Infrared-image classification using expansion matching filters and hidden Markov trees

P. Bharadwaj, P. Runkle, L. Carin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Forward-looking infrared (FLIR) images of targets are characterized by the different target components visible in the image, with such dependent on the target-sensor orientation and target history (i.e., which target components are hot). We define a target class as a set of contiguous target-sensor orientations over which the associated image is relatively invariant, or statistically stationary. Given an image from an unknown target, the objective is proper target-class association (target identify and pose). Our principal contribution is an image classifier in which a distinct set of templates is designed for each image class, with templates linked to the object sub-components, and the associated statistics are characterized via a hidden Markov model. In particular, we employ expansion matching (EXM) filters to identify the presence of the target components in the image, and use a hidden Markov tree (HMT) to characterize the statistics of the correlation of the image with the various templates. We achieve a successful classification rate of 92% on a data set of FLIR vehicle images, compared with 72% for a previously developed wavelet-feature-based HMT technique.
Original languageEnglish (US)
Title of host publicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Pages1553-1556
Number of pages4
StatePublished - Sep 26 2001
Externally publishedYes

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