We describe a nonparametric approach of dynamic thermography to the detection of buried antipersonnel (AP) mines. Dynamic thermography consists of processing temporal sequences of IR images taken from the same scene, submitted to either artificial or natural temperature variations. The aim is to obtain an image segmentation where mine and soil can be discriminated due to the different time evolution of their thermal properties. The proposed approach is rooted in a clustering stage performed by a chaotic neural network and provides the correct classification by analyzing very short image sequences, thus enabling a fast acquisition time. The effectiveness of the method is demonstrated on image sequences of plastic AP mines taken from realistic mine fields.
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