Buried object detection using handheld WEMI with task-driven extended functions of multiple instances

Matthew Cook; Alina Zare; Dominic K. C. Ho

doi:10.1117/12.2223349

3 May 2016 Buried object detection using handheld WEMI with task-driven extended functions of multiple instances

Matthew Cook, Alina Zare, Dominic K. C. Ho

Proceedings Volume 9823, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXI; 98230A (2016) https://doi.org/10.1117/12.2223349
Event: SPIE Defense + Security, 2016, Baltimore, MD, United States

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.

Citation Download Citation

Matthew Cook, Alina Zare, and Dominic K. C. Ho "Buried object detection using handheld WEMI with task-driven extended functions of multiple instances", Proc. SPIE 9823, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXI, 98230A (3 May 2016); https://doi.org/10.1117/12.2223349

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