Multiresolution hidden Markov trees for analysis of automatic target recognition algorithms

Author

Stanford, Derek C. ; Pitton, James

Volume

2

fYear

2000

fDate

10-13 Sept. 2000

Firstpage

942

Abstract

We present a method for characterizing clutter in images by using multiscale hidden Markov models in the wavelet domain, and we discuss the application of this in predicting the performance of automatic target recognition (ATR) systems. Wavelet Markov models explicitly include dependence across scale, which allows us to characterize highly structured clutter. Since structured clutter is a common cause of false alarms in modern ATR systems, these models give us the ability to analyze and predict false alarm rates and other ATR performance characteristics. These predictions can lead to adaptive ATR systems which automatically adjust to local clutter conditions.

Keywords

hidden Markov models; image classification; image recognition; image resolution; infrared imaging; radar clutter; radar resolution; radar target recognition; wavelet transforms; HMM; IR images; MSE classifiers; SAR images; adaptive ATR systems; automatic target recognition algorithms; false alarm rates; local clutter conditions; multiresolution hidden Markov trees; multiscale hidden Markov models; structured clutter; synthetic aperture radar; system performance; wavelet Markov models; wavelet domain; Algorithm design and analysis; Clutter; Discrete wavelet transforms; Hidden Markov models; Modems; Power system modeling; Predictive models; Target recognition; Wavelet coefficients; Wavelet domain;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing, 2000. Proceedings. 2000 International Conference on

Conference_Location

Vancouver, BC, Canada

ISSN

1522-4880

Print_ISBN

0-7803-6297-7

Type

conf

DOI

10.1109/ICIP.2000.899872

Filename

899872