Landmine detection using an ensemble of continuous HMMs with multiple features

Hamdi, Anis; Frigui, Hichem

doi:10.1109/igarss.2011.6048898

Cited by 1 publication

(1 citation statement)

References 14 publications

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“…The research problem for sensor data analysis is to determine how well signatures of landmines can be characterized and distinguished from other objects under the ground using returns from one or more sensors. Recently, various discrimination algorithms [110][111][112][113][114] have been proposed for detecting buried objects using ground-penetrating radar (GPR) or (cross-track,depth). The performance of the down-track and cross-track discrimination algorithms can vary significantly depending on the target shape, burial orientation, and other environmental conditions.…”

Section: Landmine Detectionmentioning

confidence: 99%

Multiple instance fuzzy inference.

Khalifa¹

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Fuzzy logic is a powerful tool to model knowledge uncertainty, measurements imprecision, and vagueness. However, there is another type of vagueness that arises when data have multiple forms of expression that fuzzy logic does not address quite well. This is the case for multiple instance learning problems (MIL). In MIL, an object is represented by a collection of instances, called a bag. A bag is labeled negative if all of its instances are negative, and positive if at least one of its instances is positive. Positive bags encode ambiguity since the instances themselves are not labeled. In this paper, we introduce fuzzy inference systems and neural networks designed to handle bags of instances as input and capable of learning from ambiguously labeled data. First, we introduce the Multiple Instance Sugeno style fuzzy inference (MI-Sugeno) that extends the standard Sugeno style inference to handle reasoning with multiple instances. Second, we use MI-Sugeno to define and develop Multiple Instance Adaptive Neuro Fuzzy Inference System (MI-ANFIS). We expand the architecture of the standard ANFIS to allow reasoning with bags and derive a learning algorithm using backpropagation to identify the premise and consequent parameters of the network. The proposed inference system is tested and validated using synthetic and benchmark datasets suitable for MIL problems. We also apply the proposed MI-ANFIS to fuse the output of multiple discrimination algorithms for the purpose of landmine detection using Ground Penetrating Radar.

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Section: Landmine Detectionmentioning

confidence: 99%

Multiple instance fuzzy inference.

Khalifa¹

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show abstract

Landmine detection using an ensemble of continuous HMMs with multiple features

Cited by 1 publication

References 14 publications

Multiple instance fuzzy inference.

Multiple instance fuzzy inference.

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