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Infrared-image classification using hidden Markov trees

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2 Author(s)
Bharadwaj, P. ; Acuson, Mountain View, CA, USA ; Carin, L.

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.

Published in:

Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:24 ,  Issue: 10 )

Date of Publication:

Oct 2002

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