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<p><b>Abstract</b>—An image of a three-dimensional target is generally characterized by the visible target subcomponents, with these dictated by the target-sensor orientation (target pose). An image often changes quickly with variable pose. We define a class as a set of contiguous target-sensor orientations over which the associated target image is relatively stationary with aspect. Each target is in general characterized by multiple classes. A distinct set of Wiener filters are employed for each class of images, to identify the presence of target subcomponents. A Karhunen-Loeve representation is used to minimize the number of filters (templates) associated with a given subcomponent. The statistical relationships between the different target subcomponents are modeled via a hidden Markov tree (HMT). The HMT classifier is discussed and example results are presented for forward-looking-infrared (FLIR) imagery of several vehicles.</p>
Hidden Markov model, infrared imagery, classification.

P. Bharadwaj and L. Carin, "Infrared-Image Classification Using Hidden Markov Trees," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 24, no. , pp. 1394-1398, 2002.
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