Buried object detection using handheld WEMI with task-driven extended functions of multiple instances
This addresses the challenge of accurate labeling in buried object detection for applications like landmine clearance, but it is incremental as it builds on existing supervised dictionary learning methods.
The paper tackles the problem of buried object detection with handheld WEMI sensors by developing a new multiple instance dictionary learning algorithm that handles imprecise point-wise labels, achieving highly discriminative results on measured data.
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.