Multiple instance dictionary learning for subsurface object detection using handheld EMI

Alina Zare; Matthew Cook; Brendan Alvey; Dominic K. Ho

doi:10.1117/12.2179177

14 May 2015 Multiple instance dictionary learning for subsurface object detection using handheld EMI

Alina Zare, Matthew Cook, Brendan Alvey, Dominic K. Ho

Proceedings Volume 9454, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XX; 94540G (2015) https://doi.org/10.1117/12.2179177
Event: SPIE Defense + Security, 2015, Baltimore, MD, United States

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.

Citation Download Citation

Alina Zare, Matthew Cook, Brendan Alvey, and Dominic K. Ho "Multiple instance dictionary learning for subsurface object detection using handheld EMI", Proc. SPIE 9454, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XX, 94540G (14 May 2015); https://doi.org/10.1117/12.2179177

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