Tag: target detection

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

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… Read More

Functions of Multiple Instances for Learning Target Signatures

Abstract: The functions of multiple instances (FUMI) approach for learning target and nontarget signatures is introduced. FUMI is a generalization of the multiple-instance learning (MIL) approach for supervised learning. FUMI differs significantly from standard MIL and supervised learning approaches because… Read More

Functions of multiple instances for sub-pixel target characterization in hyperspectral imagery

Abstract: In this paper, the Multi-target Extended Function of Multiple Instances (Multi-target eFUMI) method is developed and described. The method is capable of learning multiple target spectral signatures from weakly- and inaccurately-labeled hyperspectral imagery. Multi-target eFUMI is a generalization of… Read More

Multiple instance dictionary learning for subsurface object detection using handheld EMI

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… Read More

Extended functions of multiple instances for target characterization

Abstract: An extension of the Function of Multiple Instances (FUMI) algorithm for target characterization is presented. FUMI is a generalization of Multiple Instance Learning (MIL). However, FUMI differs significantly from standard MIL and supervised learning approaches because only data points… Read More

Landmine classification using possibilistic K-nearest neighbors with wideband electromagnetic induction data

Abstract: A possibilistic K-Nearest Neighbors classifier is presented to classify mine and non-mine objects using data collected from a wideband electromagnetic induction (WEMI) sensor. The proposed classifier is motivated by the observation that buried objects often have consistent signatures depending… Read More