Infrared-image classification using hidden Markov trees

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Bharadwaj, P; Carin, L

Published Date

  • October 1, 2002

Published In

Volume / Issue

  • 24 / 10

Start / End Page

  • 1394 - 1398

International Standard Serial Number (ISSN)

  • 0162-8828

Digital Object Identifier (DOI)

  • 10.1109/TPAMI.2002.1039210

Citation Source

  • Scopus