Infrared-image classification using hidden Markov trees

Priya Bharadwaj; Lawrence Carin

doi:10.1109/TPAMI.2002.1039210

Infrared-image classification using hidden Markov trees

Priya Bharadwaj, Lawrence Carin

Research output: Contribution to journal › Article › peer-review

35 Scopus citations

Abstract

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.

Original language	English (US)
Pages (from-to)	1394-1398
Number of pages	5
Journal	IEEE Transactions on Pattern Analysis and Machine Intelligence
Volume	24
Issue number	10
DOIs	https://doi.org/10.1109/TPAMI.2002.1039210
State	Published - Oct 1 2002
Externally published	Yes

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Generated from Scopus record by KAUST IRTS on 2021-02-09

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10.1109/TPAMI.2002.1039210

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Infrared-image classification using hidden Markov trees

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