document: Infrared-image classification using hidden Markov trees An image of a three-dimensional target is generally characterized by the visible target subcomponents , with these dictated by the target-sensor orientation -LRB- target pose -RRB- . 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 -LRB- templates -RRB- associated with a given subcomponent . The statistical relationships between the different target subcomponents are modeled via a hidden Markov tree -LRB- HMT -RRB- . The HMT classifier is discussed and example results are presented for forward-looking-infrared -LRB- FLIR -RRB- imagery of several vehicles
doc_bio_tags: O O O B I I O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B O O O O O O O O O O O O O O O O B
extractive_keyphrases: hidden markov trees target pose wiener filters hmt vehicles
abstractive_keyphrases: ir image classification infrared-image classification 3d target image target-sensor orientation contiguous target-sensor orientations karhunen-loeve representation minimization forward-looking-infrared imagery flir imagery