Clustering by inhomogeneous chaotic maps in landmine detection

A method has been recently proposed that provides a hierarchical solution to the clustering problem under very general assumptions, relying on the cooperative behavior of an inhomogeneous lattice of chaotic coupled maps. The physical system can be seen as a chaotic neural network where neurons update is performed by logistic maps. The mutual information between couples of map acts as a similarity index to get partitions of a data set, corresponding to different resolution levels. As a result a full hierarchy of clusters is generated. Esperiments on artificial and real-life problems show the effectiveness of the proposed algorithm. Here we report the results of an application to landmine detection by dynamic thermography. Dynamic thermography allows to discriminate among objects with different thermal properties by sequential IR imaging. Detection is then obtained through segmentation of temporal sequences of infrared images. An approach is proposed that gives the correct classification by analysing very short image sequence, thus allowing a fast acquisition time. The algorithm has been successfully tested on image sequences of plastic anti-personnel mines taken from realistic minefields.