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Proceedings Paper

Statistical Approach For Forward Looking Infrared (Flir) Target Classification
Author(s): Yun-Kung J. Lin
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Paper Abstract

A statistical approach for forward looking infrared (FLIR) target classification is presented. The implemented functions include enhancement, segmentation, feature extraction and classification. A 5 x 5 median filter is used for image smoothing. Segmentation involves an adaptive thresholding technique. This technique is capable of automatic selection of local thresholds of individual targets based on the local property in terms of minimal change in target area. Features extracted from segmented target candidates characterize grade shade, texture and geometry properties of these regions. Among several evaluated classifiers the Bayes decision rule is used for its performance, flexibility and future modifications. The presented approach has been applied to 92 FLIR images from three different data sets. Five types of target candidates examined in this study are tanks, APC's, jeeps, burning hulks, and noise regions. Among 281 targets of interest, 260 belong to these five categories. The Bayes classifier has achieved 87.69% detection and 76.92% classification with a FAR of 0.07 per image.

Paper Details

Date Published: 17 March 1983
PDF: 6 pages
Proc. SPIE 0359, Applications of Digital Image Processing IV, (17 March 1983); doi: 10.1117/12.965949
Show Author Affiliations
Yun-Kung J. Lin, Harris Corporation (United States)


Published in SPIE Proceedings Vol. 0359:
Applications of Digital Image Processing IV
Andrew G. Tescher, Editor(s)

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