which the size of the objects are not consistent. In this paper, a new multiple instance dictionary learning algorithm for detecting buried objects using a handheld WEMI sensor is detailed. The new algorithm, Task Driven Extended Functions of Multiple Instances, can overcome data that does not have very precise point-wise labels and still learn a highly discriminative dictionary. Results are presented and discussed on measured WEMI data.

Paper Details

Date Published: 3 May 2016
PDF: 9 pages
Proc. SPIE 9823, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXI, 98230A (3 May 2016); doi: 10.1117/12.2223349
Show Author Affiliations
Matthew Cook, Univ. of Missouri (United States)
Alina Zare, Univ. of Missouri (United States)
Dominic K. C. Ho, Univ. of Missouri (United States)

Published in SPIE Proceedings Vol. 9823:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXI
Steven S. Bishop; Jason C. Isaacs, Editor(s)

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