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.

Links:

Citation:

A. Zare, M. Cook, B. Alvey, and D. Ho, “Multiple instance dictionary learning for subsurface object detection using handheld EMI,” in Proc. SPIE 9454, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XX, 2015.

@InProceedings{zare2015multiple,
Title = {Multiple instance dictionary learning for subsurface object detection using handheld EMI},
Author = {Alina Zare and Matthew Cook and Brendan Alvey and Dominic Ho},
Booktitle = {Proc. SPIE 9454, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XX},
Year = {2015},
Month = {May},
Number = {94540G},
Volume = {9454},
Doi = {10.1117/12.2179177},
}